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CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230315T191617Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 am. \n\nABSTRACT: \nDat a Provenance\, also referred to as Data Lineage\, is metadata that descri bes from where a digital artifact came. People have argued that such meta data is useful for myriad applications such as reproducibility\, forensic analysis\, intrusion detection\, data retention\, regulatory compliance\ , and more. Unfortunately\, the vast majority of work in the area focuses on standardization and collection\, not applications. As a result\, adop tion of provenance in industry has been practically non existent. \n\nI'l l present a short background and history of research on data provenance f ollowed by a discussion of some real applications that we've developed (a re developing)\, some challenges in building powerful provenance-based ap plications\, and speculation about avenues of further research. \n\nSPEAK ER BIO SUMMARY: \nMargo Seltzer https://www.seltzer.com/margo/ is Canada 150 Research Chair in Computer Systems and the Cheriton Family chair in C omputer Science at the University of British Columbia. Her research inter ests are in systems\, construed quite broadly: systems for capturing and accessing data provenance\, file systems\, databases\, transaction proces sing systems\, storage and analysis of graph structured data\, and system s for constructing optimal and interpretable machine learning models. Rea d more: https://bit.ly/dukecs-3apr2023 DURATION:PT1H DTSTAMP:20230315T193437Z DTSTART;TZID=America/New_York:20230403T120000 LAST-MODIFIED:20230315T193437Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Data Provenance and its Applications UID:CAL-8a0290b4-860465b2-0186-e6b3a4e2-00001604demobedework@mysite.edu URL:https://www.cs.duke.edu/events/node/4209 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tatiana.margitic@duke.ed u:Tatiana Margitic X-BEDEWORK-SPEAKER:Margo Seltzer X-BEDEWORK-DUKE-SERIES:Duke Computer Science Distinguished Lecture Series X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Data Provenance and its Applications - Duke CS D istinguished Lecture Apr 3 with Margo Seltzer - Univ of British Columbia Computer Systems Canada 150 Research Chair and CS Cheriton Family Chair X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/seltzerdukecalendar2_20230315073437PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/seltzerdukecalendar2_20230315073437P M-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230314T185205Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM.\n\nABSTRACT: \nWith rapidly growing amounts of experimental data\, machine learning is incre asingly crucial for automating scientific data analysis. However\, many r eal-world workflows demand expert-in-the-loop attention and require model s that not only interface with data\, but also with experts and domain kn owledge. My research develops full stack solutions that enable scientists to scalably extract insights from diverse and messy experimental data wi th minimal supervision. My approaches learn from both data and expert kno wledge\, while exploiting the right level of domain knowledge for general ization. In this talk\, I will present progress towards developing automa ted scientist-in-the-loop solutions\, including methods that automaticall y discover meaningful structure from data such as self-supervised keypoin ts from videos of diverse behaving organisms. I will also present methods that use these interpretable structures to inject domain knowledge into the learning process\, such as guiding representation learning using symb olic programs of behavioral features computed from keypoints. I work clos ely with domain experts\, such as behavioral neuroscientists\, to integra te these methods in real-world workflows. My aim is to enable AI that col laborates with scientists to accelerate the scientific process.\n\nSPEAKE R BIO: \nJennifer is a PhD candidate in Computing and Mathematical Scienc es at Caltech\, advised by Professors Pietro Perona and Yisong Yue. Her r esearch focuses on developing scientist-in-the-loop computational systems that automatically convert experimental data into insight with minimal e xpert effort. She aims to accelerate scientific discovery and optimize ex pert attention in real-world workflows\, tackling challenges including an notation efficiency\, model interpretability and generalization\, and sem antic structure discovery. Beyond her research work\, she has organized m ultiple workshops to facilitate connections across fields at top AI confe rences\, such as CVPR\, and she has received multiple awards\, such as be st student paper at CVPR 2021. DURATION:PT1H DTSTAMP:20230314T185205Z DTSTART;TZID=America/New_York:20230406T120000 LAST-MODIFIED:20230314T185205Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:AI for Scientists: Accelerating Discovery through Knowledge\, Data & Learning UID:CAL-8a0290b4-860465b2-0186-e1771ed0-00005fc2demobedework@mysite.edu URL:https://www.cs.duke.edu/events/node/4244 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_PrattSc hool_ECE,/principals/users/agrp_PrattSchool,":Electrical and Computer Eng ineering (ECE)\,Pratt School of Engineering X-BEDEWORK-SPEAKER:Jennifer Sun X-BEDEWORK-DUKE-SERIES:Duke Computer Science and Duke Electrical and Compu ter Engineering Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tatiana.margitic@duke.ed u:Tatiana Margitic X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:AI for Scientists - Accelerating Discovery throu gh Knowledge\, Data & Learning Duke CS-ECE Colloquium Apr 6 with Jennifer Sun X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/jennifersundukecalendar_20230314065205PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/jennifersundukecalendar_202303140652 05PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230324T135454Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 am. \n\nABSTRACT: \nIn the talk I will share insights and experiences gained during my work on t hree industrial research projects in distributed systems. The three resea rch projects reached production and had profound\, sometimes surprising\, impact on foundations of distributed computing. The first system is base d on the HotStuff algorithm which became the core engine of the Diem bloc kchain infrastructure. In the second system\, Flexible Paxos\, I describe the notion of unlearning unnecessary constraints behind Log Device. The third system\, CorfuDB\, achieves scale-out without partitioning of VMwar e's NSX distributed control plane. \n\nNaturally\, the focus of my talk i s in the field of distributed systems. Nonetheless\, I believe that some take-aways from the experience of building distributed systems are of int erest and value to other disciplines in computer science and software eng ineering. \n\nSPEAKER BIO: \nDr. Malkhi's research over two decades spans broad aspects of reliability and security of distributed systems\, with recent focus on blockchains and advances in financial technology. Her wor k resulted in over 150 publications as well as strong impact on computing technology\, notably as co-inventor of HotStuff\, the core engine of Die m\, Aptos and other blockchains\; co-founder and technical lead of VMware blockchain\; co-inventor of Flexible Paxos\, a method underlying Log Dev ice\; creator of CorfuDB\, VMware NSX distributed control plane\; and co- inventor of the FairPlay project. Read more: https://malkhi.com/about/ DURATION:PT1H DTSTAMP:20230324T135635Z DTSTART;TZID=America/New_York:20230410T120000 LAST-MODIFIED:20230324T135635Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Research Flywheel: from basic research to production of three dist ributed systems UID:CAL-8a0182b3-870a191e-0187-13e6a4ae-00006ae5demobedework@mysite.edu URL:https://www.cs.duke.edu/events/node/4222 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tatiana.margitic@duke.ed u:Tatiana Margitic X-BEDEWORK-SPEAKER:Dahlia Malkhi X-BEDEWORK-DUKE-SERIES:Duke Computer Science Distinguished Lecture Series X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Distinguished Lecture Apr 10 on Research Flywheel - from Basic Research to Production of 3 Distributed Systems wi th Distinguished Scientist at Chainlink Labs Dahlia Malkhi X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/malkhidukecalendar_20230324015454PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/malkhidukecalendar_20230324015454PM- thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230403T190718Z DESCRIPTION:EVENT: \nJoin us for a Triangle CS Distinguished Lecturer Seri es (TCSDLS) live broadcast from NC State in LSRC D106! Snacks will be pro vided. Or attend via Zoom. TCSDLS is cosponsored by Duke CS\, NC State CS \, and UNC CS. \n\nABSTRACT: \nAs Program Lead for Innovate Beyond 5G for the DoD OUSD Office of Research & Engineering\, the speaker managed an a nnual research and prototyping portfolio in support of adopting 5G for th e Enterprise and Innovating Beyond. Accordingly\, this talk will be divid ed into 2 components: \n1.) An Initial Overview of DoD 5G-to-xG R&D strat egy\, followed by a deeper dive into \n2. Cellular V2X\, notably performa nce aspects of Sidelink Mode-2 enabled ad-hoc networking\, current status of Integrated Access & Backhaul (IAB) highlighting feature gaps and R&D opportunities. \nThe talk will conclude with distilling the impacts of th e above as potential vectors for 5G+/6G and implications for 3GPP standar ds evolution beyond Rel-18. \n\nSPEAKER BIO: \nSumit Roy (Fellow\, IEEE 2 007) received MS and PhD degrees from the University of California (Santa Barbara) in ECE\, as well as an MA in Statistics and Applied Probability . His previous academic appointments were at the Moore School of Electric al Engineering\, University of Pennsylvania\, and at the University of Te xas\, San Antonio. His research interests and technology expertise spans analysis/design and prototyping of wireless communication systems/network s\, with an emphasis on various 5G technologies. He is currently and Inte grated Systems Professor and Director of the Fundamentals of Networking l ab at the University of Washington. Read more: https://people.ece.uw.edu/ roy/. DURATION:PT1H DTSTAMP:20230403T190929Z DTSTART;TZID=America/New_York:20230417T160000 LAST-MODIFIED:20230403T190929Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Beyond 5G Networks: Strategic Roadmap and R&D Prospects UID:CAL-8a0182b3-870a191e-0187-48843fb6-000011dedemobedework@mysite.edu URL:https://www.cs.duke.edu/events/node/4251 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tatiana.margitic@duke.ed u:Tatiana Margitic X-BEDEWORK-SPEAKER:Sumit Roy X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:529.5 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:529.5 X-BEDEWORK-IMAGE-CROP-HEIGHT:353 X-BEDEWORK-IMAGE-ALT-TEXT:Beyond 5G Networks - Strategic Roadmap and R& \;amp\;D Prospects - Triangle CS Distinguished Lecturer Series Apr 17 wit h Univ of Washington Prof Sumit Roy X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/dukecalendar17aprTCSDLS_20230403070718PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/dukecalendar17aprTCSDLS_202304030707 18PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230417T192824Z DESCRIPTION:YOU'RE INVITED! \nJoin us for a Triangle CS Distinguished Lect urer Series (TCSDLS) live broadcast from UNC! Snacks will be provided. Or attend via Zoom. TCSDLS is cosponsored by Duke CS\, NC State CS\, and UN C CS.\n\nABSTRACT\nLarge language models (LLMs) such as ChatGPT have take n the world by storm\, but are incredibly expensive to train\, requiring significant amount of data and computational resources. They also halluci nate\, e.g. by regularly introducing made up facts\, and are difficult to keep up to date over time\, as the world around them changes. In this ta lk\, I will survey some our recent work on non-parametric and retrieval-b ased language models\, which are instead designed to be easily extensible and provide much more careful provenience for their predictions. The key idea is to trade parameters for data\; rather than attempting to memoriz e all the worlds facts and knowledge in the learned parameters of a singl e monolithic LM\, we instead provide the model an explicit knowledge stor e (e.g. a collection of web pages from Wikipedia) that can be used to loo k up information in real time. This is a relatively new research directio n where best practices are still forming\, but I will argue retrieval aug mentation is a very general idea that can lead to much more efficient tra ining\, can provide fundamentally new insights into how LLMs work\, and i s broadly applicable to a range of settings\, including e.g. models that do text-to-image generation. I will also provide\, to the best of my abil ity\, a guess about where things are going and what it would take to conv ince every major LLM to go non-parametric in the near future. \n\nSPEAKER BIO: \nLuke Zettlemoyer is a Professor in the Paul G. Allen School of Co mputer Science & Engineering at the University of Washington\, and a Rese arch Director at Meta. His research focuses on empirical methods for natu ral language semantics\, and involves designing machine learning algorith ms and models\, introducing new tasks and datasets\, and\, most recently\ , studying how to best develop self-supervision signals for pre-training. His honors include being named an ACL Fellow\, winning a PECASE award\, an Allen Distinguished Investigator award\, and multiple best paper award s. Read more: https://www.cs.washington.edu/people/faculty/lsz DURATION:PT1H DTSTAMP:20230417T192824Z DTSTART;TZID=America/New_York:20230424T160000 LAST-MODIFIED:20230417T192824Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Nonparametric Language Models: Trading Data for Parameters (and Co mpute) in Large Language Models UID:CAL-8a0182b3-870a191e-0187-90b09600-00003944demobedework@mysite.edu URL:https://www.cs.duke.edu/events/node/4257 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_PrattSc hool,":Pratt School of Engineering X-BEDEWORK-SPEAKER:Luke Zettlemoyer X-BEDEWORK-DUKE-SERIES:Triangle Computer Science Distinguished Lecturer Se ries X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tatiana.margitic@duke.ed u:Tatiana Margitic X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Triangle CS Distinguished Lecturer Series Apr 24 with Univ of Washington Professor and Meta Research Director Luke Zettle moyer on Nonparametric Language Models X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/dukecalendar24aprTCSDLS_20230417072824PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/dukecalendar24aprTCSDLS_202304170728 24PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230821T131931Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 am. \n\nABSTRACT: \nIn a dynamic optimization problem\, the goal is to maintain a solution to an optimization problem under insertions and deletions. We are interested i n trade-offs between the stability of the solution and its approximation ratio. To formalize this\, we introduce the concept of k-stable algorithm s\, which are algorithms that apply at most k changes to the solution upo n each insertion and deletion. We are particularly interested in stable a pproximation schemes\, which are update algorithms that\, for any given p arameter ε >0\, are k(ε)-stable and maintain a solution with approximatio n ratio 1+ε\, where the stability parameter k(ε) only depends on ε and no t on the size of the current input. In this talk I will discuss stable ap proximation schemes (or the non-existence thereof) for three problems: th e Broadcast Range-Assignment Problem\, Maximum Independent Set\, and Domi nating Set. The talk is based on joint work with Arpan Sadhukhan and Frit s Spieksma. \n\nSPEAKER BIO: \nMark de Berg received a PhD from Utrecht U niversity in 1992\, after which he became assistant and associate profess or at the same university. Currently he is a full professor at the TU Ein dhoven. His main research interest is in algorithms and data structures\, in particular for spatial data. He is (co-)author of two books on comput ational geometry and he has published over 250 peer-reviewed papers in jo urnals and conferences. Mark is associate editor of SICOMP\, Algorithmica \, and JoCG and former associate editor of CGTA\, IJCGA\, JDA. He was an elected member of the Computational Geometry Steering Committee\, the cha ir of the Steering Committee of the European Symposium on Algorithms\, an d he served on the Program Committee of over 50 international conferences . DURATION:PT1H DTSTAMP:20230821T152746Z DTSTART;TZID=America/New_York:20230918T120000 LAST-MODIFIED:20230821T152746Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Stable Approximation Schemes UID:CAL-8a02906b-8a0a73d8-018a-184024d3-00004c2edemobedework@mysite.edu URL:https://cs.duke.edu/events/stable-approximation-schemes X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=pankaj@duke.edu:Pankaj K . Agarwal X-BEDEWORK-SPEAKER:Mark de Berg X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Colloquium Sept 18 on Stable Approximati on Schemes with Mark de Berg from Eindhoven Univ of Tech X-BEDEWORK-IMAGE:/public/Images/markdebergdukecalendar2_20230821032746PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/markdebergdukecalendar2_202308210327 46PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230918T155338Z DESCRIPTION:ABSTRACT: \nRecent years have seen tremendous progress in mode ling graph-structured data through deep learning\, transforming models' a bility to understand relational structure. In this talk I will demonstrat e explorations that leverage graph structure to empower complex and effic ient reasoning in various machine learning scenarios\, focusing on the us e in foundation models. This talk focuses on 3 aspects of foundation mode ls: architecture\, objectives\, and inference\, where we leverage graph a nd relational learning for intelligent and efficient reasoning. In partic ular\, I will discuss sparse and efficient transformer architecture backb one\, efficient self-supervised pre-training pipeline\, and the use of re lational reasoning in large language models. \n\nLUNCH:\nLunch will be se rved at 11:45 AM.\n\nBIO: \nRex Ying is an assistant professor in the Dep artment of Computer Science at Yale University. His research focus includ es algorithms for graph learning\, geometric embeddings\, and trustworthy deep learning. He is the author of many widely used graph learning algor ithms such as GraphSAGE\, PinSAGE and GNNExplainer. Rex worked on a varie ty of applications of graph learning in physical simulations\, social net works\, NLP\, knowledge graphs and biology. He developed the first billio n-scale graph embedding services at Pinterest\, and the graph-based anoma ly detection algorithm at Amazon. Graduating with highest distinction\, h e received his B.S. degree at Duke\, his Ph.D. at Stanford\, and won the dissertation award at KDD 2022. DURATION:PT1H DTSTAMP:20230920T143419Z DTSTART;TZID=America/New_York:20231002T120000 LAST-MODIFIED:20230920T143419Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Graph Learning for Intelligent and Efficient Reasoning UID:CAL-8a03932d-8a96243a-018a-a8ff5144-00002de9demobedework@mysite.edu URL:https://cs.duke.edu/events/graph-learning-intelligent-and-efficient-re asoning X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=xiaobai.sun@duke.edu:Xia obai Sun\, Ph.D. X-BEDEWORK-SPEAKER:Rex Ying\, Yale CS Assistant Professor X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Colloquium Oct 2 on Graph Learning for I ntelligent and Efficient Reasoning with Rex Ying from Yale X-BEDEWORK-IMAGE:/public/Images/rexyingdukecalendar_20230920023419PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/rexyingdukecalendar_20230920023419PM -thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Teaching & Classroom Learning CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231020T183221Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nThi s presentation will demonstrate instruction of an abstract mathematical c oncept in a manner which shows how embedded the concept is within concret e computer science applications. The goal of this approach is to stimulat e creativity in problem-solving through the continuous connection of theo retical ideas to different forms of professional practice. The demonstrat ion will be followed by further discussion of how theory-to-application p roblems can be addressed through research\, mentorship and workshops. Und ergraduate research in machine learning will be most successful in an env ironment with clear goals\, focused scope of study\, and ongoing faculty support. Constraining research to what can be achieved with limited resou rces can actually be a positive learning experience for students as they are forced to think creatively to achieve their stated research goals\; h owever\, adequate mentorship is necessary for students who are doing rese arch for the first time. Additionally\, essential skills to communicate r esearch findings can be developed through offering targeted workshops in LaTeX\, technical writing\, and best practices for visual representation of data. \n\nSPEAKER BIO: \nLorenzo Luzi is an electrical and computer en gineering PhD student at Rice University in Houston conducting research u nder Dr. Richard Baraniuk. As a National Science Foundation and Texas Ins truments fellow\, Lorenzo studies the mathematics which underlie machine learning algorithms\, such as generative adversarial networks and diffusi on models. This is currently his primary area of research\, but more gene rally he is interested in using mathematics to solve practical problems a s well as the development and mentorship of rising students in computing fields. Additionally\, Lorenzo has collaborated since 2014 with researche rs at Pacific Northwest National Laboratory on work related to signal pro cessing and machine learning. Lorenzo received his M.S. in electrical and computer engineering from Rice University and his B.S. in electrical eng ineering from Washington State University. DURATION:PT1H DTSTAMP:20231020T211139Z DTSTART;TZID=America/New_York:20231024T120000 LAST-MODIFIED:20231020T211139Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Teaching Abstract Ideas with Concrete Applications in Mind UID:CAL-8a0382b5-8b29fde3-018b-4e5c1d89-00006b2fdemobedework@mysite.edu URL:https://cs.duke.edu/events/teaching-abstract-ideas-concrete-applicatio ns-mind X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=dus@cs.duke.edu:Susan Ro dger X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME="Teaching & Classroo m Learning":/user/public-user/Topics/Teaching Classroom Learning X-BEDEWORK-SPEAKER:Lorenzo Luzi\, Rice University X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Colloquium Oct 24 on Teaching Abstract I deas with Concrete Applications in Mind with Lorenzo Luzi from Rice X-BEDEWORK-IMAGE:/public/Images/lorenzoluzidukecalendar_20231020091139PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/lorenzoluzidukecalendar_202310200911 39PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Teaching & Classroom Learning CATEGORIES:Diversity/Inclusion CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231025T154915Z DESCRIPTION:LUNCH\nLunch will be served at 11:45 AM.\n\nABSTRACT\nIn this talk\, I will demonstrate how I would teach the topic of hash tables to s tudents. The module is designed for an introductory CS or data structures course. The demonstration does not involve programming but assumes stude nts have programming experience in at least one programming language. We will begin with a real-world application of hash tables\, then explain wh at hash tables are and their importance by\ncontrasting hashing to other alternatives. We will then introduce the concept of hash function and des cribe the goals and challenges of designing a good hash function. We will talk about collisions and possible approaches to resolving collisions. T he lecture will also involve multiple active learning exercises to demons trate how students can interleave between lecture and in-class activities to continue learning outside of the class.\n\nIn the second part of the talk\, I will talk about my research mentorship experience with undergrad uate students. The talk will highlight my ongoing work with student resea rchers on interdisciplinary research projects centered around socially be neficial applications leading to research articles in natural science and behavioral science domains. I will conclude with my future vision to des ign courses\, mentor undergraduate research\, and organize outreach event s to improve the student learning experience and to make computing educat ion accessible as a strategy to promote diversity.\n\nSPEAKER BIO\nTahiya Chowdhury is a Postdoctoral Research Fellow at the Davis Institute for A rtificial Intelligence and a visiting faculty member of the Department of Computer Science at Colby College. Her past degrees include a Master of Science in Computer Engineering with a focus on usable security and a PhD in Computer Engineering\, both from Rutgers University. Her current rese arch focuses on human-centered\, public-interest technology with artifici al intelligence. Tahiya is very interested in increasing participation in computer science and mentoring underrepresented students in interdiscipl inary research projects to prepare them for diverse career opportunities. DURATION:PT1H DTSTAMP:20231025T154915Z DTSTART;TZID=America/New_York:20231030T120000 LAST-MODIFIED:20231025T154915Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Introduction to Hash Table and Undergraduate Research for Promotin g Diversity in Computing UID:CAL-8a018d0d-8b5366bc-018b-6786998f-00002df5demobedework@mysite.edu URL:https://cs.duke.edu/events/introduction-hash-table-and-undergraduate-r esearch-promoting-diversity-computing X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Diversity/Inclusion:/user/pu blic-user/Topics/Diversity_Inclusion X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Teaching & Classroom Learnin g:/user/public-user/Topics/Teaching Classroom Learning X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-SPEAKER:Tahiya Chowdhury\, Davis Institute for AI\, Colby Colle ge X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=dus@cs.duke.edu:Susan Ro dger X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Colloquium Oct 30 on Introduction to Has h Table and Undergraduate Research for Promoting Diversity in Computing w ith Tahiya Chowdhury from Davis Institute for AI and Colby College CS X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/tahiyachowdhurydukecalendar_20231025034915 PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/tahiyachowdhurydukecalendar_20231025 034915PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Teaching & Classroom Learning CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231026T201534Z DESCRIPTION:LUNCH \nLunch will be served at 11:45 AM. \n\nABSTRACT\n1. Bin ary Heaps\nI will present the heap data structure. Two properties define a heap. First\, it is a complete binary tree\, so it is implemented using the array representation for complete binary trees. Second\, the values stored in a heap are partially ordered. This means that there is a relati onship between the value stored at any node and the values of its childre n. Due to its space and time efficiency\, the heap is a popular choice fo r implementing a priority queue. \n\n2. AP CS A Outreach program \nI will present the outreach program between Penn Engineering and the non-profit Heights Phila I am coordinating. The program's goals are: (1) to expand CS access & excellence across Philadelphia and (2) to prepare students fo r the AP CS A exam. The program runs year-long\, with in-person and onlin e instruction during the school year and in-person during the summer. Sin ce its inception\, the number of students enrolled has more than doubled\ , and more than 75% of the students who took the AP CS A exam passed with a score of 3 or above). I will also discuss future outreach\, teaching\, and research ideas and what makes Duke University a great place to imple ment those projects. \n\nSPEAKER BIO \nDr. Eric Fouh is an Assistant Prac tice Professor of Computer Science at the University of Pennsylvania\, wh ere he teaches a variety of introductory and advanced courses. His resear ch interests include computer science education\, learning sciences\, lea rning technologies\, and learning analytics. He is passionate about using technology to improve the teaching and learning of computer science. He is a member of the ACM Special Interest Group on Computer Science Educati on (SIGCSE). He has publications in top academic computing education conf erences and journals\, including the SIGCSE Technical Symposium\, the Int ernational Conference on Learning Analytics & Knowledge (LAK)\, and Compu ters in Human Behavior. DURATION:PT1H DTSTAMP:20231027T122604Z DTSTART;TZID=America/New_York:20231101T120000 LAST-MODIFIED:20231027T122604Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Binary Heaps and AP CS A Outreach Program UID:CAL-8a018d0d-8b5366bc-018b-6da0c4e6-000064fedemobedework@mysite.edu URL:https://cs.duke.edu/events/binary-heaps-and-ap-cs-outreach-program X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=dus@cs.duke.edu:Susan Ro dger X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME="Teaching & Classroom Le arning":/user/public-user/Topics/Teaching Classroom Learning X-BEDEWORK-SPEAKER:Dr. Eric Fouh\, UPenn CIS X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Colloquium Nov 1 on Binary Heaps and AP CS A Outreach Program with Eric Fouh\, Assistant Practice Professor of Co mputer Science at UPenn X-BEDEWORK-IMAGE:/public/Images/ericfouhdukecalendar_20231027122604PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/ericfouhdukecalendar_20231027122604P M-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Teaching & Classroom Learning CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231030T155203Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nI w ill start this talk with a teaching demo on differential privacy-a privac y-enhancing technology that allows for aggregate data analysis while offe ring a form of provable privacy protection for individuals. Differential privacy has been deployed by companies like Apple and Microsoft as well a s government agencies like the US Census Bureau. Next\, I will discuss my research on privacy communication\, including my work on developing expl anations of differential privacy for data subjects. I will also discuss o ngoing work and future directions that I hope to explore at Duke. \n\nSPE AKER BIO: \nMary Anne Smart is a Ph.D. candidate at UC San Diego. In her research\, Mary Anne identifies shortcomings in privacy communication and develops explanations of privacy-enhancing technologies. She is passiona te about teaching and has experience teaching a range of classes as an in structional assistant as well as experience teaching a class on probabili stic reasoning as the instructor of record. She is particularly excited a bout opportunities to teach classes related to HCI\, AI\, data privacy\, or discrete math. DURATION:PT1H DTSTAMP:20231030T155203Z DTSTART;TZID=America/New_York:20231102T120000 LAST-MODIFIED:20231030T155203Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Improving Privacy Communication UID:CAL-8a018d0d-8b5366bc-018b-8148f58f-00005c67demobedework@mysite.edu URL:https://cs.duke.edu/events/improving-privacy-communication X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Teaching & Classroom Learnin g:/user/public-user/Topics/Teaching Classroom Learning X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Mary Anne Smart\, UC San Diego X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=dus@cs.duke.edu:Susan Ro dger X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Improving Privacy Communication Duke CS Colloqui um Nov 2 with Mary Anne Smart of UC San Diego X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/maryannesmartdukecalendar_20231030035203PM .jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/maryannesmartdukecalendar_2023103003 5203PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Medicine CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Health/Wellness CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20230928T182615Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nDet ermining emergency department (ED) nurse staffing decisions to balance th e quality of service and staffing cost can be extremely challenging\, esp ecially when there is a high level of uncertainty in patient demand. Incr easing data availability and continuing advancements in predictive analyt ics provide an opportunity to mitigate demand uncertainty by utilizing de mand forecasts. In this work\, we study a two-stage prediction-driven sta ffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) staffing decisi ons in the ED. We quantify the benefit of having the ability to use the m ore expensive surge staffing and identify the importance of balancing dem and uncertainty versus demand stochasticity. We also propose a near-optim al two-stage staffing policy that is straightforward to interpret and imp lement. Lastly\, we develop a unified framework that combines parameter e stimation\, real-time demand forecasts\, and capacity sizing in the ED. S imulation experiments for the ED demonstrate that the proposed framework can reduce annual staffing costs by 11%-16% ($2 M-$3 M) while guaranteein g timely access to care. \n\nSPEAKER BIO: \nJing Dong is the Regina Pitar o Associate Professor of Business in the Decision\, Risk\, and Operations Division at Columbia Business School. Her research is at the interface o f applied probability and service operations management\, with a special focus on patient flow management in healthcare delivery systems. She rece ived her Ph.D. in Operations Research from Columbia University. Before jo ining Columbia Business School\, she was on the faculty of Northwestern U niversity. DURATION:PT1H DTSTAMP:20230928T182852Z DTSTART;TZID=America/New_York:20231106T120000 LAST-MODIFIED:20230928T182852Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Prediction-driven Surge Planning with Application in the Emergency Department UID:CAL-8a03932d-8a96243a-018a-dd0aa134-00000fc9demobedework@mysite.edu URL:https://cs.duke.edu/events/prediction-driven-surge-planning-applicatio n-emergency-department X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Health_Wellness:/user/public -user/Topics/Health_Wellness X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Medicine:/user/public-user/T opics/Medicine X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=anru.zhang@duke.edu:Dr. Anru Zhang X-BEDEWORK-SPEAKER:Dr. Jing Dong\, Columbia Business School X-BEDEWORK-DUKE-SERIES:Duke Computer Science Seminar X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) END:VEVENT BEGIN:VEVENT CATEGORIES:Teaching & Classroom Learning CATEGORIES:Diversity/Inclusion CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231102T194539Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nThe Web is a fundamentally insecure place. In this lecture\, I'll present so me of the historical context for why this is and describe how Transport L ayer Security (TLS) protects confidentiality\, integrity\, and authentici ty online. Targeted to an audience with general computing knowledge but n o networking or security experience\, students should leave this talk und erstanding both the basic technical mechanisms implemented by TLS to supp ort the (largely invisible) work of creating secure communication channel s and the distinction between the security goals of TLS vs. the security goals of user credentials. \n\nSPEAKER BIO: \nMelva T. James\, PhD is a t echnical consultant at Ab Initio Software in Lexington\, MA. They have a strong foundation in the planning and execution of scientific studies\, d ata analysis\, and technical communication\, and they bring over a decade of expertise in STEM research. Their specialized skills include systems- level threat and vulnerability assessment\, software usability assessment \, and persuasive software design. Dr. James earned their Ph.D. in Human- Centered Computing from Clemson University. Their dissertation research i nvolved the iterative design\, building\, and testing of a mobile applica tion to support food consumption monitoring and decision making. They als o hold degrees in chemistry from the Massachusetts Institute of Technolog y (SM '07\; ABD) and the University of Mississippi. Throughout their care er\, Dr. James has actively pursued opportunities to teach and mentor bot h STEM students and new professionals entering the computing field. From developing courses in software security at Ab Initio to teaching computer systems engineering at MIT\, Dr. James has shared their knowledge in a v ariety of settings and is committed to broadening participation in comput er science from all segments of society. DURATION:PT1H DTSTAMP:20231103T140802Z DTSTART;TZID=America/New_York:20231107T120000 LAST-MODIFIED:20231103T140802Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:TLS Basics - Understanding How Transport Layer Security Protects Y ou Online UID:CAL-8a018ccf-8b87f80e-018b-9191e4f3-00005fc4demobedework@mysite.edu URL:https://cs.duke.edu/events/tls-basics-understanding-how-transport-laye r-security-protects-you-online X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Diversity/Inclusion:/user/pu blic-user/Topics/Diversity_Inclusion X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=dus@cs.duke.edu:Susan Ro dger X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME="Teaching & Clas sroom Learning":/user/public-user/Topics/Teaching Classroom Learning X-BEDEWORK-SPEAKER:Melva James\, PhD\, Technical Consultant at Ab Initio S oftware X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Colloquium Nov 7 on TLS Basics - Underst anding How Transport Layer Security Protects You Online with Dr. Melva Ja mes\, Technical Consultant at Ab Initio Software X-BEDEWORK-IMAGE:/public/Images/melvajamesdukecalendar1_20231103020802PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/melvajamesdukecalendar1_202311030208 02PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Teaching & Classroom Learning CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231106T194559Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nMy talk will be divided into two phases - a teaching demo and a talk on my t eaching pedagogy. For the teaching demo\, I will give an overview of mach ine learning covering common terms\, categories\, data processing\, and v alidation. For the talk\, I will go over my teaching pedagogy for computi ng education. I will cover the challenges faced by students in Computer S cience and how we can help our students overcome these challenges. I will also talk briefly about my teaching experience and my vision for the fut ure. \n \nBIO: \nTrevor Bonjour is a Ph.D. Candidate in Computer Science at Purdue University. His research focuses on developing reinforcement le arning techniques to build agents capable of detecting and adapting to no vel situations (unseen during training) in multi-agent environments. Prev iously\, he earned his master's degree in Computer Science from Johns Hop kins University\, where he worked on Causal Inference. Prior to that\, Tr evor worked as a Software Engineer for five years. Along with his Ph.D.\, he is working towards the Teaching and Learning in Engineering Graduate Certificate from the School of Engineering Education at Purdue. DURATION:PT1H DTSTAMP:20231106T194559Z DTSTART;TZID=America/New_York:20231110T120000 LAST-MODIFIED:20231106T194559Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Machine Learning Overview and Teaching Pedagogy for Computing Educ ation UID:CAL-8a018ccf-8b87f80e-018b-a62ba306-00001dcfdemobedework@mysite.edu URL:https://cs.duke.edu/events/machine-learning-overview-and-teaching-peda gogy-computing-education X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Teaching & Classroom Learnin g:/user/public-user/Topics/Teaching Classroom Learning X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Trevor Bonjour\, Purdue University X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=dus@cs.duke.edu:Susan Ro dger X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:-0.3333333333333144 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Duke CS Colloquium Nov 10 - ML Overview and Teac hing Pedagogy for Computing Education with Trevor Bonjour\, PhD Candidate in Computer Science from Purdue University X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/trevorbonjourdukecalendar_20231106074559PM .jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/trevorbonjourdukecalendar_2023110607 4559PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20231201T172736Z DESCRIPTION:ABSTRACT \nData mining is a discipline that develops scalable and effective methods for knowledge discovery from massive data. Howeve r\, most data mining studies have been focusing on mining structured\, se quenced\, and networked data although the real-world data is largely in h ighly unstructured\, text form. With the emergence of deep learning\, em bedding\, and large language models (LLMs)\, powerful new tools are being created for handling massive text data. In this talk\, we examine some recent studies on applying large language models for natural language pro cessing and text mining\, including discriminative topic mining\, text cl assification\, and taxonomy-guided information extraction and constructio n of theme-specific knowledgebases. We show that equipped with LLMs\, dat a mining-styled\, weakly supervised approach could be promising at transf orming massive text into structured knowledge and benefiting many downstr eam applications. We will also discuss what could be the future of data m ining and text mining with the rapid development of large language models . \n\nSPEAKER BIO \nJiawei Han is Michael Aiken Chair Professor in the De partment of Computer Science\, University of Illinois at Urbana-Champaign . He received ACM SIGKDD Innovation Award (2004)\, IEEE Computer Society Technical Achievement Award (2005)\, IEEE Computer Society W. Wallace McD owell Award (2009)\, Japan's Funai Achievement Award (2018)\, and was ele vated to Fellow of Royal Society of Canada (2022). He is Fellow of ACM an d Fellow of IEEE and served as the Director of Information Network Academ ic Research Center (INARC) (2009-2016) supported by the Network Science-C ollaborative Technology Alliance (NS-CTA) program of U.S. Army Research L ab and co-Director of KnowEnG\, a Center of Excellence in Big Data Comput ing (2014-2019)\, funded by NIH Big Data to Knowledge (BD2K) Initiative. Currently\, he is serving on the executive committees of two NSF funded r esearch centers: MMLI (Molecular Make Research Institute)-one of NSF fun ded national AI centers since 2020 and I-Guide-The National Science Found ation (NSF) Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) since 2021. \n\nTRIANGLE COMPUTER SCIENC E DISTINGUISHED LECTURER SERIES \nThe CS departments at Duke\, NC State\, and UNC-Chapel Hill joined forces to create the Triangle Computer Scienc e Distinguished Lecturer Series. Read more: https://cs.unc.edu/news/tcsdl s DURATION:PT1H DTSTAMP:20231201T184020Z DTSTART;TZID=America/New_York:20240122T160000 LAST-MODIFIED:20231201T184020Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Data Mining Will Be Reborn with Large Language Models UID:CAL-8a018ccf-8b87f80e-018c-266beea3-000073bademobedework@mysite.edu URL:https://cs.duke.edu/events/data-mining-will-be-reborn-large-language-m odels X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=ilene.mccarthy@duke.edu: Ilene McCarthy X-BEDEWORK-SPEAKER:Jiawei Han\, UIUC Michael Aiken Chair and CS Professor X-BEDEWORK-DUKE-SERIES:Triangle CS Distinguished Lecturer Series X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:529.5 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:529.5 X-BEDEWORK-IMAGE-CROP-HEIGHT:353 X-BEDEWORK-IMAGE-ALT-TEXT:Jiawei Han\, CS Chair and Professor UIUC present s Data Mining Will Be Reborn with Large Language Models at the Triangle C S Distinguished Lecturer Series Jan 22 X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/dukecalendar22jan2024tcsdls_20231201052736 PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/dukecalendar22jan2024tcsdls_20231201 052736PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240119T181824Z DESCRIPTION:SNACKS: \nRefreshments will be served at 10:15 AM. \n\nABSTRAC T: \nDrug discoveries have been instrumental in improving global health o ver the last century\, but the median drug now takes about 10 years to br ing to market and costs over a billion dollars to develop. My work aims t o expedite the development of precise diagnostics and therapeutics by app lying machine learning. In this talk\, I will outline two recent research directions. In the first part\, we use single cell multi-omics to discov er regulatory mechanisms governing gene expression. This approach relies on methodological innovation\, developing new Granger causal inference te chniques to capitalize on the simultaneous but separate measures of cell state. In the second part\, I will introduce the application of large lan guage models to model protein interaction and function. These protein lan guage models enable powerful new approaches to predicting and understandi ng protein-protein and protein-drug interactions. I will conclude by sugg esting some collaborative directions of computational work\, originating from these biological applications. \n\nSPEAKER BIO: \nDr. Rohit Singh is an Assistant Professor in the Departments of Biostatistics & Bioinformat ics\, Cell Biology\, and Electrical and Computer Engineering at Duke Univ . Dr. Singh's research interests lie in computational biology\, with a fo cus on leveraging machine learning for in-depth analysis of cellular syst ems and enhancing drug discovery efficacy. His laboratory's primary resea rch directions include the application of single-cell genomics and large language models to dissect disease mechanisms\, understand biomolecular i nteractions\, and discover novel drug targets and compounds. He is the re cipient of the Test of Time Award at RECOMB\, MIT's George M. Sprowls Awa rd for his PhD thesis in Computer Science\, and Stanford's Christopher St ephenson Memorial Award for Masters Research in the same field. In additi on to academia\, he has experience in the industry. DURATION:PT1H DTSTAMP:20240119T181824Z DTSTART;TZID=America/New_York:20240124T103000 LAST-MODIFIED:20240119T181824Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Machine Learning for Precise Diagnostics and Therapeutics UID:CAL-8a0292fd-8d13410f-018d-22f20c42-00006dc3demobedework@mysite.edu URL:https://cs.duke.edu/events/machine-learning-precise-diagnostics-and-th erapeutics X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Rohit Singh X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=amink@cs.duke.edu:Alexan der Hartemink X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Rohit Singh\, Assistant Professor of Biostatisti cs & Bioinformatics\, Cell Biology\, and ECE at Duke X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/rohitsinghdukecalendar_20240119061824PM.jp g X-BEDEWORK-THUMB-IMAGE:/public/Images/rohitsinghdukecalendar_2024011906182 4PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2414c987-000002ca:Munagala\, Kamesh CREATED:20240116T122039Z DESCRIPTION:REFRESHMENTS \nSnacks will be served at 3:45 PM. \n\nABSTRACT \nTuring-complete blockchain protocols approximate the idealized abstract ion of a "computer in the sky" that is open access\, runs in plain view\, and\, in effect\, has no owner or operator. This technology can\, among other things\, enable stronger notions of ownership of digital possession s than we have ever had before. Building the computer in the sky is hard - and scientifically fascinating. This talk will highlight three threads in Dr. Roughgarden's recent research on this challenge: \n\n-Possibility and impossibility results for permissionless consensus protocols - i.e.\, implementing an "ownerless" computer.\n-Incentive-compatible transaction fee mechanism design - i.e.\, making an "open-access" computer sustainab le and welfare-maximizing.\n-A Black-Scholes-type formula for quantifying adverse selection in automated market makers\, some of the most popular "programs" running on the computer in the sky. \n\nThe talk will emphasiz e the diversity of mathematical tools necessary for understanding blockch ain protocols and their applications\, such as distributed computing\, ga me theory and mechanism design\, continuous-time finance\, etc. and also the immediate practical impact that mathematical work on this topic has h ad. \n\nSPEAKER BIO \nTim Roughgarden is a Professor in the Computer Scie nce Department at Columbia University and the Founding Head of Research a t a16z crypto. Prior to joining Columbia\, he spent 15 years on the compu ter science faculty at Stanford\, following a PhD at Cornell and a postdo c at UC Berkeley. His research interests include the many connections bet ween computer science and economics\, as well as the design\, analysis\, applications\, and limitations of algorithms. \n\nTRIANGLE COMPUTER SCIEN CE DISTINGUISHED LECTURER SERIES \nThe computer science departments at Du ke\, NC State\, and UNC-Chapel Hill joined forces to create the Triangle Computer Science Distinguished Lecturer Series in 1995. It is made possib le by grants from the US Army Research Office\, rotated between the depar tments. DURATION:PT1H DTSTAMP:20240116T171729Z DTSTART;TZID=America/New_York:20240129T160000 LAST-MODIFIED:20240116T171729Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:The Mathematics of the Computer in the Sky UID:CAL-8a008bcc-8cfbd545-018d-12377159-00003541demobedework@mysite.edu URL:https://cs.duke.edu/events/mathematics-computer-sky X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Tim Roughgarden\, Columbia University CS Professor X-BEDEWORK-DUKE-SERIES:Triangle Computer Science Distinguished Lecturer Se ries X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Tim Roughgarden\, Columbia Univ CS Professor X-BEDEWORK-IMAGE:/public/Images/dukecalendar29jan2024tcsdls2_2024011612350 2PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/dukecalendar29jan2024tcsdls2_2024011 6123502PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240123T163238Z DESCRIPTION:LUNCH \nLunch will be served at 1:15 PM. \n\nABSTRACT: \nBuild ing multisensory AI systems that learn from multiple sensory inputs such as text\, speech\, audio\, video\, real-world sensors\, wearable devices\ , and medical data holds great promise for impact in many scientific area s with practical benefits\, such as in supporting human health and well-b eing\, enabling multimedia content processing\, and enhancing real-world autonomous agents. \n\nIn this talk\, I will discuss my research on the m achine learning foundations of multisensory intelligence\, as well as pra ctical methods in building multisensory foundation models for many modali ties and tasks. In the first half\, I will present a new theoretical fram ework formalizing how modalities interact with each other to give rise to new information for a task. These interactions are the basic building bl ocks in all multimodal problems\, and their quantification enables users to understand their multimodal datasets and design principled approaches to learn these interactions. In the second part\, I will present my work in cross-modal attention and multimodal transformer architectures that no w underpin many of today's multimodal foundation models. Finally\, I will discuss our collaborative efforts in applying multisensory AI for real-w orld impact: (1) aiding mental health practitioners by predicting daily m ood fluctuations in patients using multimodal smartphone data\, (2) suppo rting doctors in cancer prognosis using histology images and multiomics d ata\, and (3) enabling robust control of physical robots using cameras an d touch sensors. \n\nSPEAKER BIO: \nPaul Liang is a PhD student in Machin e Learning at CMU\, advised by Louis-Philippe Morency and Ruslan Salakhut dinov. He studies the machine learning foundations of multisensory intell igence to design practical AI systems that integrate\, learn from\, and i nteract with a diverse range of real-world sensory modalities. His work h as been applied in affective computing\, mental health\, pathology\, and robotics. He is a recipient of the Siebel Scholars Award\, Waibel Preside ntial Fellowship\, Facebook PhD Fellowship\, Center for ML and Health Fel lowship\, Rising Stars in Data Science\, and 3 best paper/honorable menti on awards at ICMI and NeurIPS workshops. Outside of research\, he receive d the Alan J. Perlis Graduate Student Teaching Award for instructing cour ses on multimodal ML and advising students around the world in directed r esearch. DURATION:PT1H DTSTAMP:20240126T152455Z DTSTART;TZID=America/New_York:20240201T133000 LAST-MODIFIED:20240126T152455Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Foundations of Multisensory Artificial Intelligence UID:CAL-8a0292fd-8d13410f-018d-372aa887-0000675cdemobedework@mysite.edu URL:https://cs.duke.edu/events/foundations-multisensory-artificial-intelli gence X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_PrattSc hool_ECE,/principals/users/agrp_PrattSchool,":Electrical and Computer Eng ineering (ECE)\,Pratt School of Engineering X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=bdhingra@cs.duke.edu:Bhu wan Dhingra X-BEDEWORK-SPEAKER:Paul Liang X-BEDEWORK-DUKE-SERIES:Duke Computer Science/Electrical Computer Engineeri ng Colloquium X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:529.5 X-BEDEWORK-IMAGE-Y2:353 X-BEDEWORK-IMAGE-CROP-WIDTH:529.5 X-BEDEWORK-IMAGE-CROP-HEIGHT:353 X-BEDEWORK-IMAGE-ALT-TEXT:Paul Liang of CMU X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/paulliangdukecalendar_20240123043238PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/paulliangdukecalendar_20240123043238 PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Conference/Symposium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240202T204924Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nAI has emerged as the most transformative and revolutionary technique\, resh aping many aspects of our lives. Its intersection with science\, particul arly physics\, has opened new avenues for understanding our world and uni verse. This understanding is grounded in centuries of exploration by bril liant minds. Physics studies today predominantly rely on rigorous methods founded on universal physical laws. I will discuss integrating advanced learning techniques\, notably Bayesian machine learning\, into computatio nal physics in this presentation. This integration is crucial in an inter disciplinary field that combines mathematics\, physics\, and computer sci ence to address meaningful\, real-world problems. As the first principle\ , physics offers novel techniques and insights for tackling complex tasks in complex\, structured data analysis. I envision synergizing physics an d probabilistic learning to create a formidable tool for exploring new fr ontiers. \n\nSPEAKER BIO: \nShibo Li\, a fifth-year Ph.D. candidate at th e University of Utah\, is affiliated with the Kahlert School of Computing (SoC) and the Scientific Computing and Imaging Institute (SCI). He earne d his master's degree from the University of Pittsburgh and his bachelor' s degree from the South China University of Technology. Shibo's research spans a range of topics\, including Bayesian machine learning\, approxima te inference\, interactive learning (encompassing Active Learning\, Bandi ts\, and Reinforcement Learning)\, and high-dimensional spatial-temporal modeling. His Ph.D. thesis focuses on multi-fidelity modeling and optimiz ation for physical simulations. His works have been published in top-tier machine learning and data mining avenues\, such as ICML\, NeurIPS\, ICLR \, AISTATS\, IJCAI\, and CIKM. More information can be found on Shibo's h ome page: https://imshibo.com/ DURATION:PT1H DTSTAMP:20240202T204924Z DTSTART;TZID=America/New_York:20240202T120000 LAST-MODIFIED:20240202T204924Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Physics-Motivated and Inspired Probabilistic Learning UID:CAL-8a0292fd-8d13410f-018d-6b955577-00002aeedemobedework@mysite.edu URL:https://cs.duke.edu/events/physics-motivated-and-inspired-probabilisti c-learning?check_logged_in=1 X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Conference_Symposium:/user/p ublic-user/Lectures_Conferences/Conference_Symposium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Shibo Li\, University of Utah X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tomasi@duke.edu:Carlo To masi X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Shibo Li PhD Candidate\, University of Utah\, Ka hlert School of Computing\, Scientific Computing and Imaging Institute X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/shibolidukecalendar_20240202084924PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/shibolidukecalendar_20240202084924PM -thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240125T181812Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nPhy sics simulation has become the third pillar of science and engineering\, along with theory and experiments. The overarching objective of my cross- disciplinary research is to democratize physics simulation. This is achie ved through a thoughtful fusion of cutting-edge AI methodologies and clas sical numerical methods. In this talk\, I will introduce three research t hreads that align with this overarching theme. These threads will harness various machine learning tools (e.g.\, neural fields) to improve physics simulations' (1) accuracy\, (2) speed\, and (3) accessibility. A recurri ng theme in all three threads is the exceptional generalization capabilit ies of these ML-enhanced simulations\, thanks to the careful incorporatio n of partial differential equations (PDEs) as an inductive bias. \n\nSPEA KER BIO: \nPeter Yichen Chen is a postdoctoral researcher at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)\, working with Pr ofessor Wojciech Matusik. He earned his Ph.D. in computer science from Co lumbia University under the guidance of Professor Eitan Grinspun. Before this\, Peter was a Sherwood-Prize-winning mathematics undergraduate at UC LA. His research empowers 3D content creation for artists\, enhances desi gn/fabrication/control for engineers\, and aids material discovery for sc ientists. He publishes in machine learning\, computer graphics\, scientif ic computing\, mechanics\, and robotics. DURATION:PT1H DTSTAMP:20240125T181812Z DTSTART;TZID=America/New_York:20240205T120000 LAST-MODIFIED:20240125T181812Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Neural PDE: Towards AI-Enhanced Physics Simulation UID:CAL-8a0292fd-8d13410f-018d-41d8059b-0000151bdemobedework@mysite.edu URL:https://cs.duke.edu/events/neural-pde-towards-ai-enhanced-physics-simu lation X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-SPEAKER:Dr. Peter Yichen Chen\, MIT CS and AI Lab (CSAIL) X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tomasi@duke.edu:Carlo To masi X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Dr. Peter Yichen Chen of MIT CS and AI Lab (CSAI L) X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/peteryichenchendukecalendar_20240125061812 PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/peteryichenchendukecalendar_20240125 061812PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240129T145427Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nFor years\, my dream has been to create autonomous AI agents capable of carr ying out tedious procedural tasks (e.g.\, arranging conference travel)\, allowing me to focus on more creative and exciting tasks. Modern AI model s\, especially large language models (LLMs) like ChatGPT\, have suddenly brought us much closer to achieving such AI agents. But\, has my dream al ready come true? In this talk\, I will answer this question by delving in to my systematic evaluation of AI agents in realistic tasks. The evaluati on uncovers many critical limitations of AI agents\, such as accurate gro unding\, long-term planning\, and tool use. It suggests that LLMs are cru cial yet early steps towards AI autonomy. To address these challenges\, I will introduce my research of a more suitable "language" for AIs\, which overcomes the inherent limitations of using natural language for task so lving. Finally\, I will discuss my work on teaching AI agents to learn ne w tools by reading the tool documentation rather than direct demonstratio ns. \n\nSPEAKER BIO: \nShuyan Zhou is a final-year PhD student at the Lan guage Technologies Institute at CMU\, advised by Graham Neubig. Her resea rch in NLP and AI focuses on creating AI agents for real-world tasks\, su ch as using computers and generating code. Her work has been recognized a t top natural language processing and machine learning conferences and jo urnals such as ICLR\, ICML\, ACL\, EMNLP\, and TACL. DURATION:PT1H DTSTAMP:20240129T145427Z DTSTART;TZID=America/New_York:20240207T120000 LAST-MODIFIED:20240129T145427Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Solving Real-World Tasks with AI Agents UID:CAL-8a0292fd-8d13410f-018d-55b6ecee-00002379demobedework@mysite.edu URL:https://cs.duke.edu/events/solving-real-world-tasks-ai-agents X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Shuyan Zhou\, CMU Language Technologies Institute X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=cynthia@cs.duke.edu:Dr. Cynthia Rudin X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Shuyan Zhou\, CMU Language Technologies Institut e X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/shuyanzhoudukecalendar_20240129025427PM.jp g X-BEDEWORK-THUMB-IMAGE:/public/Images/shuyanzhoudukecalendar_2024012902542 7PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240122T160819Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nWit h the increase of safety-critical traffic on the Internet\, a challenge i s to provide high availability in the presence of adversarial actors. The SCION next-generation network architecture has been explicitly designed for security and scalability\, applying new ideas and novel concepts for achieving highly resilient control-plane operation and inter-domain end-t o-end communication in the face of active attacks. SCION has been in prod uction use for critical infrastructure communication since 2017\, with ex panding deployments and use cases since then. Operating side-by-side with today's Internet\, SCION offers a communication fabric that is largely f ault-independent from today's BGP-based infrastructure. \n\nGiven this ba ckdrop\, this talk will highlight use cases\, technical and business aspe cts of SCION that provide security properties such as geo-fencing and pat h validation and enable new business models for IPSs. We will also discus s interoperability\, how the fault-independence with today's infrastructu re is achieved\, and how the deployment and co-existence with today's inf rastructure is accomplished. Ultimately\, we cover the importance of open -source implementations\, communities\, IXPs\, and the SCION Association for the success of a next-generation network architecture. \n\nSPEAKER BI O: \nAdrian Perrig is a CS Professor at ETH Zürich\, Switzerland\, where he leads the network security group. He is also a Distinguished Fellow at CyLab\, and an Adjunct ECE Professor at CMU. He earned his MS and PhD de grees in CS from CMU. He is a recipient of the ACM SIGSAC Outstanding Inn ovation Award\, and is an ACM and IEEE Fellow. Adrian's research revolves around building secure systems\, and his group is currently working on t he SCION secure Internet architecture. DURATION:PT1H DTSTAMP:20240122T160819Z DTSTART;TZID=America/New_York:20240209T120000 LAST-MODIFIED:20240122T160819Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:The SCION Inter-Domain Routing Architecture UID:CAL-8a0292fd-8d13410f-018d-31ee08be-00001570demobedework@mysite.edu URL:https://cs.duke.edu/events/scion-inter-domain-routing-architecture X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Adrian Perrig\, CS Professor at ETH Zurich X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=michael.reiter@duke.edu: Mike Reiter X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Adrian Perrig\, CS Professor at ETH Zurich\, Swi tzerland X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/adrianperrigdukecalendar_20240122040819PM. jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/adrianperrigdukecalendar_20240122040 819PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240129T183017Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nThe ever-increasing scale of foundation models\, such as ChatGPT and AlphaFo ld\, has revolutionized AI and science more generally. However\, increasi ng scale also steadily raises computational barriers\, blocking almost ev eryone from studying\, adapting\, or otherwise using these models for any thing beyond static API queries. In this talk\, I will present research t hat significantly lowers these barriers for a wide range of use cases\, i ncluding inference algorithms that are used to make predictions after tra ining\, fine-tuning approaches that adapt a trained model to new data\, a nd finally\, full training of foundation models from scratch. \n\nSPEAKER BIO: \nTim Dettmers' research focuses on making foundation models\, such as ChatGPT\, accessible to researchers and practitioners by reducing the ir resource requirements. A PhD candidate at the University of Washington who has won oral\, spotlight\, and best paper awards at conferences such as ICLR and NeurIPS\, Tim Dettmers created the bits-and-bytes library fo r efficient deep learning\, which is growing at 1.4 million installations per month and received Google Open Source and PyTorch Foundation awards. DURATION:PT1H DTSTAMP:20240129T183017Z DTSTART;TZID=America/New_York:20240212T120000 LAST-MODIFIED:20240129T183017Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Accessible Foundation Models: Systems\, Algorithms\, and Science UID:CAL-8a0292fd-8d13410f-018d-567c85b0-00004e26demobedework@mysite.edu URL:https://cs.duke.edu/events/accessible-foundation-models-systems-algori thms-and-science X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-SPEAKER:Tim Dettmers\, University of Washington X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=cynthia@cs.duke.edu:Cynt hia Rudin X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Tim Dettmers from University of Washington X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/timdettmersdukecalendar_20240129063017PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/timdettmersdukecalendar_202401290630 17PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240131T193228Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nEff iciency is increasingly tied to quality to machine learning\, with more e fficient training algorithms leading to more powerful models trained on m ore data. However\, today's most popular machine learning models are buil t on asymptotically inefficient primitives. For example\, attention in Tr ansformers scales quadratically in the input size\, which makes it challe nging for Transformers to use long context. In this talk\, I discuss my w ork on improving the efficiency of the core primitives in machine learnin g\, with an emphasis on hardware-aware algorithms and long-context applic ations. In the first half\, I focus on replacing attention with gated sta te space models (SSMs) and convolutions\, which scale sub-quadratically i n context length. I describe the H3 (Hungry Hungry Hippos) architecture\, a gated SSM architecture that matches Transformers in quality up to 3B p arameters and achieves 2.4x faster inference. In the second half\, I focu s on developing hardware-aware algorithms for SSMs and convolutions. I de scribe FlashFFTConv\, a fast algorithm for computing SSMs and convolution s on GPU by optimizing the Fast Fourier Transform (FFT). FlashFFTConv yie lds up to 7x speedup and 5x memory savings\, even over vendor solutions f rom Nvidia. FlashFFTConv is now widely used in many gated SSM models\, in cluding language models\, image generation models\, and long-context DNA foundation models. \n\nSPEAKER BIO: \nDan Fu is a PhD student in the Comp uter Science Department at Stanford University\, where he is co-advised b y Christopher Ré and Kayvon Fatahalian. His research interests are at the intersection of systems and machine learning. Recently\, he has focused on developing algorithms and architectures to make machine learning more efficient\, especially for enabling longer-context applications. His rese arch has appeared as oral and spotlight presentations at NeurIPS\, ICML\, and ICLR\, and he has received the best student paper runner up at UAI. Dan has also been supported by an NDSEG fellowship. DURATION:PT1H DTSTAMP:20240131T193228Z DTSTART;TZID=America/New_York:20240213T120000 LAST-MODIFIED:20240131T193228Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Hardware-Aware Efficient Primitives for Machine Learning UID:CAL-8a0292fd-8d13410f-018d-61022d84-000044c8demobedework@mysite.edu URL:https://cs.duke.edu/events/hardware-aware-efficient-primitives-machine -learning X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Dan Fu\, Stanford University X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=cynthia@cs.duke.edu:Cynt hia Rudin X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Dan Fu of Stanford University X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/danfudukecalendar_20240131073228PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/danfudukecalendar_20240131073228PM-t humb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240131T171203Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nRec ent advancements in large language models (LLMs) have marked a significan t milestone in the field of natural language processing. Yet\, as we vent ure into the diverse and intricate terrain of the open world-encompassing varied topics\, domains\, and modalities-these models encounter formidab le challenges. Key among these are issues like hallucination\, grounded r easoning across multimodality\, and the burden of high computational dema nds. My research aims to tackle these challenges\, focusing on three broa d themes: (1) knowledge acquisition and understanding in the dynamic open world\; (2) generalizable and efficient intelligence\; (3) enhancing hum an experience when interacting with these technologies. In this talk\, I will mainly delve into the pursuit of generalizable and efficient intelli gence. I'll first introduce our latest advancements in endowing models wi th a broader cognitive scope\, evolving from answering simple questions t o complex questions\, and from understanding text to multimodality. Addit ionally\, I will also highlight our recent research on addressing task in terference\, a crucial but usually overlooked issue\, in the context of p arameter-efficient tuning. The talk will conclude with discussions into o ur future directions under these three pivotal themes. \n\nSPEAKER BIO: \ nLifu Huang is an Assistant Professor in the Computer Science department at Virginia Tech. He obtained a PhD in Computer Science from University o f Illinois at Urbana-Champaign in 2020. He has a wide range of research i nterests in natural language processing\, multimodal learning and machine learning. His research has been recognized with an Outstanding Paper Awa rd at ACL 2023 and Best Paper Award Honorable Mention at SIGIR 2023. He i s a recipient of the NSF CAREER Award in 2023 and Amazon Research Award i n 2021. DURATION:PT1H DTSTAMP:20240131T171203Z DTSTART;TZID=America/New_York:20240214T120000 LAST-MODIFIED:20240131T171203Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Bridging the Modern Language Modeling with the Complex Open World UID:CAL-8a0292fd-8d13410f-018d-60819e8e-00002ceedemobedework@mysite.edu URL:https://cs.duke.edu/events/bridging-modern-language-modeling-complex-o pen-world X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Lifu Huang X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=bdhingra@cs.duke.edu:Bhu wan Dhingra X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Lifu Huang X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/lifu-huangdukecalendar_20240131051203PM.jp g X-BEDEWORK-THUMB-IMAGE:/public/Images/lifu-huangdukecalendar_2024013105120 3PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240205T132521Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nAI has emerged as the most transformative and revolutionary technique\, resh aping many aspects of our lives. Its intersection with science\, particul arly physics\, has opened new avenues for understanding our world and uni verse. This understanding is grounded in centuries of exploration by bril liant minds. Physics studies today predominantly rely on rigorous methods founded on universal physical laws. I will discuss integrating advanced learning techniques\, notably Bayesian machine learning\, into computatio nal physics in this presentation. This integration is crucial in an inter disciplinary field that combines mathematics\, physics\, and computer sci ence to address meaningful\, real-world problems. As the first principle\ , physics offers novel techniques and insights for tackling complex tasks in complex\, structured data analysis. I envision synergizing physics an d probabilistic learning to create a formidable tool for exploring new fr ontiers. \n\nSPEAKER BIO: \nShibo Li\, a fifth-year Ph.D. candidate at th e University of Utah\, is affiliated with the Kahlert School of Computing (SoC) and the Scientific Computing and Imaging Institute (SCI). He earne d his master's degree from the University of Pittsburgh and his bachelor' s degree from the South China University of Technology. Shibo's research spans a range of topics\, including Bayesian machine learning\, approxima te inference\, interactive learning (encompassing Active Learning\, Bandi ts\, and Reinforcement Learning)\, and high-dimensional spatial-temporal modeling. His Ph.D. thesis focuses on multi-fidelity modeling and optimiz ation for physical simulations. His works have been published in top-tier machine learning and data mining avenues\, such as ICML\, NeurIPS\, ICLR \, AISTATS\, IJCAI\, and CIKM. DURATION:PT1H DTSTAMP:20240205T132521Z DTSTART;TZID=America/New_York:20240219T120000 LAST-MODIFIED:20240205T132521Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Physics-Motivated and Inspired Probabilistic Learning UID:CAL-8a0292fd-8d13410f-018d-7971dd18-00006640demobedework@mysite.edu URL:https://cs.duke.edu/events/physics-motivated-and-inspired-probabilisti c-learning X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Shibo Li\, University of Utah X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tomasi@duke.edu:Carlo To masi X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Shibo Li\, University of Utah X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/shibolidukecalendar_20240205012521PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/shibolidukecalendar_20240205012521PM -thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240206T182839Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nFor mal logic programs are useful tools in AI. However\, they require users t o first express the problem in a formal logic language\, which is difficu lt to do for many real-world problems. In this talk\, I will discuss an a lternative paradigm\, using large language models (LLMs) as informal logi c programs. In this paradigm\, the propositions are expressed in natural language and the reasoning steps are carried out by a prompted LLM.\n\nTh is talk will present 3 problems effectively addressed by this paradigm: 1 .) Event sequence modeling and prediction\, the task of reasoning about f uture events given the past. 2.) Natural language entailment. 3.) Embodie d reasoning\, in which a robot needs to plan multiple steps to complete a task. For all these problems\, our paradigm achieves stronger results th an classical methods using formal logic programs and/or using LLMs as sta ndalone solvers. \n\nSPEAKER BIO: \nDr. Hongyuan Mei is currently a Resea rch Assistant Professor at Toyota Technological Institute at Chicago (TTI C). He obtained his PhD from Johns Hopkins University (JHU) Computer Scie nce. Hongyuan's research spans ML and natural language processing. Curren tly\, he is most interested in harnessing and improving the reasoning cap abilities of large language models to solve challenging problems such as event prediction. His research has been supported by a Bloomberg Data Sci ence PhD Fellowship\, the 2020 JHU Jelinek Memorial Award\, and research gifts from Adobe and Ant Group. His technical innovations have been integ rated into real-world products such as Alipay\, the world's largest mobil e digital payment platform\, which serves more than one billion users. Hi s research has been covered by Fortune Magazine and Tech At Bloomberg. DURATION:PT1H DTSTAMP:20240206T182839Z DTSTART;TZID=America/New_York:20240221T120000 LAST-MODIFIED:20240206T182839Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Flexible Reasoning with Large Language Models as Informal Logic Pr ograms UID:CAL-8a0292fd-8d13410f-018d-7fade941-00002bb4demobedework@mysite.edu URL:https://cs.duke.edu/events/flexible-reasoning-large-language-models-in formal-logic-programs X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Hongyuan Mei\, TTIC X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=bdhingra@cs.duke.edu:Bhu wan Dhingra X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Hongyuan Mei\, TTIC X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/hongyuanmeidukecalendar_20240206062839PM.j pg X-BEDEWORK-THUMB-IMAGE:/public/Images/hongyuanmeidukecalendar_202402060628 39PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240207T191030Z DESCRIPTION:LUNCH \nLunch will be served at 11:45 AM \n\nABSTRACT \nComput er networks are at the foundation of modern society. When they fail\, ban ks go offline\, airplanes stop flying\, emergency numbers stop working\, and businesses lose millions of dollars. Over the last decade\, the netwo rking community has made significant progress toward technology that can provide formal correctness guarantees for network behavior. I co-develop ed one such tool\, called Batfish\, that is in use at many large enterpri ses. I'll share key lessons from its evolution from a research prototype to an industrial-strength product\, such as how Datalog-based logic prog ramming had significant limitations and how binary decision diagrams (BDD s) proved highly versatile. I will also outline key challenges in using c urrent verification technology and what is needed to further improve netw ork reliability by an order of magnitude. \n\nSPEAKER BIO \nRatul Mahajan is a faculty member at the University of Washington (Paul G. Allen Schoo l of Computer Science). He is also the co-director of UW FOCI (Future of Cloud Infrastructure) and an Amazon Scholar. Ratul is a computer systems researcher with a networking focus and has worked on a broad set of topic s\, including network verification\, connected homes\, network programmin g\, optical networks\, Internet routing and measurements\, and mobile sys tems. Many of the technologies that he has helped develop are part of rea l-world systems at Microsoft and other companies. Ratul has been recogniz ed as an ACM Distinguished Scientist\, an ACM SIGCOMM Rising Star\, and a Microsoft Research Graduate Fellow. His papers have won the ACM SIGCOMM Test-of-Time Award\, the IEEE William R. Bennett Prize\, the ACM SIGCOMM Best Paper Awards (twice)\, and the HVC Best Paper Award. DURATION:PT1H DTSTAMP:20240207T191030Z DTSTART;TZID=America/New_York:20240223T120000 LAST-MODIFIED:20240207T191030Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Network Verification: Lessons Learned and Outlook UID:CAL-8a0292fd-8d13410f-018d-84fa92a4-000056dademobedework@mysite.edu URL:https://cs.duke.edu/events/network-verification-lessons-learned-and-ou tlook X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-SPEAKER:Ratul Mahajan\, UW-CSE X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=danyang@cs.duke.edu:Dany ang Zhuo X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Ratul Mahajan\, UW-CSE X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/ratulmahajandukecalendar_20240207071030PM. jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/ratulmahajandukecalendar_20240207071 030PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240213T201459Z DESCRIPTION:LUNCH \nLunch will be served at 11:45 AM. \n\nABSTRACT \nAs ma ssive language models (LMs) like GPT-4 dominate natural language processi ng and AI\, extreme-scale has become a clear and frequent theme for succe ss. However\, increasing model size is inherently at odds with the intere sts of a diverse user base and community of open researchers. The largest models are typically closed to the public\, extremely energy-intensive\, and difficult to conduct systematic and reproducible research on. \n\nIn this talk\, I will discuss my vision for effective natural language proc essing beyond scale alone. I will begin by describing alternative approac hes\, more efficient methods that work with compact language models to un lock hidden capabilities. I begin with inference-time algorithms that wor k on top of existing models to allow new functionality. Next\, I describe a method for distilling valuable knowledge from extreme-scale models int o compact LMs. Finally\, I will explain my work towards understanding the limits that even extreme-scale language models suffer from\, with a part icular focus on how such models differ from human intuitions. \n\nSPEAKER BIO \nPeter West is PhD student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington\, working with Yeji n Choi. His research is focused on natural language processing and langua ge models\, particularly studying the capabilities and limits of both com pact and extreme-scale models. His work has received multiple awards\, in cluding best methods paper at NAACL 2022\, and outstanding paper awards a t ACL and EMNLP in 2023. His work has been supported in part by an NSERC PGS-D fellowship. Previously\, Peter received a BSc in computer science f rom the University of British Columbia. DURATION:PT1H DTSTAMP:20240213T201459Z DTSTART;TZID=America/New_York:20240304T120000 LAST-MODIFIED:20240213T201459Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Discovering Hidden Capabilities and Limits in Large Language Model s UID:CAL-8a0292fd-8d13410f-018d-a41bc60d-000075e1demobedework@mysite.edu URL:https://cs.duke.edu/events/discovering-hidden-capabilities-and-limits- large-language-models X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Peter West\, UW-CSE X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tomasi@duke.edu:Carlo To masi X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Peter West\, UW-CSE X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/peterwestdukecalendar_20240213081459PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/peterwestdukecalendar_20240213081459 PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240216T180036Z DESCRIPTION:SNACKS: \nSnacks will be served at 3:45 PM. \n\nABSTRACT: \nAn increasing number of security and privacy researchers are conducting res earch with the intention of informing public policy discussions. I will t alk about how I got interested in public policy and discuss a wide range of research projects I've worked on over the past 25 years that had some impact on public policy discussions including research on the usability o f privacy tools\, the cost of reading privacy policies\, various types of privacy labels\, password policy\, and the California privacy choice ico n. \n\nSPEAKER BIO: \nLorrie Faith Cranor is the Director and Bosch Disti nguished Professor of the CyLab Security and Privacy Institute and FORE S ystems University Professor of Computer Science and of Engineering and Pu blic Policy at Carnegie Mellon University. She directs the CyLab Usable P rivacy and Security Laboratory (CUPS) and co-directs the Privacy Engineer ing master's program. In 2016 she served as Chief Technologist at the US Federal Trade Commission. She co-founded Wombat Security Technologies. Sh e is a fellow of the ACM\, IEEE\, and AAAS and a member of the ACM CHI Ac ademy. \n\nZOOM BROADCAST: \nDr. Cranor will be giving her lecture from N orth Carolina State University. As she is a TCSDLS speaker\, we will be p roviding direct access to her talk via Zoom broadcast in LSRC D106 at Duk e. \n\nTRIANGLE COMPUTER SCIENCE DISTINGUISHED LECTURER SERIES: \nThe com puter science departments at Duke University\, North Carolina State Unive rsity\, and the University of North Carolina at Chapel Hill joined forces to create the Triangle Computer Science Distinguished Lecturer Series. T he lecture series began in the 1995-1996 academic year\, and is made poss ible by grants from the U.S. Army Research Office\, rotated among the dep artments. DURATION:PT1H DTSTAMP:20240216T180741Z DTSTART;TZID=America/New_York:20240304T160000 LAST-MODIFIED:20240216T180741Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:From Password Requirements to IoT Cybersecurity Labels: Informing Public Policy with Research UID:CAL-8a0292fd-8d13410f-018d-b313d0af-0000056edemobedework@mysite.edu URL:https://cs.duke.edu/events/password-requirements-iot-cybersecurity-lab els-informing-public-policy-research X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=michael.reiter@duke.edu: Mike Reiter X-BEDEWORK-SPEAKER:Lorrie Faith Cranor\, CMU X-BEDEWORK-DUKE-SERIES:Triangle Computer Science Distinguished Lecturer Se ries X-BEDEWORK-DUKE-WEBCAST:https://ncsu.zoom.us/j/97778793085?pwd=OG5CUW9GZHd Gelk0Rkk4Rmxab2lYQT09 X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Lorrie Faith Cranor\, CMU X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/dukecalendar4mar2024tcsdls_20240216060036P M.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/dukecalendar4mar2024tcsdls_202402160 60036PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240219T164336Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM \n\nABSTRACT: \nGene rative flow models learn a (possibly stochastic) mapping between source a nd target distributions. Common paradigms include diffusion models\, scor e matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow models using flow ma tching objectives using ideas from optimal transport. I will then show ho w these improved methods can be applied to the tasks of (1) modelling cel l dynamics\, which allow us to better understand disease programs - leadi ng to a new potential therapeutic pathway for triple-negative breast canc er and (2) generative protein design\, with applications to biologic drug discovery. \n\nSPEAKER BIO: \nAlex Tong is a postdoctoral researcher at Mila and Université de Montréal\, where he works with Yoshua Bengio at th e intersection of generative machine learning and biology with focuses on applications to cells and proteins. This work is part of a joint effort with Fabian Theis through the Helmholtz International Lab. He is also cof ounder of Dreamfold\, a Mila startup which builds generative models for p rotein design. DURATION:PT1H DTSTAMP:20240219T164336Z DTSTART;TZID=America/New_York:20240306T120000 LAST-MODIFIED:20240219T164336Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Flow Models with Applications to Cell Trajectories and Protein Des ign UID:CAL-8a0292fd-8d13410f-018d-c2406610-00005dafdemobedework@mysite.edu URL:https://cs.duke.edu/events/flow-models-applications-cell-trajectories- and-protein-design X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Alex Tong\, MILA and Dreamfold.ai X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=amink@cs.duke.edu:Alexan der Hartemink X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Alex Tong\, MILA and Dreamfold.ai X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/alextongdukecalendar_20240219044336PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/alextongdukecalendar_20240219044336P M-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Ethics CATEGORIES:Engineering CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240220T170723Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nEth ics discussions abound but translating "do no harm" into our work is frus trating at best\, and obfuscatory at worst. We can agree that keeping hum ans safe and in control is important\, but implementing ethics is intimid ating work. Learn how to wield your preferred technology ethics code to m ake systems that are accountable\, de-risked\, respectful\, secure\, hone st and usable. The presenter will introduce the topic of ethics and then step through a framework to guide teams successfully through this process . \n\nThis talk is for teams working on (or anticipating working on) emer ging technologies such as artificially intelligent (AI) systems. Attendee s do not need any previous experience or knowledge about ethics. \n\nSPEA KER BIO: \nCarol Smith is the AI Division Trust Lab Lead and a Principal Research Scientist at the Carnegie Mellon University (CMU)\, Software Eng ineering Institute. She leads research focused on development practices t hat result in trustworthy\, human-centered\, and responsible AI systems. Ms. Smith has been conducting research to improve the human experience wi th complex systems across industries for over 20 years. Since 2015 she ha s led research to integrate ethics and improve human experiences with AI systems\, autonomous vehicles\, and other complex and emerging technologi es. Ms. Smith is recognized globally as a leading researcher and user exp erience advocate and has presented over 250 talks and workshops in over 4 0 cities around the world. Her writing can be found in publications from organizations including AAAI\, ACM\, and the UXPA\, and she has taught co urses and lectured at CMU and other leading institutions. Ms. Smith is cu rrently an ACM Distinguished Speaker and a Working Group member of the IE EE P7008™ Standard. Ms. Smith holds a Master of Science degree in Human-C omputer Interaction from DePaul University. DURATION:PT1H DTSTAMP:20240220T170723Z DTSTART;TZID=America/New_York:20240321T120000 LAST-MODIFIED:20240220T170723Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Implementing Ethics in Emerging Technologies UID:CAL-8a0292fd-8d13410f-018d-c77c8724-000021f8demobedework@mysite.edu URL:https://cs.duke.edu/events/implementing-ethics-emerging-technologies X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use r/Topics/Engineering X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Ethics:/user/public-user/Top ics/Ethics X-BEDEWORK-SPEAKER:Carol Smith\, CMU X-BEDEWORK-DUKE-SERIES:ACM-W Distinguished Speaker X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=lisa@cs.duke.edu:Lisa Wu Wills X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Carol Smith\, CMU X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/carolsmithdukecalendar_20240220050723PM.jp g X-BEDEWORK-THUMB-IMAGE:/public/Images/carolsmithdukecalendar_2024022005072 3PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Technology CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20240221T165112Z DESCRIPTION:LUNCH: \nLunch will be served at 11:45 AM. \n\nABSTRACT: \nThe last decade saw a dramatic shift in how NLP systems are developed and de ployed: from bespoke models trained for specific tasks to a single Large Language Model (LLM) capable of solving a variety of tasks via in-context learning (ICL). In this talk\, I will discuss the problem of adapting LL Ms to out-of-distribution tasks and datasets. \n\nSPEAKER BIO: \nSilvio A mir is an assistant professor at the Khoury College of Computer Science a t Northeastern University. He completed his PhD at the University of Lisb on and was a postdoc at Johns Hopkins University before joining Northeast ern. Silvio Amir works on Natural Language Processing and Machine Learnin g methods to analyze personal and user generated text\, such as social me dia and clinical notes from Electronic Health Records. He is primarily in terested in tasks involving subjective\, personalized or user-level infer ences (e.g.\, opinion mining and digital phenotyping). In particular\, hi s work aims to improve the reliability\, interpretability and fairness of predictive models and analytics derived from these data. DURATION:PT1H DTSTAMP:20240221T165112Z DTSTART;TZID=America/New_York:20240408T120000 LAST-MODIFIED:20240221T165112Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Can One Model Rule Them All? Tailoring Large Language Models to Sp ecialized Domains\, Specific Populations\, and Unique Individuals UID:CAL-8a0292fd-8d13410f-018d-cc94125f-000043bfdemobedework@mysite.edu URL:https://cs.duke.edu/events/can-one-model-rule-them-all-tailoring-large -language-models-specialized-domains-specific X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Technology:/user/public-user /Topics/Technology X-BEDEWORK-SPEAKER:Silvio Amir\, Northeastern Univ. X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=bdhingra@cs.duke.edu:Bhu wan Dhingra X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:530 X-BEDEWORK-IMAGE-Y2:353.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Silvio Amir\, Northeastern Univ. X-BEDEWORK-SUBMITTEDBY:cr390 for Computer Science (agrp_ArtsandSciences_Co mputerScience) X-BEDEWORK-IMAGE:/public/Images/silvioamirdukecalendar_20240221045112PM.jp g X-BEDEWORK-THUMB-IMAGE:/public/Images/silvioamirdukecalendar_2024022104511 2PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Lecture/Talk CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Main CONTACT;X-BEDEWORK-UID=18832edc-1b31c518-011b-40a23061-00000095:Hester\, G lenda CREATED:20241001T183248Z DESCRIPTION:While there have been huge advances in Machine Learning in rec ent years\, many of the successes have relied on immense amounts of train ing data. Especially for sequential-decision-making tasks (the realm of reinforcement learning)\, obtaining such data from online experience can take a very long time. On the other hand\, learning can often be dramati cally accelerated by leveraging human input\, for example as demonstratio ns of successful task executions\, as interventions to correct mistakes\, or simply as evaluative feedback separating "correct" actions from incor rect actions. This talk focuses on such Human-in-the-Loop Machine Learni ng for robotics tasks\, covering both navigations\, especially in tightly constrained spaces\, and manipulation in open-world settings. DURATION:PT1H DTSTAMP:20241015T153342Z DTSTART;TZID=America/New_York:20241028T120000 LAST-MODIFIED:20241015T153342Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Human-in-the-Loop Machine Learning for Robot Navigation and Manipu lation UID:CAL-8a00048d-91324965-0192-495b3be4-0000089fdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us er/Lectures_Conferences/Lecture_Talk X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_PrattSc hool_BME,/principals/users/agrp_PrattSchool_CEE,/principals/users/agrp_Ar tsandSciences_ComputerScience,/principals/users/agrp_Institutes_Materials ScienceandEngineering,/principals/users/agrp_PrattSchool_ECE,":Biomedical Engineering (BME)\,Civil and Environmental Engineering (CEE)\,Computer S cience\,Duke Materials Initiative\,Electrical and Computer Engineering (E CE) X-BEDEWORK-SPEAKER:Peter Stone X-BEDEWORK-SUBMITTEDBY:ghester for Mechanical Engineering and Materials Sc ience (MEMS) (agrp_PrattSchool_MEMS) END:VEVENT BEGIN:VEVENT CATEGORIES:Natural Sciences CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20260114T151846Z DESCRIPTION:Addressing global security challenges requires enhanced radiat ion detection capabilities to meet the needs of organizations such as the IAEA\, CTBTO\, and the U.S. Government. Key priorities include the accur ate determination of radionuclide concentrations in environmental samples with improved speed\, sensitivity\, and adaptability to diverse sample p rofiles\, such as aged or minute samples. Advances in rare-event physics technologies\, originally developed for dark matter searches and neutrino experiments\, are proving critical in achieving the ultra-sensitive thre sholds needed for these missions. This presentation will explore the appl ication of next-generation groundwater age-dating methodologies as a case study for leveraging rare-event physics technologies. Additionally\, I w ill introduce the multidisciplinary Facility for Underground Science and Engineering (FUSE) under development at Savannah River National Laborator y\, highlighting its unique underground laboratory environment optimized for minimizing background radiation and enabling cutting-edge detection r esearch. DURATION:PT1H DTSTAMP:20260114T153211Z DTSTART;TZID=America/New_York:20260122T150000 LAST-MODIFIED:20260114T153211Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Advancing Ultra-Sensitive Radiation Detection: Applications of Rar e Event Physics to Global Security UID:CAL-8a00eca5-9af98aae-019b-bd167f74-00001d33demobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Natural Sciences:/user/publi c-user/Topics/Natural Sciences X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp__Artsan dSciences_Physics,":Physics X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=sean.finch@duke.edu:Sean Finch X-BEDEWORK-SPEAKER:Henning Back X-BEDEWORK-DUKE-WEBCAST:https://duke.zoom.us/j/94471581751?pwd=yi0iSLM7JKg LehUQsWkmuayNEi2xjI.1 X-BEDEWORK-SUBMITTEDBY:asc for TUNL Seminar Series (agrp_TUNL_SeminarSerie s) X-BEDEWORK-IMAGE-ALT-TEXT:Photo of TUNL Building X-BEDEWORK-IMAGE-X1:5 X-BEDEWORK-IMAGE-Y1:36 X-BEDEWORK-IMAGE-X2:2084 X-BEDEWORK-IMAGE-Y2:1422 X-BEDEWORK-IMAGE-CROP-WIDTH:2079 X-BEDEWORK-IMAGE-CROP-HEIGHT:1386 X-BEDEWORK-IMAGE:/public/Images/TUNL_0_20260114033211PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/TUNL_0_20260114033211PM-thumb.png END:VEVENT BEGIN:VEVENT CATEGORIES:Natural Sciences CATEGORIES:Lectures/Conferences CATEGORIES:Utilities CATEGORIES:Panel/Seminar/Colloquium CATEGORIES:Main CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None CREATED:20260126T155841Z DESCRIPTION:In the late 1950s the U.S. Atomic Energy Commission (AEC) plan ned Project Plowshare\, a series of tests designed to show the peaceful u ses for nuclear explosions. One plan was to create an artificial harbor i n Cape Thompson\, Alaska using multiple hydrogen bombs in Project Chariot . However\, nearby\, the Iñupiat residents and local scientists fought to keep their land pristine. Taking on Edward Teller and the US Government\ , this group of Alaskan citizens thwarted the project and\, in the proces s\, started a movement. This talk will discuss the project\, the results of the fight\, and the far-reaching implications of this project. DURATION:PT1H DTSTAMP:20260126T155841Z DTSTART;TZID=America/New_York:20260212T150000 LAST-MODIFIED:20260126T155841Z LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106 STATUS:CONFIRMED SUMMARY:Could nuclear physics help us understand Alzheimer’s? UID:CAL-8a00eca5-9af98aae-019b-fb075bc1-000016bademobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Panel_Seminar_Colloquium:/us er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Natural Sciences:/user/publi c-user/Topics/Natural Sciences X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp__Artsan dSciences_Physics,":Physics X-BEDEWORK-SPEAKER:Shelly R. Lesher X-BEDEWORK-DUKE-WEBCAST: https://duke.zoom.us/j/94471581751?pwd=yi0iSLM7JK gLehUQsWkmuayNEi2xjI.1 X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=sean.finch@duke.edu:Sean Finch X-BEDEWORK-IMAGE-X1:72 X-BEDEWORK-IMAGE-Y1:86 X-BEDEWORK-IMAGE-X2:967.5 X-BEDEWORK-IMAGE-Y2:683 X-BEDEWORK-IMAGE-CROP-WIDTH:895.5 X-BEDEWORK-IMAGE-CROP-HEIGHT:597 X-BEDEWORK-IMAGE-ALT-TEXT:Prof. Shelley Lesher X-BEDEWORK-SUBMITTEDBY:asc for TUNL Seminar Series (agrp_TUNL_SeminarSerie s) X-BEDEWORK-IMAGE:/public/Images/LesherPhoto-1024x683_20260126035841PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/LesherPhoto-1024x683_20260126035841P M-thumb.png END:VEVENT END:VCALENDAR