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Teaching Abstract Ideas with Concrete Applications in Mind

Duke CS Colloquium Oct 24 on Teaching Abstract Ideas with Concrete Applications in Mind with Lorenzo Luzi from Rice
Tuesday, October 24, 2023
12:00 pm - 1:00 pm
Lorenzo Luzi, Rice University
Duke Computer Science Colloquium

Lunch will be served at 11:45 AM.

This presentation will demonstrate instruction of an abstract mathematical concept in a manner which shows how embedded the concept is within concrete computer science applications. The goal of this approach is to stimulate creativity in problem-solving through the continuous connection of theoretical ideas to different forms of professional practice. The demonstration will be followed by further discussion of how theory-to-application problems can be addressed through research, mentorship and workshops. Undergraduate research in machine learning will be most successful in an environment with clear goals, focused scope of study, and ongoing faculty support. Constraining research to what can be achieved with limited resources can actually be a positive learning experience for students as they are forced to think creatively to achieve their stated research goals; however, adequate mentorship is necessary for students who are doing research for the first time. Additionally, essential skills to communicate research findings can be developed through offering targeted workshops in LaTeX, technical writing, and best practices for visual representation of data.

Lorenzo Luzi is an electrical and computer engineering PhD student at Rice University in Houston conducting research under Dr. Richard Baraniuk. As a National Science Foundation and Texas Instruments fellow, Lorenzo studies the mathematics which underlie machine learning algorithms, such as generative adversarial networks and diffusion models. This is currently his primary area of research, but more generally he is interested in using mathematics to solve practical problems as well as the development and mentorship of rising students in computing fields. Additionally, Lorenzo has collaborated since 2014 with researchers at Pacific Northwest National Laboratory on work related to signal processing and machine learning. Lorenzo received his M.S. in electrical and computer engineering from Rice University and his B.S. in electrical engineering from Washington State University.

Contact: Susan Rodger