ECE Distinguished Speaker Series: Somesh Jha - Trustworthy Machine Learning and the Security Mindset

Abstract Fueled by massive amounts of data, models produced by
machine-learning (ML) algorithms, especially deep neural networks
(DNNs), are being used in diverse domains where trustworthiness is a
concern, including automotive systems, finance, healthcare, natural
language processing, and malware detection. Of particular concern is
the use of ML algorithms in cyber-physical systems (CPS), such as
self-driving cars and aviation, where an adversary can cause serious
consequences. Interest in this area of research has simply
exploded. In this work, we will emphasize the need for a security
mindset in trustworthy machine learning, and then cover some lessons
learned.

Short Bio Somesh Jha received his B.Tech from Indian Institute of
Technology, New Delhi in Electrical Engineering. He received his
Ph.D. in Computer Science from Carnegie Mellon University under the
supervision of Prof. Edmund Clarke (a Turing award winner). Currently,
Somesh Jha is the Lubar Professor in the Computer Sciences Department
at the University of Wisconsin (Madison). His work focuses on analysis
of security protocols, survivability analysis, intrusion detection,
formal methods for security, and analyzing malicious code. Recently,
he has focussed his interested on privacy and adversarial ML
(AML). Somesh Jha has published several articles in highly-refereed
conferences and prominent journals. He has won numerous best-paper and
distinguished-paper awards. Prof. Jha is the fellow of the ACM and
IEEE.