About
Lalitha Sankar is a Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University, where she was an Associate Professor from 2018-2023 and an Assistant Professor since 2012. Prior to that she was a Science and Technology Postdoctoral Fellow at Princeton University. Her professional life also includes tenures as Senior Member of Technical Staff at AT&T Shannon Labs and Polaroid Engineering R&D.
Dr. Sankar received the B. Tech. degree from the Indian Institute of Technology, Bombay in 1992, the M.S. degree from the University of Maryland in 1994, and the Ph.D. degree from Rutgers University in 2007. Her research interests include applying information theory and data science to study reliable, responsible, robust, trustworthy, and privacy-protected machine learning. Her research also focuses on developing tools and techniques for the analysis of complex networks including the electric grid, bringing to bear her skills in signal processing, control systems, data science, and power systems.
Sankar received the National Science Foundation CAREER Award in 2014, the IEEE Globecom 2011 Best Paper Award for her work on privacy of side-information in multi-user data systems, and the Academic Excellence award from Rutgers in 2008. She was the IEEE ITSOC Distinguished Lecturer from 2020-2022. She is presently an Associate Editor for the IEEE Transactions on Information Forensics and Security and the IEEE Transactions on Information Theory and has served as Associate Editor for the BITS Magazine. She has also served as a co-lead editor of a special issue on Information-Theoretic Methods for Trustworthy and Reliable Machine Learning for the IEEE Journal on Selected Areas in Information Theory.
Sankar helped develop the Data Science PhD and MS (with concentration) programs at ASU and is a member of the Graduate Program Committee for the Data Science, Engineering and Analytics Program at ASU. She was the EE representative for the office of the Vice Dean of Research Initiatives at ASU (2021-2023). She helped develop the annual ECEE Graduate Research Day and is the chair of the ECEE Graduate Awards Committee.
Teaching
Graduate Courses:
- EEE 549 –Statistical Machine Learning: From Theory to Practice
- EEE 551 –Information Theory
- EEE 598 –Smart Grid Operations, Security and Grid Analytics
- EEE 554 –Probability and Random Processes
- EEE 498/591 –Foundations of Machine Learning: From Theory to Practice
Undergraduate Courses:
- EEE 350 –Random Signal Analysis
- EEE 394 –Statistical Machine Learning for Engineers
- EEE 202 –Circuits