- Ph.D., Electrical Engineering, University of California, Berkeley, Dec. 1992
Elyse Rosenbaum is the Melvin and Anne Louise Hassebrock Professor in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She received the Ph.D. degree in electrical engineering from University of California, Berkeley. She is the director of the Center for Advanced Electronics through Machine Learning (CAEML), a joint project of the University of Illinois, Georgia Tech and North Carolina State University. CAEML is an NSF Industry/University Cooperative Research Center (I/UCRC).
Dr. Rosenbaum’s present research interests include component and system-level ESD reliability, ESD-robust high-speed I/O circuit design, compact modeling, mitigation strategies for ESD-induced soft failures, and machine-learning aided behavioral modeling of microelectronic components and systems.
Dr. Rosenbaum has authored or co-authored nearly 200 technical papers. She has presented tutorials on reliability physics at the International Reliability Physics Symposium, the EOS/ESD Symposium, and the RFIC Symposium, and she has given invited lectures at many universities and industrial laboratories; recently she was the keynote lecturer for DesignCon 2017. From 2001 through 2011, she was an editor for IEEE Transactions on Device and Materials Reliability. She is currently an editor for IEEE Transactions on Electron Devices. Dr. Rosenbaum was the General Chair for the 2018 IEEE International Reliabiity Physics Symposium.
Dr. Rosenbaum has been a visiting professor at Katholieke Universiteit in Leuven, Belgium and National Chiao-Tung University in Hsinchu, Taiwan. She has been the recipient of a Best Student Paper Award from the IEDM, Outstanding and Best Paper Awards from the EOS/ESD Symposium, a Technical Excellence Award from the SRC, an NSF CAREER award, an IBM Faculty Award, and the ESD Association’s Industry Pioneer Recognition Award. She is a Fellow of the IEEE.
Other Professional Activities
- Director, Center for Advanced Electronics through Machine Learning. Aug. 2016 - present.
Graduate Research Opportunities
Research opportunities exist for students with interests in device physics, circuit design and electromagnetics. Students must have good communication skills and an electrical engineering background.
Undergraduate Research Opportunities
Students who have completed some or all of ECE 441, 482 and 483 are potentially able to assist with some of our research. There may also be opportunities for students who have studied machine learning or statistical learning theory. Juniors and seniors only.
- Electronics, Plasmonics, and Photonics
- Machine learning
- Semiconductor devices and manufacturing
- Industry Pioneer Recognition Award, Electrostatic Discharge Association, Sept. 2016.
- Melvin and Anne Louise Hassebrock Professor in Electrical and Computer Engineering, Aug. 2016.
- IEEE Fellow for "contributions to electrostatic discharge reliability of integrated circuits," 2011
- ECE 342 - Electronic Circuits
- ECE 441 - Physcs & Modeling Semicond Dev
- ECE 482 - Digital IC Design
- ECE 585 - MOS Device Modeling & Design