Dr. Chesney has more than 20 years’ experience working in the automotive industry as a product and process engineer and has worked at the University of Michigan as a full-time faculty member for over 15 years.
Dr. Chesney has won multiple teaching awards and was involved in the formation of a multidisciplinary Design Minor in the College of Engineering. He was also involved in the formation of an internal center to create hardware and software for children with cognitive and physical disabilities through a collaboration between the College of Engineering and CS Mott Children’s Hospital.
Euisik Yoon received the B.S. and M.S. degrees in electronics engineering from Seoul National University in 1982 and 1984, respectively, and the Ph.D. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1990.
From 1990 to 1994 he worked for the Fairchild Research Center of the National Semiconductor Corp. in Santa Clara, CA, where he engaged in researching deep submicron CMOS integration and advanced gate dielectrics. From 1994 to 1996 he was a Member of the Technical Staff at Silicon Graphics Inc. in Mountain View, CA, where he worked on the design of the MIPS microprocessor R4300i and the RCP 3-D graphic coprocessor. He took faculty positions in the Department of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST) in Daejon, Korea (1996-2005) and in the Department of Electrical and Computer Engineering at the University of Minnesota, Minneapolis, MN (2005-2008), respectively. During the academic year of 2000-2001, he was a Visiting Faculty at Agilent Laboratory, Palo Alto, CA. In 2008, he joined the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, MI, where he is a Professor and the Director of the Solid-State Electronics Laboratory and the Director of Lurie Nanofabrication Facility. His research interests are in MEMS, integrated microsystems, and VLSI circuit design.
Dr. Yoon was the co-recipient of the Student Paper Award at the IEEE International Microwave Symposium in 1999 and 2000, respectively. He has served on various Technical Program Committees including the Microprocesses and Nanotechnology Conference (1998), the International Sensor Conference (2001), the IEEE Asia-Pacific Conference on Advanced System Integrated Circuits (2001-2002), the International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) (2003, 2005), the IEEE International Electron Device Meeting (2006-2008) and the IEEE International Conference on Micro Electro Mechanical Systems (2006, 2009-2010). He also served on the IEEE International Solid-State Circuit Conference program committee (2003-2007) and was a general chair of International Symposium on Bio Micro & Nanosystems (2005).
Reetuparna Das is an assistant professor in the Computer Science and Engineering department at the University of Michigan and a member of the Advanced Computer Architecture Lab (ACAL). Prior to this she was the researcher-in-residence for Center for Future Architectures Research (C-FAR) at Michigan and also a research scientist at Intel Labs in Santa Clara. She has a Ph.D. in Computer Science and Engineering from the Pennsylvania State University, University Park. Her research interests include computer architecture and its interaction with software systems and device/VLSI technologies. Some of her recent projects include energy proportional interconnect architectures, fine-grain heterogeneous core architectures for mobile systems, and low-power scalable interconnects for kilo-core processors. Her thesis research focused on application-aware on-chip interconnects. She has received an IEEE Top Picks award, outstanding research and teaching assistantship awards from the Computer Science and Engineering department at Pennsylvania State University. She has authored over 40 articles in peer reviewed journals and conferences, and filed 5 patents through ARM Inc. in last two years.
Yogesh B. Gianchandani is a Professor at the University of Michigan, Ann Arbor, with a primary appointment in the Electrical Engineering and Computer Science Department and a courtesy appointment in the Mechanical Engineering Department. He also serves as the Director for the Center for Wireless Integrated MicroSensing and Systems (WIMS2).
Dr. Gianchandani’s research interests include all aspects of design, fabrication, and packaging of micromachined sensors and actuators. He has published 300 papers in journals and conferences, and has about 35 US patents issued or pending. He was a Chief Co-Editor of Comprehensive Microsystems: Fundamentals, Technology, and Applications, published in 2008. From 2007 to 2009 he also served at the National Science Foundation, as the program director for Micro and Nano Systems within the Electrical, Communication, and Cyber Systems Division (ECCS). Dr. Gianchandani is a Fellow of IEEE.
H. V. Jagadish is Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor. He is a co-founder and Distinguished Scientist at the Michigan Institute for Data Science. He is also a founding member of the Center for Computational Medicine and Biomedical Informatics at the University of Michigan, and a member of its Executive Committee.
After earning his PhD from Stanford in 1985, he spent over a decade at AT&T Bell Laboratories in Murray Hill, N.J., eventually becoming head of AT&T Labs database research department at the Shannon Laboratory in Florham Park, N.J. He has also served as a Professor at the University of Illinois in Urbana-Champaign and as the Shaw Visiting Professor at the National University of Singapore.
Professor Jagadish is well-known for his broad-ranging research on information management, and has over 190 major papers and 37patents. In particular, he is a leader in the integration of biomedical data from multiple sources, its analysis, and its presentation to practitioners.
Professor Jagadish is a fellow of the ACM ("The First Society in Computing") and serves on the Board of the Computing Research Association (since 2009). Among many professional positions he has held, he has previously been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), and a trustee of the VLDB (Very Large DataBase) Foundation. He is the founding Editor-in-Chief of the Proceedings of the VLDB Endowment (since 2008). He served as Senior Scientific Director of the NIH National Center for Integrative Biomedical Informatics.
Honglak Lee is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. He received his Ph.D. from Computer Science Department at Stanford University in 2010, advised by Andrew Ng. His primary research interests lie in machine learning, which spans over deep learning, unsupervised and semi-supervised learning, transfer learning, graphical models, and optimization. He also works on application problems in computer vision, audio recognition, robot perception, computational healthcare, and text processing. His work received best paper awards at ICML and CEAS. He received a Google Faculty Research Award, and he has served as a guest editor of IEEE TPAMI Special Issue on Learning Deep Architectures. He was selected as one of AI's 10 to Watch by IEEE Intelligent Systems in 2013.
Jenna Wiens is an Assistant Professor in Computer Science and Engineering (CSE) at the University of Michigan, Ann Arbor. In the fall of 2014, she joined the CSE division after completing her PhD at MIT in June 2014. Professor Wiens's primary research interests lie at the intersection of machine learning and medicine. The proliferation of electronic medical records holds out the promise of using machine learning and data mining to build models that will help health care providers improve patient outcomes. Wiens’s research focuses on developing such methods to organize, process and transform these data into actionable knowledge. Broadly, her work borrows from and improves upon techniques in time-series analysis, transfer learning, multitask learning, active learning and other supervised and unsupervised learning techniques.