Faculty
Bio: Andrew J. Mason received the BS in Physics from Western Kentucky University in 1991, the BSEE from the Georgia Institute of Technology in 1992, and the MS and Ph.D. in Electrical Engineering from The University of Michigan, Ann Arbor in 1994 and 2000, respectively. After starting his academic career at the University of Kentucky, since 2001 Dr. Mason has been with the Department of Electrical and Computer Engineering at Michigan State University in East Lansing, Michigan, where he is currently a Professor and a member of the Neuroscience Program and the Environmental Science and Policy Program. His research explores technologies for augmented human awareness and biomedical applications, including microfabricated structures, mixed-signal and embedded circuits, and machine learning algorithms, and his teaching focuses on embedded smart systems and biomedical instrumentation.
Google Scholar Page: https://scholar.google.com/citations?user=4YUNXeQAAAAJ&hl=en
Webpage: https://www.egr.msu.edu/~mason/
Bio: I am an Assistant Professor with the Electrical and Computer Engineering Department at Michigan State University. Prior to joining Michigan State University, I was a Postdoctoral Research Associate at the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, working with Professor Tamer Başar. I received the Ph.D. degree in Electrical Engineering from University of Texas at Arlington in 2017, supervised by Professor Frank Lewis. My research interests lie at the intersection of machine learning, optimization and control.
Bio: Guoming Zhu received the B.S. and M.S. degrees from Beijing University of Aeronautics and Astronautics (currently Beihang University), Beijing, China, in 1982 and 1984, respectively, and the Ph.D. degree in aerospace engineering from Purdue University, West Lafayette, IN, USA, in 1992. He is currently a Gardner Endowed Chair and Professor with the Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA. He was a Technical Fellow in advanced powertrain systems with Visteon Corporation, and a Technical Advisor with Cummins Engine Co., Ltd. He has over 40 years of experience related to control theory, engine diagnostics/control, and vibration control. He has authored or co-authored over 280 refereed technical papers and two books, and he holds more than 40 U.S. patents. His current research interests include powertrain system modeling, identification, and closed-loop control, autonomous and connected vehicle control and optimization, urban air mobility, and vibration suppression of aero-structural systems. Dr. Zhu is a Fellow of SAE and ASME. He is an Associate Editor of ASME Dynamic Systems and Control Letter and an Editorial Board Member of the International Journal of Powertrain. He was the Program Chair of the 2018 ASME Dynamic Systems and Control Conference.
Google Scholar Page: https://scholar.google.com/citations?user=iZO10LIAAAAJ&hl=en&oi=ao
Webpage: www.egr.msu.edu/zhug
Bio: Hamidreza Modares received a B.S. degree from the University of Tehran, Tehran, Iran, in 2004, an M.S. degree from the Shahrood University of Technology, Shahrood, Iran, in 2006, and the Ph.D. degree from the University of Texas at Arlington, Arlington, TX, USA, in 2015, all in Electrical Engineering. He is currently an Assistant Professor in the Department of Mechanical Engineering at Michigan State University. Prior to joining Michigan State University, he was an Assistant professor in the Department of Electrical Engineering at Missouri University of Science and Technology. His current research interests include reinforcement learning, safe control, machine learning in control, distributed control of multi-agent systems, and robotics. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Systems, Man, and Cybernetics: systems.
Google Scholar Page: https://scholar.google.com/citations?user=xhucCdUAAAAJ
Bio: Rajiv Ranganathan is an associate professor in the Department of Kinesiology. His research interests are in the area of motor learning and bio-mechanics. He is particularly interested in how humans produce skilled and coordinated movement, and how this ability is altered in the context of development, aging, and movement disorders. He uses a combination of both experimental techniques – such as motion capture, robotics and virtual reality – as well as biomechanical modeling and computer simulations to understand the mechanisms underlying the control of human movement. The overarching goal of his research program is to develop novel training paradigms to facilitate motor skill learning and the rehabilitation of movement disorders.
Google Scholar Page: https://scholar.google.com/citations?user=gOOIBfIAAAAJ&hl=en
Bio: Shaunak D. Bopardikar is an Assistant Professor with the Electrical and Computer Engineering Department, and is affiliated with the Center for Connected Autonomous Networked Vehicles for Active Safety (CANVAS) at the Michigan State University. His research interests lie in autonomous motion planning and control, in cyber-physical security and in scalable computation and optimization. He received the Bachelor of Technology (B.Tech.) and Master of Technology (M.Tech.) degrees in Mechanical Engineering from the Indian Institute of Technology, Bombay, India, in 2004, and the Ph.D. degree in Mechanical Engineering from the University of California at Santa Barbara, USA, in 2010. From 2004 to 2005, he was an Engineer with General Electric India Technology Center, Bangalore, India. From 2011 to 2018, he was a Staff Research Scientist with the Controls group of United Technologies Research Center (UTRC) at East Hartford, CT, USA and at Berkeley, CA. Prior to joining UTRC, Dr. Bopardikar worked as a post-doctoral associate at UC Santa Barbara (2010-2011) during which he developed randomized algorithms for solving large matrix games. He is a senior member of the IEEE, has over 70 refereed journal and conference publications and is a co-inventor on 2 U.S. patents. His recognitions include an Air Force Research Laboratory Summer Faculty Fellowship, a National Science Foundation Career Award and an IEEE Technical Committee on Security and Privacy's Best Student Paper Award (as advisor).
Google Scholar Page: https://scholar.google.com/citations?user=-A_CtWMAAAAJ&hl=en
Bio: Sijia Liu received the Ph.D. degree (with All-University Doctoral Prize) in Electrical and Computer Engineering from Syracuse University, NY, USA, in 2016. He was a Postdoctoral Research Fellow at the University of Michigan, Ann Arbor, in 2016-2017, and a Research Staff Member at the MIT-IBM Watson AI Lab in 2018-2020. His research focuses include scalable and trustworthy machine learning, optimization theory and methods, computer vision, and computational biology. He received the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’16), and the Best Paper Runner-Up Award at the 38th Conference on Uncertainty in Artificial Intelligence (UAI’22). He has published over 60 papers at top-tier ML/CV conferences. He is currently a Senior Member of IEEE, a Technical Committee (TC) Member of Machine Learning for Signal Processing (MLSP) in the IEEE’s Signal Processing Society, and an affiliated faculty at the MIT-IBM Watson AI Lab, IBM Research. He has organized a series of Adversarial ML workshops in KDD’19-’22 and ICML’22-23, and provided tutorials on Trustworthy and Scalable ML in CVPR’20, NeurIPS’22, AAAI'23, and CVPR'23.
Google Scholar page: https://scholar.google.com/citations?user=C7dO_UgAAAAJ&hl=en
Webpage: https://lsjxjtu.github.io/index.html
Bio: Dr. Tong (Tony) Gao is an Associate Professor at the Department of Mechanical Engineering and Department of Computational Mathematics, Science, and Engineering at Michigan State University, where he directs the Complex Fluids Group. He obtained his Ph.D. degree in Mechanical Engineering at the University of Pennsylvania in 2012. Then he worked as a research scientist in the Applied Mathematics Lab at the Courant Institute of Mathematical Sciences of New York University. Dr. Gao works in the interdisciplinary areas of soft condensed matter, fluid mechanics, and materials via mathematical modeling and high-performance computing. His expertise lies in constructing advanced computational mechanics models for fluid-solid systems with high complexities (e.g., many-body interactions) and nonlinearity (e.g., fluid/elastic structure interactions), and developing scalable simulation tools to promote data-driven, physics-informed studies. Dr. Gao received the NSF CAREER award in 2020. The current focused research topics include bioactive matter, soft robotics, and patient-specific medical models.
Webpage: https://www.egr.msu.edu/~gaotong/
Bio: Vaibhav Srivastava received the B.Tech. degree (2007) in mechanical engineering from the Indian Institute of Technology Bombay, Mumbai, India; the M.S. degree in mechanical engineering (2011), the M.A. degree in statistics (2012), and the Ph.D. degree in mechanical engineering (2012) from the University of California at Santa Barbara, Santa Barbara, CA.
Dr. Srivastava is currently an Associate Professor of Electrical and Computer Engineering at Michigan State University. He is also affiliated with Mechanical Engineering, Cognitive Science Program, and Connected and Autonomous Networked Vehicles for Active Safety (CANVAS). He served as a Lecturer and Associate Research Scholar with the Mechanical and Aerospace Engineering Department at Princeton University, Princeton, NJ from 2013-2016. He serves on the IEEE Control System Society conference editorial board since 2018. He received the best paper award (as coauthor) at the 2014 European Control Conference. His research focuses on Cyber Physical Human Systems with an emphasis on mixed human-robot systems, networked multi-agent systems, aerial robotics, and connected and autonomous vehicles.
Google Scholar Page: https://scholar.google.com/citations?user=L_K_w18AAAAJ&hl=en
Webpage: https://www.egr.msu.edu/~vaibhav/
Bio: Dr. Tan received his Bachelor's and Master's degrees in automatic control from Tsinghua University, Beijing, China, in 1995, 1998, respectively, and his Ph.D. in electrical and computer engineering from the University of Maryland in 2002. From September 2002 to July 2004, he was a Research Associate with the Institute for Systems Research (ISR) at the University of Maryland. In August 2004 he joined the Department of Electrical and Computer Engineering at Michigan State University, where he is currently an MSU Foundation Professor and the Richard M. Hong Endowed Chair. His research interests include underwater robotics, soft robotics, smart materials, and control systems. Dr. Tan's research has been supported by National Science Foundation, Office of Naval Research, National Institutes of Health, U.S. Geological Survey, the Great Lakes Fishery Commission, Toyota, Naval Research Lab, U.S. Department of Transportation, and MSU Foundation, among others.
Dr. Tan is a Fellow of IEEE and ASME. He received an NSF CAREER Award in 2006, MSU Teacher-Scholar Award in 2010, Withrow Distinguished Scholar Award (senior category) from MSU College of Engineering in 2018, and Distinguished Alumni Award from Department of Electrical and Computer Engineering at University of Maryland in 2018. He has received several Best Paper Awards. He has served as a Senior Editor for IEEE/ASME Transactions on Mechatronics (TMECH), an Associate Editor/Technical Editor for TMECH, Automatica, and International Journal of Advanced Robotic Systems, and a guest editor for six journal special issues or sections. Dr. Tan has served on the organizing or program committees for a number of international conferences, including serving as the Program Chair for the 15th International Conference on Advanced Robotics (ICAR'2011), General Chair for the 2018 ASME Dynamic Systems and Control Conference (DSCC'2018), Program Chair for the 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM'2020), and General Chair for 2023 American Control Conference (ACC'2023). Dr. Tan is also keen to integrate his research with educational and outreach activities, including serving as Director of an NSF-funded Research Experiences for Teachers (RET) Site program at MSU from 2009 – 2016 and Curator of a robotic fish exhibit at MSU Museum from April 2016 to January 2017.
Google Scholar Page: https://scholar.google.com/citations?user=tsJSW2MAAAAJ&hl=en
Webpage: https://www.egr.msu.edu/~xbtan/
Bio: Dr. Zhaojian Li is an Assistant Professor in the Department of Mechanical Engineering at Michigan State University. He obtained M.S. (2013) and Ph.D. (2015) in Aerospace Engineering (flight dynamics and control) at the University of Michigan, Ann Arbor. As an undergraduate, Dr. Li studied at Nanjing University of Aeronautics and Astronautics, Department of Civil Aviation, in China. Dr. Li worked as an algorithm engineer at General Motors from January 2016 to July 2017. His research interests lie in the intersection between control theory and machine learning, with applications to intelligent vehicles and robotics. His research has been funded by National Science Foundation, National Institute of Health, US Department of Agriculture, Army, Office of Naval Research, Ford, DENSOR, T-Mobile, among others. He is a senior member of IEEE and a recipient of the NSF CAREER award.
Google Scholar Page: https://scholar.google.com/citations?user=3TUhNa0AAAAJ&hl=en
Webpage: https://www.egr.msu.edu/rival/