Organizers

Saiprasad Ravishankar is an Assistant Professor in the Departments of Computational Mathematics, Science and Engineering, and Biomedical Engineering at Michigan State University (MSU). He directs the Signals, Learning, and Imaging (SLIM) Group at MSU. He received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, India, in 2008, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering in 2010 and 2014, respectively, from the University of Illinois at Urbana-Champaign, where he was then an Adjunct Lecturer and a Postdoctoral Research Associate. From August 2015 to 2018, he was a postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, and then a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019. His interests include biomedical and computational imaging, machine learning, inverse problems, signal and image processing, data analysis, neuroscience, and optimization. He is currently a member of the IEEE MLSP Technical Committee and has served as Guest Editor for the IEEE Signal Processing Magazine. He has organized numerous special sessions and workshops on imaging and machine learning themes including at the Institute for Mathematics and its Applications (IMA), International Conference on Computer Vision (ICCV), IEEE ICASSP, CAMSAP, ISBI, etc.

Mengsen Zhang is an Assistant Professor in the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University (MSU). She focuses on developing a computational framework for analyzing and modeling multiscale systems and connecting computational modeling to human and animal naturalistic recordings and experiments using brain stimulation. She earned a Ph.D. in Complex Systems and Brain Sciences at Florida Atlantic University under Drs. Emmanuelle Tognoli and J. A. Scott Kelso. After Ph.D., she moved to Stanford University (Brain Dynamics Lab, led by Dr. Manish Saggar) to further develop her expertise in nonlinear dynamic modeling and computational geometry/topology, applied to human neuroimaging data. After Stanford, she worked in the Frohlich Lab, led by Dr. Flavio Frohlich, where she developed computational tools to connect the scales of observation accessible in animal (ferrets!) and human research.

Barry H. Cohen is a research affiliate in the New York University (NYU) Steinhardt school, and, along with Joshua Aronson, he directs the Mindful Education Lab. Recently, he retired as a clinical associate professor, and had been director of NYU's GSAS M.A. program in psychology for more than a decade. He has taught statistics and research design at the graduate level for more than 25 years, and is the author of three statistics text books currently in print. He received a B.S. in physics from Stony Brook University, an M.A. in general psychology from Queens College, and a Ph.D. in experimental psychology from NYU. He completed two years of post-doctoral research under the guidance of Richard Davidson at Purchase College in New York. His current research is focused in two main areas: the subjective and neural correlates of inner speech; and the cognitive, affective, and physiological changes produced by the regular practice of meditation and related mental exercises. 

Mark Reimers is an associate professor in the neuroscience program at Michigan State University, where he integrates statistical analysis with biological theory while analyzing and interpreting the very large data sets now being generated in neuroscience, especially from the high-throughput technologies developed by the BRAIN initiative. He graduated from the University of Toronto and the University of British Columbia and previously held appointments at Virginia Commonwealth University.