Data Analytics and Data-Driven Computing: Applications from Internet-of-Things to Classifying Neuropsychiatric Disorders

Thursday, February 15, 2018
EER 0.824/EER 0.825

This talk will describe several approaches to reducing energy consumption in internet-of-things applications and applications of data analytics to neuro-psychiatric disorders. Machine learning and information analytics are important components in all these things. Almost all things should have embedded classifiers to make decisions on data. Thus, reducing energy consumption of features and classifiers is important. First part of the talk will present energy reduction approaches from feature selection, classification and incremental multi-stage classification perspectives. These approaches will be demonstrated using a medical internet-of-thing for monitoring EEG and predicting seizures.  Enhancing security and preventing piracy are also of critical concern.  In the second part of the talk, I will address hardware security and present approaches to designing circuits that cannot be easily reverse engineered and cannot be pirated. To this end, authentication and obfuscation approaches will be presented. In the third part of the talk, data analytics approaches to classifying psychiatric disorders such as schizophrenia, border line personality disorder and obsessive-compulsive disorder will be discussed.


University of Minnesota, Minneapolis

Keshab K. Parhi received the B.Tech. degree from the Indian Institute of Technology (IIT), Kharagpur, in 1982, the M.S.E.E. degree from the University of Pennsylvania, Philadelphia, in 1984, and the Ph.D. degree from the University of California, Berkeley, in 1988. He has been with the University of Minnesota, Minneapolis, since 1988, where he is currently Distinguished McKnight University Professor and Edgar F. Johnson Professor in the Department of Electrical and Computer Engineering. He has published over 600 papers, is the inventor of 29 patents, and has authored the textbook VLSI Digital Signal Processing Systems (Wiley, 1999) and coedited the reference book Digital Signal Processing for Multimedia Systems (Marcel Dekker, 1999). Dr. Parhi is widely recognized for his work on high-level transformations of iterative data-flow computations, for developing a formal theory of computing for design of digital signal processing systems, and for his contributions to multi-gigabit Ethernet systems on copper and fiber and for backplanes. His current research addresses VLSI architecture design of signal processing systems, hardware security, data-driven neuroscience and molecular computing. 

Dr. Parhi is the recipient of numerous awards including the 2017 Mac Van Valkenburg award and the 2012 Charles A. Desoer Technical Achievement award from the IEEE Circuits and Systems Society, the 2004 F. E. Terman award from the American Society of Engineering Education, the 2003 IEEE Kiyo Tomiyasu Technical Field Award, the 2001 IEEE W. R. G. Baker prize paper award, and a Golden Jubilee medal from the IEEE Circuits and Systems Society in 2000. He was elected a Fellow of IEEE in 1996 and a Fellow of the AAAS in 2017. He served as the Editor-in-Chief of the IEEE Trans. Circuits and Systems, Part I during 2004-2005 and as an elected member of the Board of Governors of the IEEE Circuits and Systems society from 2005 to 2007.