Signal Processing in Medicine and Biology: Innovations in Big Data Processing

Joseph Picone, Ph.D., is a professor of Electrical and Computer Engineering at Temple University, where he directs the Institute for Signal and Information Processing and is the Associate Director of the Neural Engineering Data Consortium. His primary expertise is in statistical modeling with applications in signal processing, specifically acoustic modeling in speech recognition. A common theme throughout his research career has been a focus on fundamentally new statistical modeling paradigms. He has been an active researcher in various aspects of speech processing for over 35 years. He currently collaborates with the Temple School of Medicine and has previously collaborated with many academic institutions (e.g., the Linguistic Data Consortium, Johns Hopkins University), government agencies (e.g., Department of Defense, DARPA), and companies (e.g., MITRE, Texas Instruments). The National Science Foundation, DoD, DARPA, and several commercial interests have funded his research. He has published over 200 technical papers and holds eight patents.

Ivan Selesnick, Ph.D., is a professorof Electrical and Computer Engineering at the New York University Tandon School of Engineering. He received the BS, MEE, and Ph.D. degrees in Electrical Engineering from Rice University, and joined Polytechnic University in 1997 (now NYU Tandon School of Engineering). He received an Alexander von Humboldt Fellowship in 1997 and a National Science Foundation Career award in 1999. In 2003, he received the Jacobs Excellence in Education Award from Polytechnic University. Dr. Selesnick’s research interests are in signal and image processing, wavelet-based signal processing, sparsity techniques, and biomedical signal processing. He became an IEEE Fellow in 2016 and has been an associate editor for the IEEE Transactions on Image Processing, IEEE Signal Processing Letters, IEEE Transactions on Signal Processing, and IEEE Transactions on Computational Imaging.