Machine Learning Applications in Medicine and Biology

Ammar Ahmed, Ph.D., is a radar signal processing engineer at Aptiv, Agoura Hills, CA. He earned his Ph.D. in Electrical Engineering from Temple University under the supervision of Dr. Daniel Zhang, Dr. Dennis Silage, and Dr. Joseph Picone. Dr. Ahmed received a B.Sc. degree in Electrical Engineering from the University of Engineering & Technology, Lahore, Pakistan, in 2009 and an M.S. degree in Systems Engineering from the Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan, in 2011. From 2011 to 2016, he served as an electrical engineer for the National Tokamak Fusion Program at the Pakistan Atomic Energy Commission, where he was responsible for developing an embedded system design of spherical tokamaks for electricity generation. He spent the summer of 2020 (May-August) as an intern at Qualcomm Technologies, Inc., San Diego. His research interests are in signal processing, optimization, and radar systems.

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 statistical modeling with signal processing applications, specifically acoustic modeling in speech recognition. A common theme throughout his research career has focused 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. He 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.

Introduction.- Signal and Image Analysis (EEG, ECG, MRI).- Machine Learning.- Data Mining and Classification.- Big Data.- Index.