Mohamed Ibrahim was a Visiting Scholar with the Technical University of Munich, Germany, and the University of Bremen, Germany. He spent a total of three years as a Research and Development Engineer in the semiconductor industry where he worked on design-for-test and post-silicon validation methodologies for several system-on-chip (SoC) designs. His current research interests include SoC design and embedded systems, electronic design automation of LOC systems, Internet-of-Bio-Things, security and trust of bio-systems, and machine-learning applications of bio-systems. Dr. Ibrahim was a recipient of the Best Paper award at the 2017 IEEE/ACM Design, Automation, and Test in Europe Conference, the 2017 Postdoc Mobility award from the Technical University of Munich, Germany, two ACM conference travel awards from ACM-SIGBED in 2016 and ACM-SIGDA in 2017, and Duke Graduate School Fellowship in 2013.
Krishnendu Chakrabarty is the William H. Younger Distinguished Professor and Department Chair of Electrical and Computer Engineering, and Professor of Computer Science, at Duke University. He is a recipient of the National Science Foundation CAREER award, the Office of Naval Research Young Investigator award, the Humboldt Research Award from the Alexander von Humboldt Foundation, Germany, the IEEE Transactions on CAD Donald O. Pederson Best Paper Award (2015), the ACM Transactions on Design Automation of Electronic Systems Best Paper Award (2017), and over a dozen best paper awards at major conferences. He is also a recipient of the IEEE Computer Society Technical Achievement Award (2015), the IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award (2017), the Semiconductor Research Corporation Technical Excellence Award (2018), and the Distinguished Alumnus Award from the Indian Institute of Technology, Kharagpur (2014). Prof. Chakrabarty's current research projects include: testing and design-for-testability of integrated circuits and systems; digital microfluidics, biochips, and cyberphysical systems; data analytics for fault diagnosis, failure prediction, anomaly detection, and hardware security; neuromorphic computing systems.
