Radu Marculescu is a professor and the Laura Jennings Turner Chair in Engineering in the ECE Department at University of Texas at Austin. He received his Ph.D. from Univ. of Southern California in 1998 and has been on the ECE faculty at Carnegie Mellon University between 2000- 2019. He has co-authored many papers in a range of topics covering machine learning and systems design, low-power design and optimization, manycore systems-on-chip, networks-on- chip, embedded and cyber-physical systems, edge computing. His work on networks-on-chip design and optimization is widely recognized, most recently with the 2019 IEEE Computer Society 2019 Edward J. McCluskey Technical Achievement Award. His current research projects include machine learning and optimization for manycore systems design, AI approaches for HW/SW co- design, adversarial activity in social networks, and distributed learning approaches for edge devices.

Diana Marculescu is a professor and Motorola Regents Chair in Electrical and Computer Engineering at the University of Texas at Austin. Before joining UT Austin in December 2019 as department chair, she was at Carnegie Mellon University (2000-2019). She received the Dipl.Ing. degree in computer science from the Polytechnic University of Bucharest, Bucharest, Romania (1991), and the Ph.D. degree in computer engineering from the University of Southern California, Los Angeles, CA (1998). Her research interests include energy- and reliability-aware computing, hardware aware machine learning, and computing for sustainability and natural science applications. Diana received multiple best papers awards and national or international recognitions for her research contributions. She is a Fellow of ACM and IEEE.

Umit Y. Ogras received his Ph.D. degree in ECE from Carnegie Mellon University in 2007. From 2008 to 2013, he worked as a Research Scientist at the Intel Strategic CAD Labs (SCL). He is currently an Associate Professor at Arizona State University. Dr. Ogras has received 2018 DARPA Young Faculty Award, 2017 NSF CAREER Award, Intel SCL Research Award, and best paper awards at 2019 CASES, 2017 CODES-ISSS, 2012 IEEE Trans. on CAD, 2011 IEEE VLSI Transactions. His research interests include energy-efficient embedded systems, wearable internet-of-things, flexible hybrid electronics (FHE), and multicore architectures.

Kartikeya Bhardwaj is a Senior Machine Learning Engineer in the Machine Learning Technology Group at Arm Inc., San Jose, California, US. He received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2019. His research interests include deep learning model compression, neural architecture search, and network science.

Dimitrios Stamoulis is a Senior Researcher at Microsoft. He received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2020. His work focuses on neural architecture search techniques for designing custom deep learning models for challenging computer vision applications.