2023 DAC
Early Career Workshop
Co-located with the 60th Design Automation Conference (DAC)
An In-Person Experience, 9:00 AM - 5:00 PM (PST)
July 9th, 2023
Location 3016, Level 3 | Moscone West, San Francisco
Abstract
The DAC Early Career Workshop is a long-tradition forum for young and mid-career faculty and professionals in the fields related to electronic design automation (EDA) and any domain at its intersection. This workshop provides an opportunity for attendees to learn from successful people on diverse topics, such as getting research grants, establishing an impactful research group, building strong collaboration with industry and academic research, growing career both academically and professionally, and developing valuable soft skills towards a successful career.
This workshop will provide valuable suggestions for early-career faculty and professionals through short talks and panel discussions. During the session, the attendees can closely interact and network with some of the established academicians, professionals, and program officers of funding agencies in EDA-related fields and beyond.
Registration
Registration for the workshop itself is FREE ( with 'I Love DAC'). If you're attending, please also sign up through our Google Form for receiving updates!
Invited Speakers
Organizers
Wenchao Li (Boston University)
Wenchao Li is an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University with affiliate appointments in Computer Science and Systems Engineering. Prior to joining BU, he was a Computer Scientist at SRI International, Menlo Park. He received his Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2013. His research sits at the intersection of formal methods and machine learning, with a focus on building safe and trustworthy autonomous systems. He chairs the TPC subcommittee on Autonomous Systems at DAC 2023 and is currently an Associate Editor of TCAD. His research has been supported by NSF, DARPA, ONR, IARPA, Toyota, Intuit, etc.
Arman Roohi (University of Nebraska-Lincoln)
Arman Roohi is currently an assistant professor with the School of Computing, University of Nebraska-Lincoln, USA, and the director of the Intelligent Device-2–Applications Laboratory (iDEA-Lab). Before joining UNL in 2020, he was a postdoctoral research fellow at the University of Texas at Austin. He received his Ph.D. in Computer Engineering at the University of Central Florida, Orlando, FL, USA, in 2019. His research interests span the areas of cross-layer co-design for implementing complex machine learning tasks and secure computation, including hardware security and the security of artificial intelligence, reconfigurable and adaptive computer architectures, and beyond CMOS computing. He has completed over 70 publications on these topics, including best paper recognition, book chapters, and STEM curricular development. He received Ph.D. Forum at DAC 2018 Scholarship, Frank Hubbard Engineering Endowed Scholarship in 2018, best paper recognition in IEEE Transactions on Emerging Topics in Computing in 2019, and paper of the month at IEEE Transactions on Computers in 2017.
Bei Yu (The Chinese University of Hong Kong)
Bei Yu is currently an Associate Professor in the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He has served as TPC Chair of ACM/IEEE Workshop on Machine Learning for CAD, and in many journal editorial boards and conference committees. He is Editor of the IEEE TCCPS Newsletter. He received ten Best Paper Awards from IEEE TSM 2022, DATE 2022, ICCAD 2021 & 2013, ASPDAC 2021 & 2012, ICTAI 2019, the VLSI Journal in 2018, ISPD 2017, SPIE Advanced Lithography Conference 2016, and six ICCAD/ISPD contest awards.
Jeff Zhang (Arizona State University)
Jeff Zhang is currently an assistant professor in the School of Electrical, Computer and Energy Engineering at ASU. He received his PhD degree from New York University. From 2020-2022, Zhang was a postdoctoral fellow at Harvard University. Zhang’s general research interests are in deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. He received best paper award nominations at DATE 2022 and VTS 2018, and best presentation award nomination at DATE Ph.D. Forum 2020. Jeff serves on the technical program committee of several top conferences in the area of computer engineering and computer hardware, and has served as a reviewer for several IEEE and ACM journals.
Steering Committee
IEEE CEDA Young Professionals Coordinator: Qi Zhu (Northwestern University)
ACM SIGDA Education Chair: Jingtong Hu (University of Pittsburgh)