Jiang Hu

Short CV

Jiang Hu received the B.S. degree in optical engineering from Zhejiang University in 1990, the M.S. degree in physics in 1997 and the Ph.D. degree in electrical engineering from the University of Minnesota in 2001. He worked with IBM Microelectronics from January 2001 to June 2002. In 2002, he joined the electrical engineering faculty at Texas A&M University and became a full professor in 2014. He published 250 technical papers, co-authored 7 book chapters, co-edited a book, co-invented 10 patents and supervised the study of 24 Ph.D. dissertations. He served as an associate editor for IEEE Transactions on CAD and ACM Transactions on Design Automation of Electronic Systems. He was the technical program chair and general chair of the ACM International Symposium on Physical Design in 2011 and 2012, respectively. He is also the technical program co-chair of the ACM/IEEE Workshop on Machine Learning for CAD in 2023.

Selected Awards

• 2023, Best paper award, ACM/IEEE Asia and South Pacific Design Automation Conference
• 2021, Best paper award, IEEE/ACM International Symposium on Microarchitecture
• 2020, Texas A&M engineering genesis Award
• 2018, Best paper award, IEEE International Conference on Vehicular Electronics and Safety
• 2016, ECE outstanding professor award, Texas A&M University
• 2016, IEEE Fellow
• 2012, Alexander von Humboldt Research Fellowship
• 2011, Best paper award, IEEE/ACM International Conference on Computer-Aided Design
• 2003, IBM invention achievement award
• 2001, Best paper award, ACM/IEEE Design Automation Conference

Research Interests

Electronic design automation, VLSI physical design, optimization of large computing systems, approximate computing, computer architecture, hardware security.

Selected Publications

• Z. Xie, R. Liang, X. Xu, J. Hu, C.-C. Chang, J. Pan and Y. Chen, “Pre-Placement Net Length and Timing Estimation by Customized Graph Neural Network”, IEEE Trans. Computer-Aided Design, Vol. 41, No. 11, pp. 4667-4680, November 2022.
• H. Ren and J. Hu, Machine Learning Applications in Electronic Design Automation, Springer, 2022.
• L. Feng, Jiayi Huang, Jeff Huang and J. Hu, “Toward Taming the Overhead Monster for Data-flow Integrity,” ACM Trans. Design Automation of Electronic Systems, Vol. 27, No. 3, pp, 25:1-25:24, May 2022.
• R. Liang, J. Song, B. Yuan and J. Hu, “Deep Learning Toolkit-Accelerated Analytical Co-optimization of CNN Hardware and Dataflow,” IEEE/ACM International Conference on Computer-Aided Design, 2022.
• P. Sengupta, A. Tyagi, Y. Chen and J. Hu, “How Good is Your Verilog RTL Code? A Quick Answer from Machine Learning,” IEEE/ACM International Conference on Computer-Aided Design, 2022.
• R. Liang, H. Xiang, J. Jung, J. Hu and G.-J. Nam, “A Stochastic Approach to Handle Non-Determinism in Deep Learning-Based Design Rule Violation Predictions,” IEEE/ACM International Conference on Computer-Aided Design, 2022.
• Z. Xie, X. Xu, M. Walker, J. Knebel, K. Palaniswamy, N. Hebert, J. Hu, H. Yang, Y. Chen and S. Das, “APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial Microprocessors,” IEEE/ACM International Symposium on Microarchitecture, 2021.
• E. C. Barboza, S. Jacob, M. Ketkar, M. Kishinevsky, P. Gratz and J. Hu, “Automatic Microprocessor Performance Bug Detection,” IEEE International Symposium on High Performance Computer Architecture, 2021.
• Y. Li, Y. Lin, M. Madhusudan, A. Sharma, W. Xu, S. Sapatnekar, R. Harjani and J. Hu, “A Customized Graph Neural Network Model for Guiding Analog IC Placement,” IEEE/ACM International Conference on Computer-Aided Design, 2020.
• N. G. Jayasankaran, A. S. Borbon, A. Abuellil, E. Sánchez-Sinencio, J. Hu and J. Rajendran, “Breaking Analog Locking Techniques via Satisfiability Modulo Theories,” IEEE International Test Conference, 2019.