Deming Chen is the Abel Bliss Professor in the Grainger College of Engineering at the University of Illinois Urbana-Champaign. His research interests include hybrid cloud systems, machine learning and AI, security and confidential computing, reconfigurable and heterogeneous computing, and system-level design methodologies. He has published 300 research papers, received 10 Best Paper Awards and an ACM/SIGDA TCFPGA Hall-of-Fame Paper Award, and delivered more than 160 invited talks. His work has had a significant impact, with open-source solutions adopted by industry, such as FCUDA, DNNBuilder, CSRNet, SkyNet, ScaleHLS, and Medusa. Notably, Medusa has been integrated into Nvidia's TensorRT-LLM, improving the speed of large language model (LLM) execution by 1.9-3.6x. He is an IEEE Fellow, an ACM Distinguished Speaker, and the former Editor-in-Chief of ACM Transactions on Reconfigurable Technology and Systems (TRETS). Under his leadership, the impact factor of ACM TRETS has increased by 3.8 times. He serves as the Illinois Director of the IBM-Illinois Discovery Accelerator Institute and the Director of the AMD-Xilinx Center of Excellence. Additionally, he has been involved in several startup companies, including AutoESL and Inspirit IoT. He received his Ph.D. in Computer Science from UCLA in 2005.

Igor Markov is a Distinguished Architect at Synopsys and an IEEE Fellow. As a professor at the University of Michigan, he did research on EDA, coauthored a textbook on Physical Design, and co-edited a two-volume EDA Handbook. He won best paper awards at ICCAD, ISPD, DATE, and TCAD, and has published more than 200 peer-reviewed papers. At Synopsys, Igor has been leading the AI Disruption Task Force, participating in the GPU Task Force, and contributing to quantum computing efforts.

Bing Li is a professor at the University of Siegen. His research focuses on AI-assisted EDA, circuit and system architectures, and emerging technologies such as RRAM and optical accelerators for neural networks. He has served on program committees for major conferences (DAC, ICCAD, DATE, ASP-DAC) and was program chair of the ACM/IEEE MLCAD Workshop in 2022 and 2023.

Dr. Qi Sun is currently a ZJU100 Young Professor at the College of Integrated Circuits, Zhejiang University. Before joining ZJU, he worked as a Post-Doctoral Associate at the School of Electrical and Computer Engineering, Cornell University. Previously, he received a Ph.D. degree from the Dept. of CSE, Chinese University of Hong Kong in Jul. 2022. His research interests include Machine Learning for EDA, and Large Language Models for Design Technology Co-optimization. His work has been recognized with two ICCAD Best Paper Awards, and a Best Paper Award Nomination of DATE.