Chun-Yi Lee is a Professor in the Department of Computer Science and Information Engineering at National Taiwan University (NTU), Taipei, Taiwan, and the supervisor of Elsa Lab. He received his B.S. and M.S. degrees from National Taiwan University in 2003 and 2005, respectively, and his M.A. and Ph.D. degrees from Princeton University, Princeton, NJ, USA, in 2009 and 2013, respectively, all in Electrical Engineering. Prof. Lee joined the Department of Computer Science at NTHU as an Assistant Professor in 2015. He was promoted to Associate Professor in 2019 and to full Professor in 2023. In 2024, Prof. Lee joined the Department of Computer Science and Information Engineering at National Taiwan University. Prior to 2015, he served as a senior engineer at Oracle America, Inc. in Santa Clara, CA, USA, from 2012 to 2015. Prof. Lee founded Elsa Lab in 2015. Under his leadership, Elsa Lab has garnered several prestigious awards from global robotics and AI challenges.Prof. Lee's research primarily focuses on deep reinforcement learning (DRL), intelligent robotics, computer vision (CV), and parallel computing systems. His contributions include developing key deep learning methodologies for intelligent robotics, such as sim-to-real and real-to-sim training, transfer techniques for robotic policies, digital twins, scene coordinate regression approaches, and domain adaptation techniques for semantic segmentation models. He has also advanced exploration approaches for DRL agents, multi-agent reinforcement learning (MARL) techniques, generative modeling methodologies, as well as autonomous navigation strategies. His work has been published at major AI conferences such as NeurIPS, CVPR, IJCAI, AAMAS, ICLR, ICML, ECCV, BMVC, CoRL, ICRA, IROS, GTC, MVA, and others. Additionally, his research appears in top AI journals, including IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Journal of Machine Learning Research (JMLR), and ACM Transactions on Evolutionary Learning and Optimization (TELO). In the realm of parallel systems, Prof. Lee has introduced innovative GPU enhancement methodologies to improve efficiency. His work in this area has been published in IEEE Transactions on Very Large Scale Integration Systems (TVLSI), IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), the Design Automation Conference (DAC), the Asia and South Pacific Design Automation Conference (ASP-DAC), and International Conference on Computer Design (ICCD).