Hung-yi Lee is a professor of the Department of Electrical Engineering of National Taiwan University (NTU), with a joint appointment at the Department of Computer Science & Information Engineering. His recent research focuses on developing technology that can reduce the requirement of annotated data for speech processing and natural language processing. He won Salesforce Research Deep Learning Grant in 2019, AWS ML Research Award in 2020, Outstanding Young Engineer Award from The Chinese Institute of Electrical Engineering in 2018, Young Scholar Innovation Award from Foundation for the Advancement of Outstanding Scholarship in 2019, Ta-You Wu Memorial Award from Ministry of Science and Technology of Taiwan in 2019, and the 59th Ten Outstanding Young Person Award in Science and Technology Research & Development of Taiwan. He owns a YouTube channel that teaches deep learning in Mandarin and has about 200k subscribers.

Yun-Nung (Vivian) Chen is currently an associate professor in the Department of Computer Science & Information Engineering at National Taiwan University. She earned her Ph.D. degree from Carnegie Mellon University, where her research interests focus on spoken dialogue systems and natural language processing. She was recognized as the Taiwan Outstanding Young Women in Science and received Google Faculty Research Awards, Amazon AWS Machine Learning Research Awards, MOST Young Scholar Fellowship, and FAOS Young Scholar Innovation Award. Her team was selected to participate in the first Alexa Prize TaskBot Challenge in 2021. Prior to joining National Taiwan University, she worked in the Deep Learning Technology Center at Microsoft Research Redmond.

Jiaxian Guo is a visiting researcher at the University of Tokyo. He completed his bachelor's degree at Shanghai Jiao Tong University in 2018 and his PhD from the University of Sydney in 2022. Jiaxian’s research interests lie in developing generalizable and reliable foundation model with reinforcement learning and causality. He has published several papers and serves as a reviewer for prestigious conferences such as NeurIPS, ICML, CVPR, and ICLR. Jiaxian's academic excellence was highlighted by his nomination for the best thesis candidate at Shanghai Jiao Tong University and receiving the PhD Completion Award from the University of Sydney.

Dr. Samuel Yen-Chi Chen received the Ph.D. and B.S. degree in physics from National Taiwan University, Taipei City, Taiwan. He is now a senior research scientist at Wells Fargo Bank. Prior to that, he was an assistant computational scientist in the Computational Science Initiative, Brookhaven National Laboratory. He is the first one to use variational quantum circuits to perform deep reinforcement learning and the inventor of quantum LSTM. His research interests include building quantum machine learning algorithms as well as applying classical machine learning techniques to solve quantum computing problems. He won the First Prize In the Software Competition (Research Category) from Xanadu Quantum Technologies, in 2019.

Leo Yang, currently serving as an Applied Research Scientist at Thomson Reuters Labs, primarily engages in research and development within the field of document understanding. His educational background encompasses economics, international business management, and computer science. He has previously worked as a data analyst in Taiwan and conducted academic research on knowledge graphs and recommendation systems. Currently residing in Toronto, Canada, Leo is an avid camper and skier.

YC currently serves as a Senior Technology Manager at MediaTek Research. He has over 6 years of research and development experience in machine learning, with a focus on language, visual, and speech AI. With 3 years of team leadership experience, he adeptly leads teams through the entire process from research to practical application of technologies. He has published 5 machine learning papers, including in renowned conferences such as INTERSPEECH, ASRU, and ICIP. Additionally, he serves as the webmaster of YC Note.

More details: https://ycc.idv.tw/about-me.html

Mark Chang is a Research Scientist at MediaTek Research, specializing in reinforcement learning theory and its applications in electronic design automation. Previously, he worked at DeepQ on medical AI applications. He received his master's degree from the Graduate Institute of Electronics Engineering at National Taiwan University, and he is currently pursuing his PhD degree in the Graduate Institute of Communication Engineering at National Taiwan University, advised by Professor Pei-Yuan Wu.