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Ed Chi
Ed Chi
VP of Research,
Google DeepMind
Ed H. Chi is VP of Research at Google DeepMind, leading machine learning research teams working on large language models (from LaMDA leading to launching Bard/Gemini), and universal assistant agents. With 39 patents and ~200 research articles, he is also known for research on user behavior in web and social media. As the Research Platform Lead, he helped launched Bard/Gemini, a conversational chatbot experiment. His research also delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >1000 product landings and ~$10.4B in annual revenue since 2013.
Prior to Google, he was Area Manager and Principal Scientist at Xerox Palo Alto Research Center's Augmented Social Cognition Group in researching how social computing systems help groups of people to remember, think and reason. Ed earned his 3 degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Inducted as an ACM Fellow and into the CHI Academy, he also received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid golfer, swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.
Heng-Tze Cheng
Heng-Tze Cheng
Research Director & Principal Scientist
Google DeepMind
Heng-Tze Cheng is Research Director & Principal Research Scientist at Google DeepMind, Gemini Team, focusing on Large Language Models and Generative AI research. Heng-Tze has been leading a series of major leaps in Gemini Deep Think, Post-Training, Reinforcement Learning, Reasoning and Coding, with landmark achievements including gold-medal level performance at ICPC & IMO, LMSYS #1, and Gemini 3. Before Gemini, he led the LaMDA project, Google's breakthrough Conversational AI with high quality, factuality, and safety. He also co-founded Google Bard from Day 1, launched it in 100 days, and helped grow it into Gemini App with 900 million users as of May 2026. Formerly on the Google Brain team, he founded and led Mixel Task-oriented Dialog Research, enabling 10+ new and improved dialog products in Duplex Assistant, Search, Maps, and Cloud. He founded and led Wide & Deep Learning, which was cited 5000+ times, open-sourced as an official model in TensorFlow and Google Cloud, and widely adopted across the industry. He also helped build large-scale end-to-end machine learning platforms, including Sibyl and TFX. Since 2015, he and his team have delivered 100+ product innovations and improvements through machine learning research across YouTube, Assistant, Google Play, Ads, and more. Prior to Google, he received his Ph.D. from Carnegie Mellon University in 2013, and B.S. from National Taiwan University in 2008.
Kuang-Huei Lee
Kuang-Huei Lee
Senior Staff Research Scientist
Google DeepMind
Kuang-Huei Lee is a Senior Staff Research Scientist at Google DeepMind. His research interests are in generative AI, robotics, reinforcement learning, and evolutionary methods. Besides fundamental research, his notable contributions at Google include leading Veo RL post-training, co-leading AI for chip placement research, and co-initiating the RT-1 robotic transformer. He has published extensively in conferences across machine learning (NeurIPS, ICML, ICLR), robotics (RSS, CoRL, IROS), computer vision (CVPR, ECCV), and NLP (EMNLP), receiving a CoRL Special Innovation Award and an IROS Best Paper nomination. Prior to joining the then Google Brain in 2019, he worked at Microsoft from 2016 to 2019. Kuang-Huei obtained his graduate degree from Carnegie Mellon University and his undergraduate degree from National Taiwan University.
Ming-Hsuan Yang
Ming-Hsuan Yang
Professor at UC Merced; Research Scientist at Google DeepMind
Ming-Hsuan Yang is a research scientist at Google working on vision and learning problems. He is also a professor of Electrical Engineering and Computer Science at University of California, Merced. He received Test-of-Time Award at WACV 2025, Best Paper at ICML 2024, Longuet-Higgins Prize at IEEE CVPR 2023, Best Paper Honorable Mention in IEEE CVPR 2018 and ACM UIST 2017. He is a recipient of the Faculty Early Career Development (CAREER) Award from the National Science Foundation in 2012 and Google Faculty Award in 2009. He is a Fellow of IEEE, ACM, AAAI, and AAAS.
Wei-Hung Weng
Wei-Hung Weng
Research Scientist
Google DeepMind
Wei-Hung Weng is a Research Scientist at Google DeepMind. He specializes in AI for science and medicine, contributing to the development of Co-Scientist, Science Skills for Antigravity, the conversational AI system AMIE, Med-Gemini, and specialized models for medical audio and waveform analysis. Wei-Hung holds an MD from Chang Gung University, an MMSc in Biomedical Informatics from Harvard, and a PhD in Computer Science from MIT. He is also an adjunct lecturer at Science Tokyo and previously practiced as a medical doctor in Taiwan.
Shuo-Yiin Chang
Shuo-Yiin Chang
Senior Staff Research Scientist
Google DeepMind
Shuo-Yiin Chang is a Senior Staff Research Scientist at Google DeepMind. He leads Gemini Multimodal Video and Audio-Visual workstream. His work is centered on Google's core foundational large language models, Gemini, and the universal AI assistants, Astra and Gemini Live. He received the IEEE Signal Processing Society Best Paper Award in 2024. Prior to Google, Shuo-Yiin completed his Ph.D. in EECS at the University of California, Berkeley.
臺灣學者
Shao-Hua Sun
Shao-Hua Sun
(孫紹華)
(孫紹華)
Assistant Professor, NTU
Shao-Hua Sun is an Assistant Professor in the Department of Electrical Engineering at National Taiwan University (NTU). He received his Ph.D. from the University of Southern California and his B.S. from NTU. His research spans machine learning, robot learning, reinforcement learning, and program synthesis. His work has been published at leading venues, including NeurIPS, ICML, CoRL, ICLR, etc. He has organized tutorials and workshops at NeurIPS, ICML, RLC, and CoRL. He has been awarded the MOE Yushan Young Fellow, NSTC Ta-You Wu Memorial Award, IICM K. T. Li Young Researcher Award, and NSTC 2030 Cross-Generation Young Scholars Program-Research Project for Emerging Young Scholars.
Hsuan-Tien Lin
Hsuan-Tien Lin
(林軒田)
(林軒田)
Professor, NTU
Prof. Hsuan-Tien Lin received his B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, and his M.S. and Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an Assistant Professor in 2008, and was promoted to Associate Professor in 2012, and has been a Professor since August 2017. In 2022, he was named the Cyberlink/Perfect Endowed Chair Professor. From 2016 to 2019, he served as Chief Data Scientist at Appier, a startup company that specializes in making AI easier across domains such as digital marketing and business intelligence, and he continued as Chief Data Science Consultant until 2025.
From the university, Prof. Lin received the Distinguished Teaching Awards in 2011 and 2021, the Outstanding Mentoring Award in 2013, and five Outstanding Teaching Awards between 2016 and 2020. He co-authored the introductory machine learning textbook Learning from Data and offered two popular Mandarin-teaching MOOCs Machine Learning Foundations and Machine Learning Techniques based on the textbook.
Prof. Lin served in the machine learning community as Progam Co-Chair of NeurIPS 2020, Expo Co-Chair of ICML 2021, Workshop Co-Chair of NeurIPS 2022, Workshop Chair of NeurIPS 2023, Program Co-Chair of ACML 2024, General Co-Chair of ACML 2025, Senior Program Chair of NeurIPS 2025, and General Co-Chair of NeurIPS 2026. His research interests include mathematical foundations of machine learning, studies on new learning problems, and improvements on learning algorithms. He received the 2012 K.-T. Li Young Researcher Award from the ACM Taipei Chapter, the 2013 D.-Y. Wu Memorial Award from National Science Council of Taiwan, the 2017 Creative Young Scholar Award from Foundation for the Advancement of Outstanding Scholarship in Taiwan, the 2025 Breakthrough Chair Professorship from Foundation for the Advancement of Outstanding Scholarship in Taiwan, and the 2025 Outstanding Research Award from National Science and Technology Council of Taiwan. He co-led the teams that won the third place of KDDCup 2009 slow track, the champion of KDDCup 2010, the double-champion of the two tracks in KDDCup 2011, the champion of track 2 in KDDCup 2012, and the double-champion of the two tracks in KDDCup 2013.
Cheyu Hsu
Cheyu Hsu
(許哲瑜)
(許哲瑜)
Attending Physician and Radiation Oncologist
Department of Oncology, National Taiwan University Hospital
Cheyu Hsu, MD, PhD, is an attending physician and radiation oncologist in the Department of Oncology at National Taiwan University Hospital.
He received his medical degree from National Yang-Ming University, a master's degree in Genome and Systems Biology from the National Taiwan University-Academia Sinica program, and a PhD in Data Science from the National Taiwan University-Academia Sinica program.
His clinical and research interests include radiation oncology, stereotactic radiotherapy, brain metastases and neuro-oncology, multimodal medical imaging, radiomics, and medical artificial intelligence.
His work focuses on integrating perception, segmentation, and language-based reasoning to develop grounded medical intelligence for radiological images.
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