Invited Talks

About the speaker Tie-Yan Liu

Tie-Yan Liu is the founding President of Zhongguancun Academy in Beijing. His research spans machine learning and its application to industrial transformation and scientific discovery, with pioneering contributions to learning to rank, computational advertising, efficient and distributed machine learning, unsupervised machine translation, and scalable AI-for-Science models. Before starting the Academy in 2024, he spent over two decades at Microsoft Research (MSR), serving as Assistant Managing Director of MSR Asia and later as Distinguished Scientist of MSR AI for Science. He has published several hundred papers, cited over 110,000 times with an h-index of 115, and has served as program chair and senior area chair at major AI conferences. BenchCouncil's AI100 list names him among the most influential contributors to AI since 1943, and his work LightGBM among the 100 most important achievements in the field. He is a Fellow of the IEEE, and the ACM, and has held adjunct or honorary professorships at Carnegie Mellon, Tsinghua, and the University of Nottingham, among others.

About the speaker Barbara E Engelhardt

Barbara E Engelhardt is a Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science. She received her B.S. (Symbolic Systems) and M.S. (Computer Science) from Stanford University and her PhD from UC Berkeley (EECS) advised my Prof. Michael I Jordan. She was a postdoctoral fellow with Prof. Matthew Stephens at the University of Chicago. She was an Assistant Professor at Duke University from 2011-2014, and an Assistant, Associate, and then Full Professor at Princeton University in Computer Science from 2014-2022. She has worked at Jet Propulsion Labs, Google Research, 23andMe, and Genomics plc. In her career, she received an NSF GRFP, the Google Anita Borg Scholarship, the SMBE Walter M. Fitch Prize (2004), a Sloan Faculty Fellowship, an NSF CAREER, and the ISCB Overton Prize (2021). Her research is focused on developing and applying models for structured biomedical data that capture patterns in the data, predict results of interventions to the system, assist with decision-making support, and prioritize experiments for design and engineering of biological systems.

About the speaker Satinder Singh

My main research interest is in the old-fashioned goal of Artificial Intelligence (AI), that of building autonomous agents that can learn to be broadly competent in complex, dynamic, and uncertain environments. The field of reinforcement learning (RL) has focused on this goal and accordingly my deepest contributions are in RL. A very recent effort combines Deep Learning and Reinforcement Learning.

About the speaker Cheng Soon Ong

Cheng Soon Ong is the science director at the Technology Research Unit in CSIRO. He works on extending machine learning methods and enabling new approaches to scientific discovery, and has led programs of research including foundational machine learning in science and purpose driven machine learning applications at CSIRO. He supervises and mentors many junior scientists, collaborates with scientists on problems in genomics and astronomy, advocates for open science, and is an adjunct Associate Professor at the Australian National University. He is co-author of the textbook Mathematics for Machine Learning, and his career has spanned multiple roles in Malaysia, Germany, Switzerland, and Australia.

About the speaker Shannon Vallor

Professor Shannon Vallor FRSE is the Baillie Gifford Chair in the Ethics of Data and Artificial Intelligence in the Department of Philosophy at The University of Edinburgh, where she also codirects the Centre for Technomoral Futures in the university’s interdisciplinary Edinburgh Futures Institute. Prof. Vallor’s research explores how AI, robotics, and data science reshape human character: our habits, virtues and capabilities. Professor Vallor's work includes advising policymakers and industry bodies on the ethical design and use of AI, and she is a former Visiting Researcher and AI Ethicist at Google. She is the recipient of multiple prizes and awards for her research, including the 2026 Barwise Prize in Philosophy and Computing from the American Philosophical Association, the 2026 Leadership Award from the UK’s AI and Robotics Research Awards, and the 2022 Covey Award from the International Association of Computing and Philosophy. She is a standing member of Stanford University’s One Hundred Year Study of Artificial Intelligence (AI100), EY’s Global AI Advisory Council, and a member of the Oversight Board of the Ada Lovelace Institute. She is the author of two monographs, Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting, from Oxford University Press in 2016, and The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking, from Oxford University Press in 2024.

About the speaker José Hernández-Orallo

Jose H. Orallo is Director of Research at the Leverhulme Centre for the Future of Intelligence, University of Cambridge, UK, and Professor (on partial leave) at TU Valencia, Spain. He pioneered the use of algorithmic information theory, psychometrics and cognitive science in AI evaluation, to better understand and anticipate the capabilities, generality, progress, impact and risks of artificial intelligence. He has published five books and more than two hundred journal articles and conference papers in Nature, Science, CACM, NeurIPS, ICML, ACL, AAAI, etc. His research in the area of machine intelligence evaluation has been covered by several popular outlets, such as The Economist, WSJ, FT and New Scientist. He keeps exploring a more integrated view of the evaluation of natural and artificial intelligence, as vindicated in his book The Measure of All Minds (Cambridge University Press, 2017, PROSE Award 2018). He is an EurAI fellow and has been advising national and international organisations such as the EU, OECD, the UK and Spanish governments, and contributes to the scientific panel of the EU AI Office. He is a founder of aievaluation.substack.com and ai-evaluation.org. In 2026 he received the Spanish National Award in Computer Science.