Speaker Profiles

SPEAKER PROFILES

Assistant Professor Antoine Bosselut

Talk Title
Democratizing Open LLMs for Global Sovereign AI Applications

Speaker
Assistant Professor Antoine Bosselut

Biography
Antoine Bosselut is an assistant professor in the School of Computer and Communication Sciences at the École Polytechnique Fédéral de Lausanne (EPFL). Previously, he was a postdoctoral fellow at Stanford University and a Young Investigator at the Allen Institute for AI (AI2). He received his PhD at the University of Washington in 2020. His work focuses on developing AI reasoning methodologies that can be effectively translated to important societal problems in health, education, and helping underserved communities. He is an ELLIS Scholar and was named as one of the Forbes 30 under 30 list for Science and Healthcare in 2021. He is also on the steering committee of the Swiss AI Initiative, which builds capacities in Switzerland for advanced and large-scale AI systems for the benefit of society.

Professor Arnaud Doucet

Talk Title
Accelerating Diffusion Models via Speculative Sampling

Speaker
Professor Arnaud Doucet
University of Oxford

Biography
Arnaud Doucet received his PhD in Information Engineering from University Paris-XI in 1997. Ever since, he has held faculty positions at the University of Melbourne, the University of Cambridge, the University of British Columbia and the Institute of Statistical Mathematics in Tokyo. Until 2024, he was a Chair Professor in the Department of Statistics of Oxford University. He is now a Senior Staff Research Scientist at Google DeepMind.

He was an Institute Mathematical Statistics Medallion Lecturer in 2016, received the Guy Silver Medal of the Royal Statistical Society in 2022, the Akaike Memorial Award in 2024 and was the Breiman Lecturer at NeurIPS 2024.

His main research interests are in computational statistics, statistical machine learning and Monte Carlo methods.

Associate Professor Stefano Ermon

Talk Title
Diffusion Language Models: A Unifying Paradigm for Multimodal Generative Modeling

Speaker
Associate Professor Stefano Ermon
Stanford University

Biography
Stefano Ermon is an Associate Professor of Computer Science in the CS Department at Stanford University where he is affiliated with the Artificial Intelligence Laboratory. His research is centered on techniques for probabilistic modeling of data and generative models. He has won several awards, including multiple Best Paper Awards, a NSF Career Award, ONR and AFOSR Young Investigator Awards, Microsoft Research Fellowship, Sloan Fellowship, and the IJCAI Computers and Thought Award. Stefano earned his Ph.D. in Computer Science at Cornell University in 2015.

Associate Professor Yarin Gal

Talk Title
Hallucinations in GPT

Speaker
Associate Professor Yarin Gal
University of Oxford

Biography
Yarin leads the Oxford Applied and Theoretical Machine Learning (OATML) group. He is an Associate Professor of Machine Learning at the Computer Science department, University of Oxford. He is also the Tutorial Fellow in Computer Science at Christ Church, Oxford, a Turing AI Fellow at the Turing Institute, and Director of Research at the UK Government’s AI Security Institute (AISI, formerly the Frontier AI Taskforce).

Prior to his move to Oxford he was a Research Fellow in Computer Science at St Catharine’s College at the University of Cambridge. He obtained his PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship.

Yarin made substantial contributions to early work in modern Bayesian deep learning—quantifying uncertainty in deep learning—and developed ML/AI tools that can inform their users when the tools are “guessing at random”. These tools have been deployed widely in industry and academia, with the tools used in medical applications, robotics, computer vision, astronomy, in the sciences, and by NASA.

Beyond his academic work, Yarin works with industry on deploying robust ML tools safely and responsibly. He co-chairs the NASA FDL AI committee, and is an advisor with Canadian medical imaging company Imagia, Japanese robotics company Preferred Networks, as well as numerous startups.

The primary theme guiding his research is Pragmatic Approaches to Fundamental Research. This includes making use of principled approaches to develop new, practical, ML tools, and studying theoretical questions uncovered by real-world applications of ML.

Fields Yarin has published work in include: Bayesian deep learning • deep learning • adversarial machine learning • causal inference • ML and security • quantisation and pruning • active learning • continual learning • approximate Bayesian inference • Gaussian processes • Bayesian modelling • Bayesian non-parametrics • scalable MCMC • generative modelling. With applications including: computer vision • medical analysis • astronomy • autonomous driving • finance • natural language processing • robotics • technical AI safety • ML interpretability • reinforcement learning • and others.

Stephen McAleer

Talk Title
High-Compute Alignment and Control

Speaker
Stephen McAleer
OpenAI

Biography
Stephen McAleer is a researcher at OpenAI working on agent safety. He was previously a postdoc at Carnegie Mellon University working with Tuomas Sandholm. His work has been published in Science, Nature Machine Intelligence, ICML, NeurIPS, and ICLR, and has been featured in news outlets such as the Washington Post, the LA Times, MIT Technology Review, and Forbes.

Professor Dawn Song

Talk Title
Towards Building Safe and Secure AI

Speaker
Professor Dawn Song
University of California, Berkeley

Biography
Dawn Song is a Professor in Computer Science at UC Berkeley and Co-Director of Berkeley Center for Responsible Decentralized Intelligence. Her research interest lies in AI and deep learning, security and privacy, and decentralization technology. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, ACM SIGSAC Outstanding Innovation Award, and more than 10 Test-of-Time Awards and Best Paper Awards from top conferences in Computer Security and Deep Learning. She has been recognized as Most Influential Scholar (AMiner Award), for being the most cited scholar in computer security. She is an ACM Fellow and an IEEE Fellow. She obtained her Ph.D. degree from UC Berkeley. She is also a serial entrepreneur and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

Associate Professor Mengdi Wang

Talk Title
From Genome to Theorem: Can Large Language Models Do Science?

Speaker
Associate Professor Mengdi Wang
Princeton University

Biography
Mengdi Wang is Co-Director of Princeton AI for Accelerated Invention, and Professor of the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning at Princeton University. She is also affiliated with the Department of Computer Science, Omenn-Darling Bioengineering Institute, and Princeton Language+Intelligence. She was a visiting research scientist at Google DeepMind, IAS and Simons Institute on Theoretical Computer Science. Her research focuses on machine learning, reinforcement learning, generative AI, large language models, and AI for science.

Assistant Professor Diyi Yang

Talk Title
The Future of Human-AI Interaction: Teaching, Talking and Teaming Up

Speaker
Assistant Professor Diyi Yang
Stanford University

Biography
Diyi Yang is an assistant professor in the Computer Science Department at Stanford University. Her research focuses on human-centered natural language processing and computational social science. She is a recipient of Microsoft Research Faculty Fellowship (2021), NSF CAREER Award (2022), an ONR Young Investigator Award (2023), and a Sloan Research Fellowship (2024). Her work has received multiple paper awards or nominations at top NLP and HCI conferences.