This meeting room is for ICML delegates to relax and recharge in a comfortable environment.
How to move losses down, and rewards and metrics up: from a robot’s arm motion in my PhD, to the policy of a virtual assistant or of a self-driving car in my Berkeley lab and at Waymo later, to the Gemini model today at Google DeepMind, that’s been the name of the game. But throughout it all, what I cared about more was what those losses/rewards/metrics ought to be in the first place. What started as an intuition in grad school – that what to optimize was the deeper and harder question than how to optimize – became a central pursuit when I became faculty, as my lab and I sought to understand the ins and outs of how agents can accomplish what we want without unintended side effects. Now at the heart of frontier AI development, that experience is coming in handy as we work to make Gemini a useful and safe collaborator for humanity.
Science communication skills are often lacking from academic programs, but knowing how to explain your research effectively will help you when presenting it to your peers, performing in a job interview, or soliciting funding for a project. This hands-on session will give you practical tips and exercises to craft a short, effective and accessible overview of your work for a wide range of audiences and applications.
How can we accelerate scientific discovery when experiments are costly and uncertainty is high? From protein engineering to robotics, data efficiency is critical—but advances in lab automation and the rise of foundation models are creating rich new opportunities for intelligent exploration. In this talk, I’ll share recent work toward closing the loop between learning and experimentation, drawing on active learning, Bayesian optimization, and reinforcement learning. I’ll show how we can guide exploration in complex, high-dimensional spaces; how meta-learned generative priors enable rapid adaptation from simulation to reality; and how even foundation models can be adaptively steered at test time to reduce their epistemic uncertainty. I’ll conclude by highlighting key challenges and exciting opportunities for machine learning to drive optimization and discovery across science and engineering.
Building ML Systems: From Research to Real-World Production with MLOps
Building machine learning systems that work in production is significantly more complex than training high-accuracy models in research. This social aims to bring together researchers, engineers, and practitioners interested in MLOps—the set of practices that enables scalable, reproducible, and reliable ML deployment. We will explore the challenges of operationalizing ML, from data drift and CI/CD to model monitoring and governance. The session will include lightning talks, informal discussion circles, and networking opportunities. It is targeted at attendees who want to bridge the gap between cutting-edge ML research and real-world system deployment.
We will begin with a panel on the impacts of reasoning models and goal-directed behavior on AI safety, followed by Q&A and free discussions. Our panelists are Aditi Raghunathan, Anca Dragan, David Duvenaud, and Siva Reddy. Come connect over snacks & drinks!
This event is hosted by the Center for AI Safety.
Building Inclusive Communities at ICML by LatinX in AI, WiML and RBC Borealis
Event page: https://rbcborealis.com/icml-2025-event-building-inclusive-communities-at-icml/
Register here: https://lu.ma/vhu2byhd
Join our mentoring sessions for students, postdocs, and early career industry researchers and engineers. The format is speed mentoring: a group of mentees join a mentor at a table, chat for 15-20 minutes, and then the mentors rotate across the tables and keep the conversation going. This is a great way to discuss a lot of topics in a little time and hear from different perspectives.
While the social is 7-9pm, do feel free to come and go, and join for just the first or second hour if that is what fits your schedule.
- Sign up as a mentor!
- Sign up as a mentee!
Our mentors include
- Margo Seltzer: UBC
- Peter McElroy: EarthDaily
- Yu Sun: Stanford University
- Motasem Alfarra: Qualcomm AI Research (was: KAUST)
- Tahniat Khan: Vector Institute
- Claas Voelcker: University of Toronto
- Abeer Badawi: York University
- Mahdi Haghifam: Northeastern University
- Yani Ioannou: University of Calgary
- Anthony Fuller: Carleton University + Vector
- Danica Sutherland: UBC + Amii
- Evan Shelhamer: UBC + Vector (was: Google DeepMind, Adobe Research, UC Berkeley)