
Prof. Irene Chen
Assistant Professor

AI Safety from the Lab to the Real World
A live panel on evaluating, securing, and responsibly deploying AI and setting the stage for Research Slam this October.
The Research Slam is a fast, accessible showcase for academics and researchers tackling consequential real-world problems. Presenters distill their work into a memorable story: what matters, what changed, and where it could lead.
This year’s focus is Trust & Safety: how we evaluate, secure, govern, and responsibly deploy increasingly capable AI systems. The stage connects promising research with the operators, collaborators, and investors who can help take it further.
Two perspectives on AI safety, from research to real-world deployment.

Assistant Professor

ML Team Lead & Gradio Co-Founder
The panel surfaces the key challenges, perspectives, and open questions in Trust & Safety that will frame the research presented at the Slam, including evaluation, alignment, bias, and high-stakes deployment.
View event detailsPizza, drinks, and informal networking with researchers and builders from across the Bay Area.
A quick pulse-check surfaces the room’s biggest questions about trust, safety, and deployment.
A conversation spanning model evaluation, alignment, bias, high-stakes applications, and real-world deployment.
Bring your toughest questions and help shape the themes carried into the Research Slam.
Continue the conversation with the panelists and fellow attendees.
Check in with building security in the lobby at 525 Market Street. The lobby team will provide elevator access to the 32nd floor, where a member of the Goodwin team will greet you.
Pizza and drinks will be served.Our list is intentionally broad. If your work makes an important AI idea understandable, challenges an assumption, or opens a path to meaningful impact, we want to hear from you.
Clinical decision support, diagnostics, drug discovery, personalized care, and AI systems built for sensitive health settings.
Embodied intelligence, safe control, human–robot collaboration, and autonomous systems that operate beyond the lab.
Reasoning, tool use, agent architectures, evaluation, and dependable systems capable of taking meaningful action.
Robustness, interpretability, red teaming, governance, and practical approaches to keeping capable systems aligned.
Perception, multimodal learning, spatial intelligence, synthetic media, and trustworthy vision in the real world.
Research accelerating climate modeling, energy systems, materials discovery, adaptation, and environmental resilience.
New ways people create, decide, learn, and collaborate with AI, designed around agency, access, and human values.
Learning theory, optimization, new model architectures, data methods, and the scientific ideas underpinning what comes next.