Govern frontier AI with confidence.

Elsa is a Legal Officer in AI Safety at the European Commission. Before that, she was an AI Policy Fellow at Ada Lovelace Institute. She studied law before moving into AI governance, completing several programmes in the field including ML4Good, MARS, Orion and Talos. Last summer, she was a summer fellow at GovAI where she explored legal issues surrounding future autonomous/AI-run businesses, specifically legal personhood and corporate law.

She has also been a co-head teacher at ML4Good, teaching at residential bootcamps focused on AI governance and technical AI safety, and a collaborator at the Institute for Law & AI. She spent ten months supervising research projects at the Supervised Program for Alignment Research. She teaches at the European Seminar for Frontier AI and Law, and ML4Good, in a personal capacity, and her views do not represent those of the European Commission.

Katalina is Head of Legal at EquiStamp, an AI evaluation company focused on safety and regulatory compliance. Before that, she spent nearly three years as Responsible AI Lead at VOIS, where she led EU AI Act implementation across ten jurisdictions and sat on the Group AI Board. She has a background in data protection and privacy law, and holds an LLM with Distinction from London Metropolitan University.

Douw is an AI safety research engineer at Equistamp, where he works on agentic evaluations, risk estimation and alignment, contributing to projects for the European Commission's Technical Assistance for AI Safety tender, Redwood Research, METR and the AI Futures Project. He holds a PhD from KU Leuven as a Marie Skłodowska-Curie Fellow, applying unsupervised machine learning and differentiable signal processing to fault detection in rotating machinery, with a dozen peer-reviewed publications and a runner-up finish in the PHM 2023 data challenge. Originally a mechanical engineer from the University of Pretoria, Douw brings a strong grounding in physical systems and responsible data science.

Jeff is a technical AI safety researcher and CBAI fellow in Cambridge, MA, working with MIT FutureTech on interpretability. Specifically, he works on models that describe other models' internal states in plain language, building on recent work such as the Natural Language Autoencoder. His research explores training these "activation parsing" models to be more sensitive to a target model's intentions and strategising, and he has shown they can be made small enough to deploy cheaply in oversight and control settings. He came to the field after a decade as a physicist and teacher, including postdoctoral work on the Simons Observatory and faculty posts at Villanova and Colorado College.

Diego is a Senior Software Developer for PEReN, the Center of expertise for digital platform regulation in the French government, and works with the EU AI Office to build the technical infrastructure required for the large-scale evaluation of models. His research has been published at ICML, a top academic conference in AI.