Long Bio

My background is in math and physics, leaning theoretical but always grounded in experiments and applications. Early exposure to quantum information science with A. Steinberg, combined with an interest in physicists' successful forays into biology, pointed me toward neuroscience. For my PhD with F. Wolf at the Max Planck Institute, I studied neural circuit dynamics from a physics and dynamical-systems perspective, situating me in theoretical neuroscience and biophysics.

My first postdoc, with A. Walczak and T. Mora in Paris, pushed further into applications: building statistical inference tools to detect change in large datasets, using immune repertoires as the test case — though the toolset mattered more than the specific domain.

For my second postdoc (IVADO, with Y. Bengio and G. Lajoie at UdeM), I turned to learning dynamics and reinforcement learning, modelling goal-driven, strategic agent behaviour. Work with P. Cisek on the "tokens task" showed how animals deviate from reward-optimal behaviour once you account for their social context — reinforcing how central social factors are to decision-making. I also joined Irina Rish's Autonomous AI research cluster, studying why RL algorithms struggle in non-stationary, multi-agent continual learning settings.

Around then, growing concern about climate change pulled me from neuroscience toward the social sciences. Social dilemmas became my focus, with text as the natural data source — topic modelling being a close relative of the generative models I'd used for immune repertoires. This drew me into computational social science, and I joined McGill's Complex Data Lab to combine this with multi-agent modelling.

Today, social dilemmas are increasingly mediated by AI, which is why I see AI safety as the central social dilemma question. My contribution is a fairly unusual combination: theory and empirics on network dynamical systems, applied to the stability of collective states in learning agents.

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