About
I am a second-year PhD student at MIT, where I am fortunate to be advised by Chara Podimata. I work on statistical foundations for the control and evaluation of AI systems, motivated by safety considerations and future regulatory pressures. My research revolves around two questions:
How can AI models understand their own uncertainty and react accordingly?
How can humans understand and audit the capabilities of AI systems?
I approach the former through calibration and uncertainty quantification methods that meet the needs of modern generative systems. To address the latter, I develop statistical tools that help us evaluate, monitor, and perform audits of black-box models.
Previously, I was an undergrad and master's student at ETH Zürich, where I was lucky to be part of the Learning and Adaptive Systems group of Andreas Krause. I worked on online learning, continual learning and meta-learning, as well as optimization algorithms for reinforcement learning. During my studies I was generously supported by the Zeno-Karl Schindler Foundation and the Swiss Study Foundation, facilitating visits at the University of Copenhagen and MIT.
Papers
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Nicolas Emmenegger*; Theo X. Olausson*; Armando Solar-Lezama; Chara Podimata Conformal Language Modelling via Posterior Sampling. Preprint. EIML and at Agentic UQ (Spotlight) workshops at ICML 2026.
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Nicolas Emmenegger; Ellery Stahler; Chara Podimata Prediction-Powered Inference across many Tasks for AI Evaluation and Social Science Research. Preprint. to be presented at Hypothesis Testing Workshop at ICML 2026.
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Nicolas Emmenegger*; Mojmír Mutný*; Andreas Krause Likelihood Ratio Confidence Sets for Sequential Decision Making. In Conference on Neural Information Processing Systems (NeurIPS 2023).
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Parnian Kassraie; Nicolas Emmenegger; Andreas Krause Anytime Model Selection in Linear Bandits. In Conference on Neural Information Processing Systems (NeurIPS 2023).
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Nicolas Emmenegger; Rasmus Kyng; Ahad. N. Zehmakan On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization. In International Conference on Artificial Intelligence and Statistics (AISTATS 2022).
