About
I am a second-year PhD student at MIT in the Operations Research Center, where I am fortunate to be advised by Chara Podimata. My goal is to develop principled tools that make increasingly capable AI systems more reliable, transparent, and accountable. My research is motivated by two questions:
How can AI models understand their own capabilities?
How can humans understand the capabilities of AI systems?
I approach the former through calibration and uncertainty quantification methods that meet the needs of modern AI 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*; 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).
