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Im Oberen Werk 1
66386 St. Ingbert (Germany)

Short Bio

Dr. Krikamol Muandet is tenured Faculty at CISPA Helmholtz Center for Information Security. From 2018 to 2022, he was a research group leader affiliated with the Empirical Inference Department at Max Planck Institute for Intelligent Systems, Tübingen, Germany. From January 2016 to December 2017, he was a lecturer at the Department of Mathematics, Faculty of Science, Mahidol University in Thailand. He graduated summa cum laude with a PhD degree specializing in kernel methods in machine learning. His PhD advisor was Prof. Bernhard Schölkopf. He also obtained a master’s degree with distinction in machine learning from University College London (UCL), United Kingdom. At UCL, he worked primarily in the Gatsby Computational Neuroscience Unit with Prof. Yee Whye Teh.

CV: Last stations

Since 2026
Tenured Faculty at CISPA Helmholtz Center for Information Security
2022 - 2026
Tenure-Track Faculty at CISPA Helmholtz Center for Information Security
2018 - 2022
Research group leader affiliated with the Empirical Inference Department at Max Planck Institute for Intelligent Systems
2016 - 2017
Lecturer at the Department of Mathematics, Faculty of Science, Mahidol University in Thailand

Publications by Krikamol Muandet

Year 2025

Conference / Medium

International Conference on Artificial Intelligence and Statistics (AISTATS) Credal Two-Sample Tests of Epistemic Uncertainty

Year 2024

Conference / Medium

International Conference on Machine Learning (ICML) Domain Generalisation via Imprecise Learning.

Article

Trans. Mach. Learn. Res. Learning Counterfactually Invariant Predictors.

Conference / Medium

International Conference on Artificial Intelligence and Statistics (AISTATS) Looping in the Human: Collaborative and Explainable Bayesian Optimization.

Conference / Medium

National Conference of the American Association for Artificial Intelligence (AAAI) Causal Strategic Learning with Competitive Selection

Year 2023

Conference / Medium

Conference on Neural Information Processing Systems (NeurIPS) A Measure-Theoretic Axiomatisation of Causality.

Conference / Medium

Conference on Neural Information Processing Systems (NeurIPS) Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models.

Article

Trans. Mach. Learn. Res. Gated Domain Units for Multi-source Domain Generalization.

Conference / Medium

International Conference on Algorithmic Learning Theory Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes