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Im Oberen Werk 1
66386 St. Ingbert (Germany)

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Short Bio

Dr. Krikamol Muandet is tenure-track faculty (fast track) 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 2022
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 2026

Conference / Medium

The 29th International Conference on Artificial Intelligence and Statistics (AISTATS) Explanation Design in Strategic Learning: Sufficient Explanations That Induce Non-harmful Responses

Conference / Medium

Association for the Advancement of Artificial Intelligence (AAAI) Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes

Conference / Medium

International Conference on Learning Representations (ICLR) When Shift Happens - Confounding Is to Blame

Conference / Medium

International Conference on Learning Representations (ICLR) Boosting for Predictive Sufficiency

Year 2025

Conference / Medium

Conference on Neural Information Processing Systems (NeurIPS) An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation

Conference / Medium

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Sufficient Invariant Learning for Distribution Shift

Conference / Medium

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

Conference / Medium

Conference on Neural Information Processing Systems (NeurIPS) Integral Imprecise Probability Metrics

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.