Leonard Henckel https://henckell.github.io/ Recent content on Leonard Henckel Hugo -- gohugo.io en-us Publications https://henckell.github.io/research/ Mon, 01 Jan 0001 00:00:00 +0000 https://henckell.github.io/research/ Published papers: M. Wienöbst, L. Henckel, S. Weichwald. Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning. International Conference on Learning Representations, accepted, 2026. arxiv M. Drton, L. Henckel, B. Hollering, P. Misra. Faithlessness in Gaussian graphical models. Bernoulli, 32(1):638-663, 2026. link B. Stucky , L. Henckel, M.H. Maathuis, C. Hirotsu, J. Haba-Rubio, P. Marques-Vidal, F. Siclari, R. Heinzer, and H.-P. Landolt. Community-based causal evidence that high habitual caffeine consumption alters distinct polysomnography-derived sleep. Selected Talks https://henckell.github.io/talks/ Mon, 01 Jan 0001 00:00:00 +0000 https://henckell.github.io/talks/ The World through the Lens of Causality Symposium, Tohoku University, Sendai, February 2026 Invited talk: Conceptual perspectives on defining distances between causal graphs Huawei-IHES Workshop on Causality in the Era of AI, Paris, May 2025 Invited talk: Adjustment identification distance: A gadjid for causal structure learning Mathematical Colloquium, University of Bremen, Bremen, May 2025 Invited talk: Adjustment identification distance: A gadjid for causal structure learning The 11th IMS World Congress, Bochum, August 2024 Teaching and Supervision https://henckell.github.io/teaching/ Mon, 01 Jan 0001 00:00:00 +0000 https://henckell.github.io/teaching/ Lectures: Inferential Statistics (UCD, Spring Trimester 2025/26) Statistics and Probability (UCD, Spring Trimester 2025/26) Introduction to Data Analytics (UCD, Autumn Trimester 2025/26) Inferential Statistics (UCD, Spring Trimester 2024/25) Statistics and Probability (UCD, Spring Trimester 2024/25) Introduction to Data Analytics (UCD, Autumn Trimester 2024/25) Statistics and Probability (UCD, Spring Trimester 2023/24) Causality (KU, Block 4 2022/23) PhD students: Moises Chavira Flores: Information theoretic bounds in probabilistic graphical models.