Updates
2024
07/2024 - Deep Backtracking Counterfactuals for Causally Compliant Explanations accepted at TMLR.
07/2024 - I gave a lecture on Causal Representation Learning at the OxML summer school on MLx Health & Bio.
05/2024 - Pleased to receive the 2024 Cambridge G-Research PhD Prize in Quantitative Research.
05/2024 - A Sparsity Principle for Partially Observable Causal Representation Learning accepted at ICML.
04/2024 - I gave an invited talk on causal representation learning at the CAUSE Junior Researcher Day at TU Munich.
03/2024 - I visited Portugal to give a Tutorial on "Causality for ML" at the INVICTA Spring School (Porto) and at the Champalimaud Centre for the Unknown (Lisbon)
03/2024 - I've started my postdoc with Jonas Peters at the Seminar for Statistics of ETH Zürich.
02/2024 - I successfully defended my PhD thesis titled "Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment" (pass with no corrections)
01/2024 - Multi-View Causal Representation Learning with Partial Observability accepted at ICLR (spotlight).
2023
12/2023 - I gave an invited talk at the Causal Representation Learning Workshop at NeurIPS, here are my [slides].
09/2023 - With fantastic co-authors, 3 papers accepted at NeurIPS:
05/2023 - Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators accepted at UAI.
04/2023 - Provably Learning Object-Centric Representations accepted at ICML (oral).
04/2023 - We organised a Workshop on Causal Representation Learning in Tübingen.
04/2023 - Our work on Backtracking Counterfactuals received the Best Paper Award at CLeaR.
03/2023 - Evaluating vaccine allocation strategies using simulation-assisted causal modelling accepted at Patterns (CellPress)
01/2023 - DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability accepted at ICLR
01/2023 - With great collaborators, 2 papers accepted at CLeaR:
01/2023 - Had a great time at the Bellairs Workshop on Causality: Inference and Representation Learning.
2022
09/2022 - With great collaborators, 4 papers accepted at NeurIPS:
09/2022 - I have been awarded a Google PhD Fellowship in Machine Learning; huge thanks to all my collaborators and mentors for their support!
09/2022 - I will help co-organise CLeaR 2023 in Tübingen as Social Chair; consider submitting your work.
09/2022 - I gave a long talk about our work on identifying invariant representations with self-supervised learning from data augmentation at the Causality Discussion Group, check out the recording.
08/2022 - Slides and recording from our UAI'22 Causal Representation Learning Workshop are now available on the workshop website.
06/2022 - I gave a talk about Active Bayesian Causal Inference at the International Society for Bayesian Analysis (ISBA) World Meeting in Montreal.
05/2022 - I gave a lecture on "Introduction to Causal Inference" at the Gdańsk Machine Learning Summer School; here are my slides.
05/2022 - "Causal Inference Through the Structural Causal Marginal Problem" accepted at ICML, co-led with Luigi Gresele & Jonas Kübler.
04/2022 - Together with Amir Karimi, we've written a book chapter "Toward Causal Algorithmic Recourse" as part of the Springer LNAI 13200 "xxAI - Beyond Explainable AI".
04/2022 - Together with Bernhard Schölkopf, we've written a review "From Statistical to Causal Learning", to appear in the Proceeding of the International Congress of Mathematicians.
03/2022 - We are organising a Workshop on Causal Representation Learning at UAI'22 in Eindhoven, The Netherlands on 5 August (in-person/hybrid). Check out the website for details about the workshop and a list of selected references.
03/2022 - I have started my academic visit(s) in the US, where I will be for the next four months:
1 month at the Simons Institute at UC Berkeley as a Visiting Student Researcher to participate in the "Causality" program;
3 months at Columbia University, New York to work with Dave Blei and Elias Bareinboim.
03/2022 - "Complex interlinkages, key objectives and nexuses amongst the Sustainable Development Goals and climate change: a network analysis" accepted at The Lancet Planetary Health, led by the excellent Felix Laumann.
01/2022 - With a great team of co-authors, 2 papers accepted at ICLR:
2021
12/2021 - I gave an invited talk at the WHY-21 NeurIPS Workshop "Causal Inference & Machine Learning: Why now?": links to slides and recording.
12/2021 - "On the Fairness of Causal Algorithmic Recourse" accepted at AAAI (oral).
10/2021 - In this podcast [Spotify] [mpg] and this article (p.24 ff; German / English), I speak about the role of causality for AI.
09/2021 - With amazing co-authors, 3 papers accepted at NeurIPS:
08/2021 - I will help organise the 1st Conference on Causal Learning and Reasoning (CLeaR) as part of the Logistics and Conference Planning Team.
08/2021 - "Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP" accepted at EMNLP (oral).
04/2021 - "Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects" accepted at IEEE Transactions on Artificial Intelligence
03/2021 - Pleased to receive an Outstanding Reviewer Award from ICLR
2020
09/2020 - "Algorithmic recourse under imperfect causal knowledge: a probabilistic approach" accepted at NeurIPS (spotlight)
06/2020 - Joining Amazon's research lab in Tübingen as part-time Applied Science Intern to continue earlier work on object centric representation learning and causal generative models.
05/2020 - "Semi-supervised learning, causality and the conditional cluster assumption" accepted at UAI