I'm a professor at the Technical University of Munich in the Informatics Department and a group leader at Helmholtz AI working on causality and socially beneficial machine learning. I'm also part of MCML, the Konrad Zuse School relAI, and ELLIS (and the Munich Unit).
I obtained my PhD in the Cambridge-Tübingen program (with generous donations from Microsoft) as an Ellis student and member of Pembroke College. During my PhD I interned at Deepmind, Google, and Amazon.
I grew up in Austria, studied Physics and Mathematics in Regensburg, and was fortunate to spend time at Harvard and Stanford during my studies.
News
09/2024: Targeted Sequential Indirect Experiment Design accepted @NeurIPS
09/2024: Nora Schneider joined the group as ELLIS PhD student jointly supervised with Mihaela van der Schaar 🎉
06/2024: Learning counterfactually invariant predictors accepted @TMLR
05/2024: I am now member of the Junge Akademie
04/2024: I was awarded the Leopoldina Prize for Young Scientists
04/2024: Causal machine learning for predicting treatment outcomes @Nature Medicine
01/2024: ODEFormer: Symbolic Regression of Dynamical Systems with Transformers & Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation @ICLR2024
09/2023: Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints @NeurIPS2023
Group members
PhD students
Sören Becker
Georg Manten
Till Richter (jointly with Fabian Theis and Yoshua Bengio)
Jiaqi Lu (jointly with Matthias Heinig)
Birgit Kühbacher (jointly with Veronika Eyring)
Alumni
Thomas Schwarz (MSc student)
Alejandro Hernandez Artiles (MSc student)
Florian Hautmann (MSc student)
Dmytro Shchurovskyi (BSc student)
Katharina Hagedorn (BSc student)
Yuan Cao (MSc student)
Raveena Dandona (MSc student)
Yujun Wang (MSc student)
Omer Arshad (MSc student)
Jan Marco Ruiz de Vargas Staudacher (MSc student)
Franz Srambical (BSc student)
Ronald Skorobogat (BSc student)
Luca Eyring (MSc student)
Michal Klein (MSc student)
Simon Böhm (MSc student)
Alexander Reisach (MSc student)
Prospective students
MSc, BSc, HiWi, guided research, internships
If you are interested in doing your MSc or BSc thesis with me or a guided research project (only for TUM students) please email me directly with a CV, current transcripts, and some directions you would be interested in.
PhD students
I advertise all openings for PhD students on this homepage and on X. If there are no current openings, please feel free to reach out directly via email and let me know why you'd be an excellent fit for the group.
Please consider applying to the current MCML call for PhD positions. [deadline: 2024-11-14]
Please consider applying to the upcoming relAI call for PhD positions. [application: 2024-12/2025-01]
Postdocs
If you're interested in doing a postdoc with me, I'm keen to hear from you. Please reach out directly via email.
Teaching @TUM
Winter 24/25
Selected publications
Generative Intervention Models for Causal Perturbation Modeling [paper]
Nora Schneider, Lars Lorch, NK, Bernhard Schölkopf, Andreas KrauseLearning Representations of Instruments for Partial Identification of Treatment Effects [paper]
Jonas Schweisthal, Dennis Frauen, Maresa Schröder, Konstantin Hess, NK, Stefan FeuerriegelUncertainty-Aware Optimal Treatment Selection for Clinical Time Series [paper] (oral)
Thomas Schwarz, Cecilia Casolo, NK
NeurIPS workshop on Causal Representation Learning (CLR) 2024Projected Neural Differential Equations for Power Grid Modeling with Constraints [paper]
Alistair White, Anna Büttner, Maximilian Gelbrecht, NK, Frank Hellmann, Niklas Boers
NeurIPS workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers (D3S3) 2024Targeted Sequential Indirect Experiment Design [paper]
Elisabeth Ailer, Niclas Dern, Jason Hartford, NK
NeurIPS 2024Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity [paper]
Zhufeng Li, Sandeep S Cranganore, Nicholas Youngblut, NK
ICLR workshop on Machine Learning for Genomics Explorations (MLGenX) 2024Towards Physically Consistent Deep Learning For Climate Model Parameterizations [paper] (oral)
Birgit Kühbacher, Fernando Iglesias-Suarez, NK, Veronika Eyring
ICMLA 2024Learning Counterfactually Invariant Predictors [paper]
Francesco Quinzan*, Cecilia Casolo*, Krikamol Muandet, Yucen Luo, NK
TMLR 2024Causal machine learning for predicting treatment outcomes [paper]
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Alicia Curth, Stefan Bauer, NK, Isaac S. Kohane, Mihaela van der Schaar
Nature Medicine 2024 (perspective)Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes [paper]
Georg Manten*, Cecilia Casolo*, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, NK
UAI workshop on Causal Inference for Time Series (CI4TS) 2024Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation [paper]
Luca Eyring*, Dominik Klein*, Théo Uscidda, Giovanni Palla, NK, Zeynep Akata, Fabian Theis
ICLR 2024ODEFormer: Symbolic Regression of Dynamical Systems with Transformers [paper] [code] (spotlight)
Stéphane d'Ascoli*, Sören Becker*, Alexander Mathis, Philippe Schwaller, NK
ICLR 2024Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints [paper][code]
Alistair White, NK, Maximilian Gelbrecht, Niklas Boers
NeurIPS 2023Predicting Ordinary Differential Equations with Transformers [paper][code coming soon]
Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, NK
ICML 2023Sequential Underspecified Instrument Selection for Cause-Effect Estimation [paper][code] (oral)
Elisabeth Ailer, Jason Hartford, NK
ICML 2023Modeling Content Creator Incentives on Algorithm-Curated Platforms [paper] (notable-top-5%)
Jiri Hron*, Karl Krauth*, Michael I. Jordan, NK, Sarah Dean
ICLR 2023Supervised Learning and Model Analysis with Compositional Data [paper] [code]
Shimeng Huang, Elisabeth Ailer, NK, Niklas Pfister
PLOS CompBio 2023Stochastic Causal Programming for Bounding Treatment Effects [paper][code] (oral)
Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, NK
CLeaR 2023Discovering ordinary differential equations that govern time-series [paper]
Sören Becker*, Michal Klein*, Alexander Neitz, Giambattista Parascandolo, NK
NeurIPS 2022 workshop on AI4ScienceModeling Single-Cell Dynamics Using Unbalanced Parameterized Monge Maps [paper]
Luca V. Eyring, Dominik Klein, Giovanni Palla, Sören Becker, Philipp Weiler, NK, Fabian Theis
NeurIPS 2022 workshop on Learning Meaningful Representations of LifeSparsity in Continuous-Depth Neural Networks [paper][code]
Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian Theis, NK
NeurIPS 2022Predicting single-cell perturbation responses for unseen drugs [paper] [code]
Leon Hetzel*, Simon Böhm*, NK, Stephan Günnemann, Mohammad Lotfollahi, Fabian Theis (* equal contribution)
NeurIPS 2022Multi-disciplinary fairness considerations in machine learning for clinical trials [paper]
Isabel Chien, Nina Deliu, Richard Turner, Adrian Weller, Sofia Villar, NK
FAccT 2022Numerical Analysis of the Causal Action Principle in Low Dimensions [paper]
Felix Finster, Robert H. Jonsson, NKOn component interactions in two-stage recommender systems [paper]
Jiri Hron*, Karl Krauth*, Michael I. Jordan, NK
NeurIPS 2021Beyond Predictions in Neural ODEs: Identification and Interventions [paper]
Hananeh Aliee, Fabian Theis, NKA causal view on compositional data [paper][code]
Elisabeth Ailer, Christian L. Müller, NKOn Disentangled Representations Learned From Correlated Data [paper]
Frederik Träuble, Elliot Creager, NK, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer
ICML 2021Exploration in two-stage recommender systems [paper] [short talk video]
Jiri Hron*, Karl Krauth*, Michael I. Jordan, NK (* equal contribution)
ACM RecSys 2020 Workshop on Bandit and Reinforcement Learning from User Interactions (REVEAL 2020)
NeurIPS 2020 Workshop on Consequential Decisions in Dynamic Environments
NeurIPS 2020 Workshop on Challenges of Real-World RLA class of algorithms for general instrumental variable models [paper] [code] [talk video]
NK, Matt J. Kusner, Ricardo Silva
NeurIPS 2020Fair decisions despite imperfect predictions [paper] [bibtex] [code]
NK, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
AISTATS 2020The sensitivity of counterfactual fairness to unmeasured confounding [paper] [bibtex] [code]
NK, Philip Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
UAI 2019Convolutional neural networks: a magic bullet for gravitational-wave detection? [paper] [bibtex] [code] [data generation] [DOI]
Timothy Gebhard*, NK*, Ian Harry, Bernhard Schölkopf (* equal contribution)
Physical Review D 2019Improving consequential decision making under imperfect predictions [paper]
NK, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
KDD 2019 Workshop on Data Collection, Curation, and Labeling for Mining and Learning (DCCL)Generalization in anti-causal learning [paper]
NK*, Giambattista Parascandolo*, Bernhard Schölkopf (* equal contribution)
NeurIPS 2018 Workshop on Critiquing and correcting trends in machine learningBlind Justice: Fairness with Encrypted Sensitive Attributes [paper] [bibtex] [poster] [code]
NK, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller
ICML 2018Learning Independent Causal Mechanisms [paper] [bibtex]
Giambattista Parascandolo, NK, Mateo Rojas-Carulla, Bernhard Schölkopf
ICML 2018Avoiding Discrimination Through Causal Reasoning [paper] [bibtex] [poster]
NK, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
NeurIPS 2017ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets [paper] [bibtex] [code] [poster]
Timothy Gebhard*, NK*, Giambattista Parascandolo, Ian Harry, Bernhard Schölkopf (* equal contribution)
NeurIPS 2017 Workshop on Deep Learning for Physical SciencesUniversal Hydrodynamic Flow in Holographic Planar Shock Collisions [paper, arxiv version] [detailed project report]
Paul Chesler, NK, Wilke van der Schee
Journal for High Energy Physics, 2015
PhD thesis
Beyond traditional assumptions in fair machine learning [thesis pdf (arxiv)]
PhD Thesis @ University of Cambridge
Book
Quod erat knobelandum [springer] [amazon]
Clara Löh, Stefan Krauss, NK
Springer Spektrum (1st edition: 2016, 2nd edition: 2019)
Theses and projects
Master Thesis Physics: Numerical Analysis of Gravitational Wave Generation during Metric Preheating [thesis] [code]
Master Thesis Mathematics: Numerical Analysis of Causal Fermion Systems on R x S^3 [thesis]
Physics Research Project: Sky-MoCa: The Skyrmion Phase in 3D Lattice Simulations [report] [code]
Activities
Co-organizing a workshop on A causal view on dynamical systems @ NeurIPS2022
Co-organized a workshop on Machine Learning meets Econometrics @ NeurIPS2021
Co-organized a workshop on the Neglected Assumptions in Causal Inference @ ICML2021
Co-organized a workshop on Consequential Decisions in Dynamic Environments @ NeurIPS2020
Co-organized the Human Centric Machine Learning workshop @ NeurIPS2019
Co-organized the Privacy Preserving Machine Learning workshop @ NeurIPS2018
Organized the CamTue workshop on Tenerife in 2018 and on Mallorca in 2017