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
09/2023: Stabilized Neural Differential Equations for Learning Constrained Dynamics @NeurIPS2023
06/2023: Jiaqi Lu joined the group as a PhD student jointly supervised with Matthias Heinig 🎉
06/2023: Supervised Learning and Model Analysis with Compositional Data @PLOS CompBio
04/2023: Sequential Underspecified Instrument Selection for Cause-Effect Estimation & Predicting Ordinary Differential Equations with Transformers @ICML2023
01/2023: Modeling Content Creator Incentives on Algorithm-Curated Platforms @ICLR2023
01/2023: Stochastic Causal Programming for Bounding Treatment Effects @CLeaR 2023
12/2022: Sparsity in Continuous-Depth Neural Networks & Predicting single-cell perturbation responses for unseen drugs + 4 workshop papers @NeurIPS2022
11/2022: Georg Manten joined the group as a PhD student 🎉
07/2022: Sören, Cecilia, and I co-organized a NeurIPS workshop on A causal view on dynamical systems
Group members
PhD students
Sören Becker
Till Richter (jointly with Fabian Theis and Yoshua Bengio)
Georg Manten
Jiaqi Lu (jointly with Matthias Heinig)
Birgit Kühbacher (jointly with Veronika Eyring)
MSc students
Nour Bouzid
Yuan Cao
Thomas Schwarz
Raveena Dandona
Yujun Wang
Omer Arshad
Jan Marco Ruiz de Vargas Staudacher
Bsc students
Dmytro Shchurovskyi
Alumni
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 fill out this form and indicate that you are interested in working with me directly. (This form is maintained by people at the Helmholtz Center, but I also use it for TUM students and supervision of external students.)
PhD students
I advertise all openings for PhD students on this homepage as well as 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 a great fit for the group.
Postdocs
If you're interested in doing a postdoc with me, I'm keen on hearing from you. Please reach out directly via email.
Teaching @TUM
Winter 23/24
Summer 23
Winter 22/23
Seminar on Causality and Machine Learning
Summer 22
Selected publications
Stabilized Neural Differential Equations for Learning Constrained Dynamics [paper]
Alistair White, NK, Maximilian Gelbrecht, Niklas BoersPredicting 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 2023 (previously: NeurIPS 2022 workshop on Causality for Real-World Impact)Discovering 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 LifeLearning Counterfactually Invariant Predictors [paper]
Francesco Quinzan*, Cecilia Casolo*, Krikamol Muandet, Yucen Luo, NK
NeurIPS 2022 workshop on Causality for Real-World ImpactSparsity 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