I'm a group leader at HelmholtzAI in Munich and a TUM Junior Fellow working on causality and socially beneficial machine learning looking for motivated students.

I obtained my PhD in the Cambridge-Tübingen program co-supervised by Bernhard Schölkopf, Carl Rasmussen, and Adrian Weller. I was an Ellis student and member of Pembroke College, funded by the Cambridge-Tübingen PhD fellowship with generous donations from Microsoft.

During my PhD I interned at Deepmind, Google, and Amazon.

I grew up in Austria, studied Physics and Math in Regensburg, and was fortunate to spend time at Harvard and Stanford during my studies.


Group members


Selected Publications & Projects

On component interactions in two-stage recommender systems

Jiri Hron, Karl Krauth, Michael I. Jordan, NK


NeurIPS 2021

Beyond Predictions in Neural ODEs: Identification and Interventions

Hananeh Aliee, Fabian Theis, NK


A causal view on compositional data

Elisabeth Ailer, Christian L. Müller, NK


On Disentangled Representations Learned From Correlated Data

Frederik Träuble, Elliot Creager, NK, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer


ICML 2021

Exploration in two-stage recommender systems

Jiri Hron*, Karl Krauth*, Michael I. Jordan, NK (* equal contribution)

[paper] [short talk video]

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 RL

A class of algorithms for general instrumental variable models

NK, Matt J. Kusner, Ricardo Silva

[paper] [code] [talk video]

NeurIPS 2020

Fair decisions despite imperfect predictions

NK, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera

[paper] [bibtex] [code]


The sensitivity of counterfactual fairness to unmeasured confounding

NK, Philip Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

UAI 2019

[paper] [bibtex] [code]

Convolutional neural networks: a magic bullet for gravitational-wave detection?

Timothy Gebhard*, NK*, Ian Harry, Bernhard Schölkopf (* equal contribution)

Physical Review D, 2019

[paper] [bibtex] [code] [data generation] [DOI]

Improving consequential decision making under imperfect predictions

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

NK*, Giambattista Parascandolo*, Bernhard Schölkopf (* equal contribution)

NeurIPS 2018 Workshop on Critiquing and correcting trends in machine learning


Blind Justice: Fairness with Encrypted Sensitive Attributes

NK, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller

ICML 2018

also at: FATML 2018 [talk] and PIMLAI 2018

[paper] [bibtex] [poster] [code]

Learning Independent Causal Mechanisms

Giambattista Parascandolo, NK, Mateo Rojas-Carulla, Bernhard Schölkopf

ICML 2018

also at: NIPS 2017 Workshop on Learning Disentangled Representations

[paper] [bibtex]

Avoiding Discrimination Through Causal Reasoning

NK, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf

NeurIPS 2017

[paper] [bibtex] [poster]

ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets

Timothy Gebhard*, NK*, Giambattista Parascandolo, Ian Harry, Bernhard Schölkopf (* equal contribution)

NeurIPS 2017 Workshop on Deep Learning for Physical Sciences

[paper] [bibtex] [code] [poster]

PhD Thesis

Beyond traditional assumptions in fair machine learning


PhD Thesis @ University of Cambridge

[thesis pdf (arxiv)]

Physics & Math

Universal Hydrodynamic Flow in Holographic Planar Shock Collisions

Paul Chesler, NK, Wilke van der Schee

Journal for High Energy Physics, 2015

[paper, arxiv version] [detailed project report (pdf, ~1MB)]

Master Thesis Physics: Numerical Analysis of Gravitational Wave Generation during Metric Preheating


[thesis (pdf, ~12MB)] [code]

Master Thesis Mathematics: Numerical Analysis of Causal Fermion Systems on R x S^3


[thesis (pdf, ~3.8MB)]

Physics Project: Sky-MoCa: The Skyrmion Phase in 3D Lattice Simulations


[report] [code]


Quod erat knobelandum

Clara Löh, Stefan Krauss, NK

Springer Spektrum, (1st edition: 2016, 2nd edition: 2019)

[springer] [amazon]

News mentions and science communication