Niki Kilbertus

My mission is to build socially beneficial, interpretable and theoretically substantiated machine learning systems.

I started my PhD in the Cambridge-Tübingen program in machine learning in October 2017, where I am co-supervised by Bernhard Schölkopf at the Max Planck Institute for Intelligent Systems and Carl Rasmussen and Adrian Weller in the machine learning group at the University of Cambridge. I am a member of Pembroke College and funded by the Cambridge-Tübingen PhD fellowship with generous donations from Microsoft. My prospective graduation year is 2020.

Prior, I obtained an M.Sc. in both Mathematics and Physics from the University of Regensburg. During my studies I spent time at the High Energy Theory Group at Harvard, where I worked with Paul Chesler and Wilke van der Schee on simulating holographic planar shock collisions, as well as at the Kavli Institute for Particle Astrophysics and Cosmology at Stanford, where I worked with William East and Tom Abel on simulations of gravitational wave formation during preheating.

Publications & Projects

Avoiding Discrimination Through Causal Reasoning

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

NIPS 2017

[paper][bibtex][in the press: MPI][related grant: Digital Impact Grant by Stanford PACS]

ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets

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

NIPS 2017 Workshop on Deep Learning for Physical Sciences


Learning Independent Causal Mechanisms

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

NIPS 2017 Workshop on Learning Disentangled Representations


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)]

Quod erat knobelandum

Clara Löh, Stefan Krauss, NK

Springer Spektrum, 2016


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 $\mathbb{R} \times S^3$


[thesis (pdf, ~3.8MB)]

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




  • Albert Einstein Institute (Potsdam-Golm, Germany): Machine Learning powered CBC Search
  • Alan Turing Institute (London, UK): Fairness in Machine Learning
  • Max Planck Institute for Software Systems (Saarbrücken, Germany): Fairness in Machine Learning
  • Stanford University (CA, USA): Searching for Gravitational Waves with Machine Learning
  • University of Regensburg (Regensburg, Germany): Fully Convolutional Networks for Gravitational Wave Searches
  • Microsoft Research (Cambridge, UK): Learning Independent Causal Mechanisms



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