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 2016 (prospective graduation in 2020), where I am co-supervised by Bernhard Schölkopf and Carl Rasmussen. My advisor is Adrian Weller.
I am a member of Pembroke College, funded by the Cambridge-Tübingen PhD fellowship with generous donations from Microsoft.
My background is in Physics and Mathematics. I was fortunate to spend time at Harvard, working with Paul Chesler and Wilke van der Schee, as well as at Stanford, working with William East and Tom Abel.
News
- 05/2018: Two papers accepted at ICML 2018
- 04/2018: I will join Amazon for a summer internship
- 11/2017: "Learning Independent Causal Mechanisms" accepted at NIPS workshop on Learning Disentangled Representations
- 11/2017: "ConvWave" accepted at NIPS workshop on Deep Learning for Physical Sciences
- 09/2017: "Avoiding Discrimination Through Causal Reasoning" accepted at NIPS 2017
Selected Publications & Projects
Avoiding Discrimination Through Causal Reasoning
NK, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
NIPS 2017
[paper][bibtex][poster][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, NK, Mateo Rojas-Carulla, Bernhard Schölkopf
NIPS 2017 Workshop on Learning Disentangled Representations
[paper]
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 Mathematics: Numerical Analysis of Causal Fermion Systems on $\mathbb{R} \times S^3$
NK
[thesis (pdf, ~3.8MB)]
Talks
- 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
Miscellaneous
- I organized the first external CamTue workshop in November 2017 on Mallorca.
- I thoroughly enjoy teaching, was active in the Schülerzirkel Mathematik in Regensburg, a TA for many courses in Math, Physics, and CS, lectured a semi-annual course on Computer- and Microcontroller-Technology, and co-lectured the course Green-IT at the summer academy 2016 in Leysin, organized by the German Academic Scholarship Foundation.
- I like building things, for example: Babyzen - A flexible sensor BoosterPack [codeproject article][short video][report (pdf)] or some things here.
Contact
[last] [dot] [first] [at] gmail [dot] com
