My mission is to build socially beneficial, robust, and theoretically substantiated machine learning systems.
I am a member of Pembroke College, funded by the Cambridge-Tübingen PhD fellowship with generous donations from Microsoft.
During my PhD I spent time at Deepmind and Amazon.
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.
- 12/2019: Honored to be one of the 10 "AI-newcomers" in Germany
- 07/2019: Organizing a NeurIPS workshop on Human Centric Machine Learning
- 06/2019: Improving consequential decisions under imperfect predictions @ KDD 2019 Workshop (DCCL)
- 06/2019: Convolutional neural networks: a magic bullet for gravitational-wave detection? @ Physical Review D
- 05/2019: The sensitivity of counterfactual fairness to unmeasured confounding @ UAI 2019
- 02/2019: 2nd edition of our book Quod erat knobelandum is now available at Springer [German]
Selected Publications & Projects
Fair Decisions Despite Imperfect Predictions
NK, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
shorter version: Improving consequential decision making under imperfect predictions
KDD 2019 Workshop on Data Collection, Curation, and Labeling for Mining and Learning (DCCL)
News mentions and science communication
- KI-Newcomer; MPI News (2019)
- Psychologie Heute: Der faire Algorithmus (2019)
- Die ZEIT: Wenn Maschinen kalt entscheiden (2019)
- Max Planck Forschung: Auf Fairness programmiert (2019)
- MPI news: Blind Justice -- Researchers take new approach to machine learning fairness by applying privacy methods (2018)
- The Alan Turing Institute: Can justice be blind when it comes to machine learning? (2018)
- Second Nexus: Niki Kilbertus of Max Planck Institute for Intelligent Systems Has a Plan to Remove Bias From AIs (2018)
- New Scientist: How to stop artificial intelligence being so racist and sexist (2018)
- Financial Times: Finding a fair way to tame the bigoted bots (2018)
- MPI news: The Question is Why -- Algorithms learn a Sense of Fairness (2017)
- 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
- Amazon Research (Cambridge, UK): Blind Justice: Fairness with Encrypted Sensitive Attributes
Service to the community
- Co-organized the Human Centric Machine Learning @ NeurIPS2019
- Co-organized the Privacy Preserving Machine Learning workshop @ NeurIPS2018
- Organized the CamTue workshop on Tenerife in 2018 and on Mallorca in 2017
- Regularly review for venues like ICML, NeurIPS, ICLR, FAT*, JMLR, and multiple workshops (prize for outstanding reviewing at ICML and NeurIPS)
- 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.