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, where I am co-supervised by Bernhard Schölkopf at the MPI for Intelligent Systems and Carl Rasmussen and Adrian Weller at the University of Cambridge. I am a member of Pembroke College, funded by the Cambridge-Tübingen PhD fellowship with generous donations from Microsoft. My prospective graduation year is 2020.
My background is in Physics and Mathematics. During my studies I spent time at Harvard, where I worked with Paul Chesler and Wilke van der Schee on simulating holographic planar shock collisions, as well as at Stanford, where I worked with William East and Tom Abel on simulating gravitational wave formation during preheating.
Selected Publications & Projects
- 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
- University of Cambridge (Cambridge, UK): Introduction to the Minimum Description Length Principle
- 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.