I am a Senior Data Scientist at the Swiss Data Science Center. In my research, I develop principled and practical algorithms for sequential decisionmaking. I also work on combining stateoftheart deep learning and sequential decisionmaking methods in challenging realworld applications.
Before I completed a PostDoc with Csaba Szepesvári at the University of Alberta (supported by an Early Postdoc.Mobility fellowship of the Swiss National Science Foundation). I hold a PhD in machine learning that I completed under the supervision of Andreas Krause at ETH Zurich. In 2020, I completed a research internship at DeepMind. Prior, I received Bachelor and Master of Science degrees in Mathematics at ETH Zurich.
My research interests include reinforcement learning, adaptive experimental design, Bayesian optimization, robustness and safety, as well as deep learning and computer vision. I am equally excited about creating mathematical foundations for learning algorithms and bringing their value to realworld application. See below for some of my research highlights.
News
Nov 1, 2023 
Two papers accepted at NeurIPS!


Aug 21, 2023  I am starting a new position as a Sr. Data Sciencist at the Swiss Data Science Center in Zurich. Ping me if you like to catch up! 
Aug 13, 2023  Our paper on Linear Partial Monitoring for Sequential DecisionMaking: Algorithms, Regret Bounds and Applications, together with Tor Lattimore and Andreas Krause, got accepted for publication at JMLR. 
May 1, 2023  I am helping to resurrect the reinforcement learning online seminars. Join us for exciting talks and engaging discussions! 
Jan 20, 2023 
Two papers accepted:

Feb 1, 2022  I am serving as Associate Chair at ICML 2022 
Aug 1, 2021  Started my PostDoc at the University of Alberta 
May 17, 2021  Successfully defended my PhD thesis! 
Dec 15, 2020  I received an SNF Early PostDoc.Mobility Fellowship 
Research Highlights
Safe Bayesian Optimization for Particle Accelerators
Together with collaboraters at PSI and ETH Zurich, I developed safe datadriven tuning algorithms for particle accelerators. Manually adjusting machine parameters is a reoccuring and time consuming task that is required on many acceletors and cuts down valuable time for experiments. A main difficulty is that all adjustments need to respect safety parameters to avoid damaging the machines (or trigger automated shutdown procedures). We successfully deployed our methods on two major experimental facilities at PSI, the High Intensity Proton Accelerator (HIPA) and the Swiss Free Electron Laser (SwissFEL).
Frequentist Analysis of InformationDirected Sampling
In my PhD thesis, I pioneered mathematical foundations of informationdirected sampling (IDS), an algorithm design principle proposed by Daniel Russo and Benjamin Van Roy. Together with Tor Lattimore and Andreas Krause, I showed that the algorithm applies much more broadly to linear partial monitoring (and is provably nearoptimal in all finiteaction settings). More recently, I showed that IDS is also asymptotically optimal (together with Claire Vernade, Tor Lattimore and Csaba Szepesvári). This resolves an open problem in the literatue. It is also a remarkable result, because IDS was never explicitly designed for this regime.