Hello, I'm Jonas
I am a doctoral researcher in the Learning and Adaptive Systems Group at ETH Zürich, under the supervision of Andreas Krause.
In my research I design algorithms for online learning, sequential decision-making, optimization, and uncertainty quantification.
Prior to this, I obtained a Master's degree in Theoretical Computer Science and Machine Learning from ETH Zürich and a Bachelor's degree in Computer Science and Mathematics from the Technical University of Munich. As an intern at Citadel Securities, I have previously worked with Guillaume Basse and Sören Künzel on time-series prediction.
Most of my work can be found on GitHub or Google Scholar.
Reach out to me with things you find exciting.
Sincerely, Jonas
Notes.
Written in collaboration with Andreas Krause. Bayesian learning (Gaussian processes & Bayesian deep learning), approximate inference (variational & MCMC), active learning, Bayesian optimization, Markov decision processes, and reinforcement learning.
Undergraduate revision course covering discrete and continuous probability spaces, random variables, inductive statistics, and Markov chains.
Undergraduate revision course covering basics of Haskell, proofs of correctness by structural/computation induction and abstraction functions, I/O and monads, evaluation order.
Languages and grammars, regular and context-free languages, decidability and computability, P and NP.
Talks.
Randomized Algorithms and Probabilistic Methods Seminar. Zurich, 2022.
Computational Social Choice Seminar. Zurich, 2021.
Approximation Algorithms Seminar. Munich, 2020.
Projects.
Algorithms for online convex optimization with an associated cost for movement in the decision space. Useful for resource allocation, contextual sequence prediction, portfolio management, and object tracking.
Solutions to a wide variety of competitive programming-type questions.
A serverless web app to organize and stream media from anywhere.
Website of a landscape architecture firm.