I am broadly interested in machine learning, computational mathematics and reinforcement learning.
My long-term goals are to 1) develop novel training algorithms to enhance numerical solvers for partial differential equations with deep learning (e.g., wave propagation)
2) apply deep reinforcement learning and Monte Carlo tree search strategies to game theory problems (e.g., hide-and-seek games).
Positions
2022–2026The University of Texas at AustinAustin, TX
PhD Research Scientist,
advised by Richard Tsai
2023–2024BloombergLondon, UK and New York, US
Software Engineer,
part of the Analyst Workflow Team, building financial and disruptive technology research solutions for BloombergNEF
2020University of WuerzburgWuerzburg, Germany
Research Scientist,
advised by Andreas Hotho working on reinforcement learning
2019–2020Cosmo ConsultWuerzburg, Germany
Data Scientist,
worked on ML and optimization projects for global clients
2020KPMGMunich, Germany
Data Analyst,
part of the Financial Services Team
2019University of WuerzburgWuerzburg, Germany
Research Scientist,
part of the DeepScan team to detect fraud in large-scale systems
Education
2023–2026Ph.D. in Computational Mathematics (CSEM), The University of Texas at AustinAustin, TX
Advised by Richard Tsai.
Member of the Scientific ML Group.
2021–2023M.S. in Mathematics and Computer Science, University of WuerzburgWuerzburg, Germany
Advised by Richard Tsai (UT Austin) and Christian Klingenberg.
Thesis: “Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model”.
2021–2022Exchange Program in Computer Science, The University of Texas at AustinAustin, TX
2021Exchange Program in Data Science, Norwegian School of EconomicsBergen, Norway
2017–2021B.S. in Mathematics and Economics, Technical University of Munich and University of WuerzburgWuerzburg, Germany
Advised by Andreas Hotho, Julian Tritscher, and Padraig Davidson.
Thesis: “Evaluation and Improvement of Deep Reinforcement Learning Agents for Algorithmic Trading”.