Mathematician with extensive postdoctoral experience, specializing in the intersection of mathematics and machine learning. Background in Pure Mathematics with a PhD in Applied Mathematics, focusing on Large Deviation Theory and Gradient Flows on spaces of probability measures (a.k.a Wasserstein space). Evolved from studying abstract mathematical questions to applied fields with connections to natural sciences and machine learning.
During my career I changed various research topics in an never ending quest of finding a topic that exhibits the golden ratio between challenge and impact. Currently I am working in a group where Bayesian tools are used for machine learning and general optimization. In the last two years, I have been polishing my ML skills with an emphasis on everything related to Large Language Models (LLMs), including developing various projects (portfolio available at Metaskepsis.com) and training LLMs for reasoning (portfolio available at huggingface.co/Metaskepsis). Currently running a seminar for my entire organization (WIAS) where I educate colleagues on the uses of LLMs with the scope of facilitating new research on the topic.
Postdoctoral Researcher
Bayesian methods on optimal transport, optimal transport and rough paths, algorithms on discrete optimal transport, EVIs on spaces of measures.
Postdoctoral Researcher
Risk sensitive decision making, optimal transport and machine learning, reinforcement learning and meta-learning techniques.
Postdoctoral Researcher
Metrics on spaces of probability measures.
Postdoctoral Researcher
Large deviations for Gibbs configurations, multi-agent risk sensitive stochastic control.
Guest Postdoctoral Researcher
Solutions of the Euler equation as quasigeodesics on the Wasserstein space.
PhD in Applied Mathematics
Thesis: Wasserstein gradient flows via large deviations from thermodynamic limits of independent particle models.
Master in Pure Mathematics
Dissertation: Potential theory and Brownian motion.
Major in Pure Mathematics
Focus on Real Analysis.
My research has advanced understanding in several areas:
Extensive teaching experience across multiple institutions, including tutorials in Calculus and Linear Algebra as a PhD student. Served as coordinator of the Probability seminar at Brown University for two consecutive years. At TU-Berlin, conducted several seminars on advanced topics in Reinforcement Learning and co-organized a course where students completed projects related to Machine Learning or Computational Neuroscience.
Supervised 20+ Master's theses (2019-2024) across diverse topics in machine learning, reinforcement learning, and optimal transport applications.
