I hold a Master's degree in Statistics from ETH Zurich. For my Master's thesis, I worked with David Alvarez-Melis at Harvard on optimal transport for modeling single-cell dynamics. Before my PhD, I was a Data Scientist at QuantCo and interned at IBM.
My interests in machine learning span across various fields. In particular, I am interested in Optimal Transport, Probabilistic Modeling, and Causal Discovery.
You can dig into some of my most recent projects below!
Neural Unbalanced Optimal Transport for Modeling Single-Cell Dynamics
Harvard & ETH, December 2022
Spotlight Presentation at NeurIPS Workshop 'Learning Meaningful Representations of Life'
Tracking the development of cells over time is a major challenge in biology, as measuring cells usually requires their destruction. Optimal transport (OT) can help solve this challenge by learning an optimal coupling of samples taken at different points in time. In this work, we propose a novel framework for unbalanced OT.
Self-Supervised Learning on Protein Structures based on Geometric Deep Learning
ETH, February 2022
Learning on 3D protein structures is an emerging area of machine learning research with promising applications in drug discovery and material innovation. In this semester project at ETH, we explored pre-training strategies for protein structures using Graph Neural Networks.