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I'm a PhD candidate at Technical University of Munich since April 2021 where I am supervised by Professor Nikolaus Adams. My PhD research aims at accelerating particle-based fluid dynamics with machine learning. Starting in May 2025, I will be first interning on the Meta FAIR Chemistry team, and then at the CCS-2 Division at Los Alamos National Laboratory. A non-exhaustive list of what currently excites me includes: large-scale physics-based machine learning, geometric deep learning, latent-space generative modeling, atomistic materials design and synthesis, turbulence modeling, and uncertainty quantification. If any of these topics resonate with you, feel free to reach out at any time. I am presently in San Francisco, and always happy to meet for a coffee. More about me can be found in my resume. If you want to chat, just drop me an email or connect with me on one of the following platforms. Email / X / Google Scholar / Github / LinkedIn |
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LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities
Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
On Learning Quasi-Lagrangian Turbulence
UPT++: Latent Point Set Neural Operators for Modeling System State Transitions
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks
E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods |
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It was a pleasure to be a teaching assistant for the following courses at TUM.
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Last update 05.08.2025 |
Template by Jon Barron. |