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I'm a Founding Research Scientist at Zenithon AI, where I work across the machine learning stack, including representation learning, large-scale model training, and the data processing and curation pipelines supporting them. My current work focuses on developing foundation models for plasma physics. I'm also a final-year PhD candidate at Technical University of Munich, supervised by Professor Nikolaus Adams. My PhD research focuses on accelerating particle-based fluid dynamics with machine learning. In 2025, I completed research internships with the Meta FAIR Chemistry team in San Francisco and at the CAI-2 Division at Los Alamos National Laboratory. A non-exhaustive list of what currently excites me includes: large-scale machine learning for scientific applications, representation learning for physical systems, geometric deep learning, latent-space generative modeling, turbulence and plasma physics. If any of these topics resonate with you, feel free to reach out. If you want to chat, just drop me an email or connect with me on one of the following platforms: X / Google Scholar / Github / LinkedIn |
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Enhancing Diffusion-Based Sampling with Molecular Collective Variables
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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Zenithon AI, Founding Research Scientist
Los Alamos National Laboratory, Graduate Research Intern, CNLS / CAI-2
Meta FAIR Chemistry, Research Intern
Johannes Kepler University Linz, Visiting Researcher
Technical University of Munich, PhD Researcher |
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Technical University of Munich
Technical University of Munich
KAIST, Republic of Korea
Technical University of Munich |
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It was a pleasure to be a teaching assistant for the following courses at TUM.
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LagrangeBench
JAX-SPH
SciML Jupyter Book |
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Research: geometric deep learning, differentiable physics, generative modeling, uncertainty quantification, molecular and fluid simulation Tools: JAX, PyTorch, Python, Ray, Slurm, Docker, Git Academic service: reviewer for NeurIPS, ICML, ICLR, and JCP; ICLR 2026 Area Chair (Blog Post Track); NeurIPS top reviewer 2024 |
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Last update 12.08.2026 |
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