Pantelis R. Vlachas


Machine Learning Research Scientist, ETH Zurich

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Hi!

I’m Pantelis R. Vlachas, a Machine Learning Research Scientist at ETH Zurich. I am currently working on generative modeling for spatial transcriptomics with Kalin Nonchev at Gunnar Rätsch’s Biomedical Informatics group at ETH Zurich.

In parallel, I am affiliated with the group of Prof. Eleni Chatzi, where I am exploring hypernetworks, inverse problems, parametric modeling and adaptive surrogate models of dynamical systems with online and real-time learning capabilities.

More broadly, my research lies at the intersection of machine learning, dynamical systems, and (lately) biology. It explores how ML methods enable the prediction of complex dynamics, inference of hidden properties, and discovery of underlying structure in systems ranging from turbulent flows to biological processes. Current focus areas include deep learning, probabilistic modeling, and scientific machine learning, with an emphasis on models that generalize to unseen regimes and adapt online.

Before that, I was leading the ML Research team at AI2C Technologies for four years, a spin-off from ETH working on novel machine learning solutions for fintech.

I completed my Ph.D. under the supervision of Prof. Petros Koumoutsakos in the CSE Lab in Zurich. There I worked on novel efficient methods for learning effective dynamics of complex systems. We demonstrated the effectiveness of the developed methods in a wide range of applications, from systems that exhibit spatiotemporal chaotic behavior and turbulent flows to small molecules. During my Ph.D. I also visited the John A. Paulson School Of Engineering And Applied Sciences in Harvard as an Associate Researcher.

I am always open to chat about cool projects, ideas, and collaborations.

News

October 1, 2025 I will be giving a talk on PHLieNets at the joint 5th Symposium on Machine Learning and Dynamical Systems (MLDS 5) and Differential Equations for Data Science (DEDS 2026). The event will take place at the Research Institute for Mathematical Sciences (RIMS), Kyoto University, from February 9–13, 2026. This symposium brings together researchers at the intersection of machine learning, dynamical systems, and mathematical modeling, an ideal venue to discuss how PHLieNets can enable scalable and generalizable learning across parametric dynamical systems.
July 1, 2025 I will be presenting a poster at the Greeks in AI Symposium 2025, which will take place at Serafeio City of Athens, Greece, on July 19–20, 2025. The poster will showcase PHLieNets, a framework for learning parametric dynamical systems through hypernetworks and latent embeddings for long-term forecasting.
June 23, 2025 I am pleased to announce that I will be co-organising a mini-symposium titled “Data-driven Methods in Complex Dynamical Systems” at Dynamics Days Europe 2025, to be held in Thessaloniki, Greece, 23-27 June.
January 23, 2025 I presented our work on Adaptive Learning of Effective Dynamics (AdaLED) at the 16th Conference on Dynamical Systems Applied to Biology and Natural Sciences (DSABNS16), held at the Università degli Studi di Napoli “Federico II”, Naples, Italy.
February 1, 2024 I presented our work on Adaptive Learning of Effective Dynamics (AdaLED) at the Differential Equations for Data Science 2024 (DEDS2024) conference, held online on February 19–21, 2024.