CV

Machine Learning Research Scientist · ETH Zurich
Email: pvlachas [at] ethz [dot] ch, or prvlachas [at] gmail [dot] com
Scholar: Google Scholar
LinkedIn: LinkedIn

Education

• Ph.D. in Computational Science and Machine Learning, ETH Zurich (2017–2022)
  Thesis: Learning and Forecasting the Effective Dynamics of Complex Systems across Scales
  Supervisor: Prof. Petros Koumoutsakos
• M.Sc. in Electrical Engineering, Technical University of Munich (2014–2016)
  GPA: 1.0/1.0 (top 1%)
• B.Sc. in Electrical Engineering, Technical University of Munich (2011–2014)
  GPA: 1.3/1.0 (top 3%)

Experience

• Research Associate, ETH Zurich, Biomedical Informatics — Prof. Gunnar Rätsch (2025–present)
  Transcriptomics-conditioned generative models combining gene foundation models with spatial omics for virtual tissue synthesis, and diffusion transformers for histopathology image synthesis.
• Research Associate, ETH Zurich, Structural Dynamics — Prof. Eleni Chatzi (2023–present)
  Developed the PHLieNets hypernetwork framework for parametric dynamical systems, enabling generalization to unseen regimes and real-time, active-learning adaptation.
• Head of Machine Learning Research, Ai2C Technologies AG (2022–2025)
  Led a 6-member ML team developing and deploying scalable ML systems for financial forecasting.
• Associate in Applied Mathematics, Harvard SEAS (2021–2022)
  Developed adaptive, real-time ML surrogates (AdaLED) for multiscale dynamical systems.
• PhD Researcher, ETH Zurich, CSE Lab (2017–2022)
  Pioneered deep learning frameworks (LED) for effective learning of multiscale dynamics.