Victor Cohen

Victor Cohen

Senior Data Scientist
Academia
(Alumni)

Victor has joined the SDSC in 2020 to design solutions for data-driven optimization problems. His research interests lie at the crossroad of machine learning and decision-making. This contains several topics such as stochastic optimization, reinforcement learning, combinatorial optimization, and probabilistic graphical models. Victor received a PhD in operations research and machine learning from Ecole des Ponts Paristech in 2020. Before that, he completed a master degree in Operation Research and Machine learning at Ecole des Ponts Paristech and a bachelor degree in Applied Mathematics and Computer Sciences.

Projects

DS4MS

Completed
Data Science for Multiplexing Spectrometers
Big Science Data

SMARTAIR

Completed
Self-guided Machine Learning Algorithms for Real-Time Assimilation, Interpolation and Rendering of Flow Data
Engineering

PolyNet

Completed
Exploring disease trajectories and outcome prediction using novel methods in network analysis and machine learning
Biomedical Data Science

Publications

Humml, J. M.; Cohen, V.; Perez-Cruz, F.; Gharib, M.; Rösgen, T. "Augmented Reality Guided Aerodynamic Sampling" AIAA SCITECH 2024 Forum 2024 View publication
Cohen, V.; Parmentier, A. "Future memories are not needed for large classes of POMDPs" Operations Research Letters 51 3 270-277 2023 View publication
Goshtasbpour, S.; Cohen, V.; Perez-Cruz, F.; Krause, A.; Brunskill, E.; Cho, K.; Engelhardt, B.; Sabato, S.; Scarlett, J. "Adaptive Annealed Importance Sampling with Constant Rate Progress" Proceedings of the 40th International Conference on Machine Learning 202 11642–11658 2023 View publication
Parmentier, A.; Cohen, V.; Leclère, V.; Obozinski, G.; Salmon, J. "Integer Programming on the Junction Tree Polytope for Influence Diagrams" INFORMS Journal on Optimization 2 3 209-228 2020 View publication

Mentioned in

May 1, 2024

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.

Case Studies

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