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Staff AI Engineer @ Synopsys (PhysicsAI team, ex-Ansys, ex-Extrality) · Deep Learning for Physics Simulation · PhD Sorbonne (LIP6)
Staff AI Engineer and applied scientist as individual contributor at Synopsys (PhysicsAI team, ex-Ansys, ex-Extrality), where I design and ship physics surrogate deep learning models for 3D high-fidelity industrial physics simulation including Computational Fluid Dynamics (CFD), structural, and semiconductor multi-physics workflows. Six+ years of post-PhD experience bridging ML research and production of AI Physics models. PhD in Graph Neural Networks and Action Recognition in videos from Sorbonne Université (LIP6 / CNRS, MLIA team) in 2020
Pierre and Marie Curie University
Doctor of Philosophy - PhD, Graph Convolutional Neural Networks and Multiple Kernel Learning for Action Recognition in Videos
January 1, 2017 – January 1, 2020
Paris-Sud University (Paris XI)
Master’s Degree, Artificial Intelligence
January 1, 2016 – January 1, 2017
Université Paris Cité
Master’s Degree, Artificial Intelligence and machine learning for data science
January 1, 2015 – January 1, 2016
University of Bejaia - Algeria
Master’s Degree, Distributed computing and networking
January 1, 2010 – January 1, 2015
Ansys
AI, Staff Engineer, PhysicsAI, PhD
July 1, 2025 – Present
Paris · Hybrid
Ansys
Senior R&D Engineer, SimAI team, PhD
October 1, 2023 – Present
Paris · Hybrid
Extrality
Deep Learning Researcher - Geometric Deep Learning for Physics Simulation
October 1, 2020 – October 1, 2023
Greater Paris Metropolitan Region · Hybrid
LIP6 - Laboratoire d'Informatique Sorbonne Université/CNRS
PhD Student @ LIP6/CNRS, MLIA team. Machine Learning and computer vision
October 1, 2017 – September 1, 2020
Paris · On-site
Dhatim
Master 2 Intern at Dhatim
March 1, 2017 – September 1, 2017
Greater Paris Metropolitan Region · On-site
Inria
Master 1 Research Internship
June 1, 2016 – September 1, 2016
Greater Paris Metropolitan Region
Applied mathematics laboratory university of Bejaia-Algeria
Master 2 Intern
January 1, 2015 – June 1, 2015
University of Bejaia-Algeria · On-site
Statistical Inference score : 97,2%
Coursera Course Certificates
June 24, 2026 – Present
Artificial Intelligence for Robotics
Udacity
June 24, 2026 – Present
Intro to Artificial Intelligence
Udacity
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera
June 24, 2026 – Present
Introduction to Philosophy score : 88,6%
Coursera Course Certificates
June 24, 2026 – Present
Aléatoire : une Introduction aux Probabilités
Coursera
June 24, 2026 – Present
Artificial Intelligence Planning
Coursera
June 24, 2026 – Present
R Programming
Coursera
June 24, 2026 – Present
Introduction to Programming with MATLAB Score : 94.3 %
Coursera Course Certificates
June 24, 2026 – Present
Learning How to Learn: Powerful mental tools to help you master tough subjects
Coursera Course Certificates
June 24, 2026 – Present
SciWrite Writing in the Sciences score: 77%
Stanford University
June 24, 2026 – Present
Philosophy and the Sciences Score: 83.1%
Coursera Course Certificates
June 24, 2026 – Present
Søren Kierkegaard - Subjectivity, Irony and the Crisis of Modernity score : 82%
Coursera Course Certificates
June 24, 2026 – Present
Computational Neuroscience score : 94.2 %
Coursera Course Certificates
June 24, 2026 – Present
Synapses, Neurons and Brains score : 90.9%
Coursera Course Certificates
June 24, 2026 – Present
Introduction to Mathematical Philosophy Score : 83.3 %
Coursera.org - Ludwig-Maximilians-Universität München (LMU)
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily research-oriented, with significant academic and R&D roles. This aligns well with organizations that value deep technical expertise, innovation, and a research-driven approach to ML engineering. The diversity of their academic pursuits (multiple Master's, PhD) and research topics (from distributed systems to various ML applications) indicates intellectual curiosity and adaptability. Their experience at Ansys and Extrality suggests an ability to transition research into practical applications, which is crucial for an ML Engineer role.
Soft Skills & Operational Fit
The candidate's extensive research and academic background suggest strong analytical, problem-solving, and independent learning skills. The descriptions of their PhD work and internships indicate a capacity for complex problem decomposition and innovative solution development. However, without direct assessment data, specific soft skills like teamwork, leadership, or communication in a corporate setting cannot be definitively evaluated.