Nico Thiebaut

Drawn to theoretical physics, Nico Thiebaut became an instructor at the University of Paris-Saclay, pursuing a PhD in quantum physics. Machine learning soon caught his fascination, leading him to a three-year exploration as a data scientist. Nico crafted an enriching online big data course and a dynamic deep learning program for master's students.

A 2018 move to San Francisco sparked a new chapter, where Nico was working with a startup before leading a machine learning team for a recruitment marketplace. He now enhances virtual worlds as a machine learning engineer at Roblox.

From quantum physics to machine learning interpretability, Nico's research path has been prolific, including contributions to medical research as a statistician.

By joining the Master's of Science in data science at USF, Nico continues to nurture his unquenchable passion for teaching, bridging the elegant intricacy of technology with the human quest for understanding.

• Explainable machine learning
• Natural language processing
• Deep learning

• Machine learning
• Statistics for medical sciences
• Theoretical physics

• University of Paris-Saclay, PhD in Theoretical Physics, 2015
• Ecole Normale Superieur, MA in Condensed Matter Physics, 2011

• Senior Machine Learning Engineering, Roblox
• Adjunct Professor in Deep Learning, University of Technology of Troyes
• Machine Learning Engineering Manager, Hired

• Breast cancer in elderly women and altered clinico-pathological characteristics: a systematic review
• Countergan: Generating realistic counterfactuals with residual generative adversarial nets
• Fractional quantum Hall states versus Wigner crystals in wide quantum wells in the half-filled lowest and second Landau levels
• Relevance of breast MRI in determining the size and focality of invasive breast cancer treated by mastectomy: a prospective study
• Two-component fractional quantum Hall effect in the half-filled lowest Landau level in an asymmetric wide quantum well