I am a research scientist at INRIA Paris and the DI ENS (computer science department of Ecole Normale Supérieure de Paris) in the Argo team, and a part-time associate professor (chargé de cours) at Ecole Polytechnique. My research lies at the intersection of machine learning, optimization and graph theory. Prior to that, I was a research scientist in Huawei’s machine learning lab in Paris leading the optimization for ML project-team. I did a post-doc in distributed optimization at the Microsoft Research – Inria Joint Center, and obtained my PhD in Applied Mathematics from the Ecole Normale Supérieure Paris-Saclay under the supervision of Nicolas Vayatis. In 2018, I received a best paper award of the 2018 NeurIPS conference for my work on distributed optimization.
Education
- 2013 - 2016
PhD in Machine Learning
Ecole Normale Supérieure Paris-Saclay - 2011 - 2012
Msc in Machine Learning (MVA)
Ecole Polytechnique - 2008 - 2011
Eng. deg in Applied Mathematics
Ecole Polytechnique
Experience
- 2022 - now
Part-time associate professor
Ecole Polytechnique, Saclay - 2021 - now
Researcher
INRIA and ENS, Paris - 2018 - 2021
Principal researcher
Huawei Noah's Ark, Paris - 2017 - 2018
Post-doctoral researcher
Microsoft Research - INRIA joint center, Saclay
Awards
- 2018
Best paper award (4 out of 4865 submissions)
NeurIPS 2018, Montréal - 2020
Individual gold medal award
Huawei, Paris - 2019
Outstanding contributions individual award
Huawei, Paris - 2018
Future star award
Huawei, Paris
Research
My field of study is at the intersection of Machine Learning, structured data analysis (graphs, time series) and optimization. A common theme of my research consists in analyzing the impact of structure in data (spatial proximity, time dependency or item correlations) and using it for ML purposes (training large-scale machine learning models, improving low-dimensional representations of graphs, identifying patterns from malware traces, or predicting the outcome of information cascades).
Teaching
- 2023-2024
Mathematics of Deep Learning [course material]
M2 MASH, PSL, Paris - 2023-2024
Deep Learning [course material]
ENSAE, Saclay - 2023-2024
Deep Learning
ENS, Paris - 2023-2024
Deep Learning
Ecole Polytechnique, Saclay - 2022-2023
Machine Learning 2
Master X-HEC, Ecole Polytechnique, Saclay - 2022-2023
Capstone projects
M2 Data Science, Ecole Polytechnique, Saclay - 2022-2023
Mathematics of Deep Learning [course material]
M2 MASH, PSL, Paris - 2022-2023
Deep Learning
ENS, Paris - 2021-2022
Deep Learning (mathematics track)
ENS, Paris - 2021-2022
Structures et Algorithmes Aléatoires (TDs)
ENS, Paris
Students supervision
I have had the pleasure to supervize the work of amazing students and junior researchers.
Contact
Email: kevin.scaman@inria.fr
Office: INRIA Paris, 2 rue Simone IFF, 75012 Paris