Hello! I am an ML Scientist currently working at Nike in the Search team, in charge of Matching, Ranking and Generative AI initiatives. Before that, I was a Research Scientist in the Cognitive Computing Lab at Baidu Research led by Dr. Ping Li. I obtained my Ph.D. in Machine Learning at Ecole Polytechnique and INRIA under the supervision of Marc Lavielle and Eric Moulines.
- Layer-wise and Dimension-wise Locally Adaptive Federated Learning [UAI, 2023]
- On the Convergence of Decentralized Adaptive Gradient Methods [ACML, 2022]
- FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems [IEEE BigData, 2022]
- On Distributed Adaptive Optimization with Gradient Compression [ICLR, 2022]
- Variational Flow Graphical Model [KDD, 2022]
- Dual Energy-Flow Enhanced Graph Neural Network for Visual Question Answering [ICME, 2022]
- An Optimistic Acceleration of AMSGrad for Nonconvex Optimization [ACML, 2021]
- Towards Better Generalization of Adaptive Gradient Methods [NeurIPS, 2020]
- On the global convergence of (fast) incremental expectation maximization methods [NeurIPS, 2019]
- Non-asymptotic Analysis of Biased Stochastic Approximation Scheme [COLT, 2019]
- Saemix: Open Source R package for nonlinear mixed effects modeling. [Project Page] [Case Studies]
- Brainattic: Video search engine and automatic trailer generation using Embeddings-based modeling.
- Monk AI: AI Powered Vehicle Inspection solutions for the automotive, insurance and mobility markets.