Postdoctoral Researcher, Sorbonne University
I develop efficient algorithms for approximate computing in scientific computing and machine learning, with a focus on mixed-precision computing, and precision auto-tuning. My research emphasizes mathematical modeling, algorithm design and analysis, numerical error analysis to achieve optimal trade-offs between accuracy, performance, and energy efficiency in practical applications.
My CV
I am currently a Postdoctoral Researcher at Sorbonne University (LIP6). I was previously a Postdoctoral Researcher at Charles University. I received my PhD in Applied Mathematics from the University of Manchester.
Numerical Linear Algebra, Algorithm Analysis, Sparse Linear System Solvers and Preconditioning, Probabilistic Modeling
Graph Representation Learning, Natural Language Processing, Deep Learning
C/C++, Python, Matlab, Julia, R, Shell, TeX, CUDA
Pytorch, LLVM, Tensorflow, CMake, OpenMP, BLAS/LAPACK, Ubuntu/Mac, LaTeX, Docker
Served as a reviewer for ACM TKDD, IEEE SPL, IEEE TNSRE, ICLR, JOSS, IJF (Elsevier), Peer J Comp Sci, and Stats Comp (Springer).
Major scientific program PEPR (Programme et Équipements Prioritaires de Recherche) under the "France 2030" plan, key project of the European High-Performance Computing Joint Undertaking (EuroHPC)
Aims to change the way people design and analyze algorithms in the exascale era. Studies the combined effects of multiple sources of inexactness (e.g., approximation, lower precision) in computations to develop algorithms that are both fast and provably accurate.
Open to collaboration and discussions.
