About me

I graduated from MSc. in Advanced Computing (22-23) at Imperial College London in the Department of Computing in 2023. My interests in research are building scalable and cost-efficient machine learning systems for democratizing large-scale Deep Learning and enabling new Machine Learning applications.

Update: I recently started working as a research assistant at LSDS, Imperial as a continuation to my master’s project. I am currently looking for a PhD position starting in Fall 2024.

Summary

In real industrial scenarios, time-consuming model training makes exclusively reserving a homogeneous cloud GPU cluster expensive. My master’s thesis at Imperial College London aims at designing intuitive methods to apply automatic parallelism for large DNNs in heterogeneous GPU clusters. I was supervised by Prof. Peter Pietzuch from LSDS, ICL. It was an enlightening experience.

During my master at Imperial, I was also enrolled as an “étudiant Cursus ingénieur” (engineering student) at CentraleSupélec, University of Paris-Saclay. My passion for high-performance computing, cloud and distributed systems started there.

From Sep. 2021 to Sep. 2022, I did two internships as part of my gap year:

  • Data Scientist intern at Alstom
    I was responsible for maintaining and accelerating the cloud infrastructure (by aggregating DB/filesystem queries!) that supported the ML pipeline for predicting the status of on-board radio equipments
  • Software engineer intern at SAP France
    I developed a patch deployment tool for the version control of large-scale commercial software products.