I am starting my second year of Master at IP Paris. This year my Master program is based on Data Science program of Ecole Polytechnique.

In our new paper we prove that a class of so called Three Point Compressors can be applied on Newton method. This result opens up a wide variety of communication strategies, such as a combination of contractive compression and lazy aggregation, available to our disposal to compress prohibitively costly curvature information.

I am very proud to announce that our FedNL paper was accepted to 39th International Conference on Machine Learning (ICML 2022).

Currenly I am in Lausanne. Here I have started my internship at Machine Learning and Optimization Lab, EPFL run by Prof. Martin Jaggi. I will work under the supervision of postdoctoral fellow Hadrien Hendrikx and Prof. Martin Jaggi.

Our paper: Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning, joint work with Xun Qian, Mher Safaryan, and Peter Richtàrik, was accepted to AISTATS 2022.