Hi there, I’m Konstantin, an AI researcher at Meta and a wannabe musician. I like using mathematics to make things work in practice, especially in deep learning applications. Previously I was a research scientist at Samsung AI Center in Cambridge, UK. Beside doing research, I serve as an Action Editor for TMLR, tweet about interesting papers, and give talks about my studies. In 2023, I was lucky to receive the Outstanding Paper Award together with Aaron Defazio for our work on adaptive methods.
Before joining Samsung, I did a postdoc at Inria Sierra with Alexandre d’Aspremont and Francis Bach. I received my PhD from KAUST, where I worked under the supervision of Peter Richtárik on optimization theory and its applications in machine learning. In 2020, I also interned at Google Brain. I obtained my double degree MSc diploma from École Normale Supérieure Paris-Saclay and Paris-Dauphine, and a BSc from Moscow Institute of Physics and Technology.
My interests and hobbies tend to change every couple of years or so. Recently, I finished 6 months of evening classes at The Institute of Contemporary Music Performance where I studied electronic music production using Ableton Live. I hope to release some music online in the future.
Feel free to shoot me an email if you want to chat in person about research or music, go to a museum, or maybe just take a walk in Paris!
PhD in Computer Science, 2021
KAUST
MSc in Data Science, 2017
École normale supérieure Paris-Saclay and Paris-Dauphine
BSc in Computer Science and Physics, 2016
Moscow Institute of Physics and Technology
I’m excited to announce that I started my new job at Meta as a Research Scientist on the CodeGen team in Paris, France led by Gabriel Synnaeve.
Code generation using ML got me very excited because I was always frustrated by the amount of time it was taking me to translate my ideas into code, and it became much easier in the last couple of years. I think John Carmack said in an interview that in game development, most of code written is never read by anyone because there is just too much of it. I like to imagine systems like that in which the code is generated on the fly for different use cases, optimized, tested and debugged without us directly seeing any of that. More than anything, I just want a tool that would make programming more about designing elegant systems and solutions than actually writing or debugging them.
I am particularly excited to join Meta given their strong commitment to open source AI, which I believe is crucial for ensuring democratic access to this technology. I will keep writing and publishing papers as well as releasing my code.