Konstantin Mishchenko
Konstantin Mishchenko is an optimization researcher whose work on learning-rate-free methods lets neural networks train without hand-tuned step sizes. He earned a BSc from the Moscow Institute of Physics and Technology in 2016, a master's degree in Paris in 2017, and a PhD at KAUST in 2021 under Peter Richtárik. After a postdoc at Inria Sierra and research at the Samsung AI Center in Cambridge, he won an ICML Outstanding Paper Award in 2023 with Aaron Defazio for D-Adaptation, then followed it with Prodigy in 2023 and Schedule-Free learning in 2024. He is a research scientist at Meta in Paris, working on code generation.
Timeline
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2016–2017
Degrees in Moscow and Paris
He earns a BSc in computer science and physics from the Moscow Institute of Physics and Technology in 2016. In 2017, he completes a double-degree MSc in data science at École normale supérieure Paris-Saclay and Paris-Dauphine.
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2017–2021
A PhD at KAUST
He works at KAUST under Peter Richtárik on optimization theory and its applications in machine learning, and he completes his computer science PhD in 2021 with a thesis titled On Seven Fundamental Optimization Challenges in Machine Learning. In 2020, he interns at Google Brain.
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December 2021–December 2022
Postdoc at Inria Sierra
He is a postdoc at Inria Sierra in Paris with Alexandre d'Aspremont and Francis Bach, researching adaptive, second-order, and distributed optimization.
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January 2023–October 2024
Research scientist at Samsung
At the Samsung AI Center in Cambridge, UK, he works on embedded AI systems as a member of the Distributed AI team and the GenAI initiative. His adaptive optimization methods for automated training appear at ICML 2023 and ICML 2024.
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2023
ICML Outstanding Paper Award
Learning-Rate-Free Learning by D-Adaptation, written with Aaron Defazio, receives an Outstanding Paper award at ICML 2023. The method matches hand-tuned learning rates across more than a dozen machine learning problems without knowing the distance from the starting point to the solution.
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June 2023
The Prodigy optimizer
In June 2023, he and Aaron Defazio post Prodigy, a modification of D-Adaptation that improves its convergence rate and estimates the learning rate for methods such as AdaGrad and Adam.
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May 2024
The Road Less Scheduled
In May 2024, he coauthors The Road Less Scheduled with Aaron Defazio, Xingyu Alice Yang, Harsh Mehta, Ahmed Khaled and Ashok Cutkosky, which introduces Schedule-Free learning without learning-rate schedules. Schedule-Free AdamW is the core algorithm behind the team's winning entry to the MLCommons 2024 AlgoPerf Self-Tuning track.
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Since October 2024
Research scientist at Meta
He joins Meta in Paris in October 2024 as a research scientist, doing research on code generation.
Career
- Since 2024MetaResearch scientist
- 2023–2024Samsung
- 2021–2022InriaPostdoctoral researcher
- 2020Google BrainIntern
Education
Sources and credits
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