Dmitrii Avdiukhin
Starting from September 2023, I am a McCormick Postdoctoral Fellow at Northwestern University. Mentor: Konstantin Makarychev
I got my Ph.D. at Indiana University in 2023. Advisor: Grigory Yaroslavtsev
E-mail: first name (at) first name (dot) us
Research interests
- Convex and nonconvex optimization
- Machine learning
- Hierarchical clustering
- Approximation algorithms
- Learning Theory
News
- 16 Jan 2024
- Our paper "Optimal Sample Complexity of Contrastive Learning" is accepted for ICLR 24 for spotlight presentation!
- 8 Dec 2023
- Our paper "Approximation Scheme for Weighted Metric Clustering via Sherali-Adams" is accepted for AAAI 24!
- 30 Nov 2023
- Junior Theorists Workshop 2023 starts today! The first day is held at Northwestern University, and the second day is held by TTIC. We will be hosting some of the best PhD students and postdocs, so look forward to excellent talks!
- 8 Sep 2023
- I'm excited to join Northwestern University as a McCormick Postdoctoral Fellow under the mentorship of Konstantin Makarychev!
Selected Publications
- [Check this link for the list of all publications]
- ICLR 2024
- N. Alon, D. Avdiukhin, D. Elboim, O. Fischer, G. Yaroslavtsev. "Optimal Sample Complexity of Contrastive Learning"
- AAAI 2023
- D. Avdiukhin, G. Yaroslavtsev, D. Vainstein, O. Fischer, S. Das, and F. Mirza. "Tree Learning: Optimal Algorithms and Sample Complexity" [paper]
- NeurIPS 2021
- D. Avdiukhin., and G. Yaroslavtsev. "Escaping Saddle Points with Compressed SGD" [paper]
- ICML 2021
- D. Avdiukhin., and S. Kasiviswanathan. "Federated Learning under Arbitrary Communication Patterns" [paper]
- AAAI 2021
- D. Avdiukhin., S. Naumov, and G. Yaroslavtsev. "Objective-Based Hierarchical Clustering of Deep Embedding Vectors" [paper]
- VLDB 2019
- D. Avdiukhin, S. Pupyrev and G. Yaroslavtsev. “Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent” [paper]
Experience
- Summer 2022
- Research Intern, Amazon.
Demonstration selection for few-shot learning for small language models. - Summer 2020
- Research Intern, Amazon.
Federated Learning under weak assumptions - Summer 2019
- Research Intern, Amazon, New York.
Improving accuracy and performance of graph convolutional networks - Summer 2018
- Software Engineer, Pro Unlimited @ Facebook, Menlo Park.
Working on balanced graph partitioning - 2016-2017
- Researcher, ITMO University.
Model generation from execution traces - 2013-2016
- Software Engineer. JetBrains, Saint Petersburg.
SQL dialects support - 2012-2013
- Software Engineer. Lanit Tercom, Saint Petersburg.
Participating in project of migration a system from SQL server to Oracle
Organizer
- November 2023
- Junior Theorists Workshop 2023
Talks and Posters
- ITA 2023
- Talk
- “First-Order Methods in Distributed Optimization”
- OPT 2022
- Poster
- “HOUDINI: Escaping from Moderately Constrained Saddles”
- OPT 2022
- Poster
- “Bidirectional Adaptive Communication for Heterogeneous Distributed Learning”
- NeurIPS 2021
- Poster
- “Escaping Saddle Points with Compressed SGD”
- OPT 2020
- Paster
- “Escaping Saddle Points with Compressed SGD”
- VLDB 2019
- Talk
- “Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent”
- KDD 2019
- Talk
- “Adversarially Robust Submodular Maximization under Knapsack Constraints”
Other Talks
- Junior Theorists Workshop 2023
- "Optimal Sample Complexity of Contrastive Learning"
- SIAM OP 2023
- "Escaping Saddle Points with Compressed SGD"
- Google Algorithms Seminar
- "Tree Learning: Optimal Algorithms and Sample Complexity"
- SPbSU, Russia
- "Escaping from Saddle Points with Compressed SGD"
- Yandex, Russia
- "Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent"
Fellowships
- 2019
- Nominated for Google PhD Fellowship Program by Indiana University
- 2019
- Nominated for Microsoft Fellowship by Indiana University
Teaching
- Fall 2022
- "Introduction to Algorithm Design and Analysis" (undergraduate). Indiana University, Bloomington, CSCI-B403. Associate Instructor.
- Spring 2022
- "Math & logic for cognitive science" (graduate). Indiana University, Bloomington, CSCI-B590. Associate Instructor.
- Spring 2020
- "Applied Algorithms" (graduate). Indiana University, Bloomington, CSCI-B505. Head Associate Instructor.
- Spring 2019
- "Data Structures" (Honors, undergraduate)}. Indiana University, Bloomington, CSCI-H343. Associate Instructor.