معرفی
Konstantin Makarychev is a Professor of Computer Science and Associate Chair for Graduate Studies at Northwestern University's McCormick School of Engineering. His research focuses on designing efficient algorithms for computationally hard problems, with an emphasis on approximation algorithms, beyond worst-case analysis, and applications of high-dimensional geometry. Before joining Northwestern, he was a researcher at Microsoft and IBM Research Labs, and earned his Ph.D. from Princeton University in 2007 under Moses Charikar.
He holds a B.S. in Mechanics and Mathematics from Moscow State University and an M.S. from the Department of Mathematics at Moscow State University. His academic career includes roles at Microsoft Research, IBM Research, and teaching positions at the University of Washington.
Research interests include approximation algorithms for constraint satisfaction problems, clustering algorithms, and algorithmic approaches to machine learning. He has published extensively in top conferences like SODA, ICML, and STOC, and his work often bridges theoretical computer science with practical applications in data storage and bioinformatics.
- Awards: IBM A-Level Accomplishment (2011), IBM Pat Goldberg Best Paper Award (2009), IBM PhD Fellowship (2006–2007).
- Grants: NSF Award CCF-1955351 (2020–2025), participation in IDEAL Institute (2019–2022).
- Teaching: Courses include Design and Analysis of Algorithms, Approximation Algorithms, and Advanced Algorithm Design.
His work on correlation clustering, explainable k-means, and DNA data storage has led to impactful contributions in both theory and practical applications.
