Konstantin Makarychev's Photo

About me

I am a Professor of Computer Science at Northwestern University. I am interested in designing efficient algorithms for computationally hard problems. The aim of my research is to introduce new core techniques and design general principles for developing and analyzing algorithms that work in theory and practice. My research interests include approximation algorithms, beyond worst-case analysis, theory of machine learning, and applications of high-dimension geometry in computer science.

Before joining Northwestern, I was a researcher at Microsoft and IBM Research Labs. I graduated from Princeton University in 2007. My PhD advisor was Moses Charikar. I received my undergraduate degree at the Department of Mathematics at Moscow State University. I finished Moscow Math High School #57.

See my CV in html or pdf format for more info.

You can find my video lectures as well as video recordings from workshops I recently organized with Yury Makarychev on my Youtube channel @AdvancedAlgorithms.

Events at Northwestern

Recent and Upcoming Talks

  • IDEAL Get Ready for Research Workshop at University of Illinois Chicago, November 8, 2025: Explainable k-means and k-medians Clustering
  • Workshop on Constraint Satisfaction Problems at Dagstuhl, May 19, 2025: Phylogenetic CSPs are Approximation Resistant
  • Workshop on Clustering at EnCORE, UCSD, March 4, 2024: Correlation Clustering Algorithm for Streaming, Dynamic, Parallel, and Local Computation Models

Teaching

Northwestern University

  • Design and Analysis of Algorithms: Winter 2026, Winter 2024, Fall 2022, Fall 2021, Winter 2021, Winter 2020, Winter 2019, Spring 2018, Winter 2018
  • Graduate Algorithms (syllabus): Spring 2026, Fall 2024, Spring 2024, Fall 2020, Spring 2020
  • Approximation Algorithms (syllabus): Fall 2025, Fall 2024, Winter 2023, Winter 2021, Spring 2019, Spring 2017
  • Special Topics in Approximation Algorithms: Winter 2025, Spring 2020
  • Advanced Algorithm Design Through the Lens of Competitive Programming: Winter 2022
  • Algorithms for Big Data: Spring 2022
  • Math Toolkit for Theoretical Computer Scientists: Spring 2019

University of Washington

  • Linear and Semi-Definite Programming in Approximation Algorithms (with Mohit Singh): Fall 2014

Current and Former PhD Students

Surveys and Book Chapters

  1. Perturbation Resilience
    • Konstantin Makarychev and Yury Makarychev
    • Beyond the Worst-Case Analysis of Algorithms. Editor: Tim Roughgarden. Cambridge University Press. 2020.
  2. Approximation Algorithms for CSPs (a survey of results)
    • Konstantin Makarychev and Yury Makarychev
    • The Constraint Satisfaction Problem: Complexity and Approximability. Editors: Andrei Krokhin and Stanislav Zivny. Dagstuhl Follow-Ups. 2017.
  3. Bilu-Linial Stability (a survey on Bilu-Linial stability and perturbation resilience)
    • Konstantin Makarychev and Yury Makarychev
    • Advanced Structured Prediction. Editors: T. Hazan, G. Papandreou, D. Tarlow. MIT Press. 2016.

Publications

  1. On the Approximability of Max-Cut on 3-Colorable Graphs and Graphs with Large Independent Sets
  2. Threshold Rounding and Bounded-Degree Boolean MAX 2-CSP
  3. Strong Refutation of Ordering, Phylogenetic, and Ordinary CSPs, and New Satisfiability and Refutation Thresholds for Triplet and Quartet Reconstruction
  4. Optimal Phylogenetic Reconstruction from Sampled Quartets
  5. Simple Average-case Analysis of Recursive Randomized Greedy MIS
  6. Dynamic Algorithm for Explainable k-medians Clustering under ℓp Norm
  7. Sparse-pivot: Dynamic correlation clustering for node insertions
  8. Constraint Satisfaction Problems with Advice
  9. Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models
  10. Approximation Scheme for Weighted Metric Clustering via Sherali-Adams
  11. Higher-Order Cheeger Inequality for Partitioning with Buffers
  12. Random Cuts are Optimal for Explainable k-Medians
    • Konstantin Makarychev and Liren Shan
    • NeurIPS 2023 (oral presentation)
  13. Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!
    • Sayak Chakrabarty and Konstantin Makarychev
    • NeurIPS 2023
  14. Phylogenetic CSPs are Approximation Resistant
  15. Approximation Algorithm for Norm Multiway Cut
  16. Explainable k-means. Don’t be greedy, plant bigger trees!
  17. Near-optimal algorithms for explainable k-medians and k-means
  18. Local Correlation Clustering with Asymmetric Classification Errors
  19. Batch Optimization for DNA Synthesis
  20. Two-sided Kirszbraun Theorem
  21. Improved Guarantees for k-means++ and k-means++ Parallel
  22. Correlation Clustering with Asymmetric Classification Errors
  23. Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection
  24. Certified Algorithms: Worst-Case Analysis and Beyond
  25. Correlation Clustering with Local Objectives
  26. Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering
  27. DNA assembly for nanopore data storage readout
  28. Scaling up DNA data storage and random access retrieval
  29. Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
  30. Clustering Billions of Reads for DNA Data Storage
  31. Algorithms for Stable and Perturbation-Resilient Problems
  32. Robust algorithms with polynomial loss for near-unanimity CSPs
  33. Learning Communities in the Presence of Errors
  34. Union of Euclidean Metric Spaces is Euclidean
  35. A bi-criteria approximation algorithm for k-Means
  36. Satisfiability of Ordering CSPs Above Average
  37. Correlation Clustering with Noisy Partial Information
  38. Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete Graphs
  39. Network-Aware Scheduling for Data-Parallel Jobs: Plan When You Can
  40. Solving Optimization Problems with Diseconomies of Scale
  41. Nonuniform Graph Partitioning with Unrelated Weights
  42. Precedence-constrained Scheduling of Malleable Jobs with Preemption
  43. Constant Factor Approximation for Balanced Cut in the PIE Model
  44. Bilu-Linial Stable Instances of Max Cut
  45. Approximation Algorithm for Sparsest k-Partitioning
  46. Speed Regularization and Optimality in Word Classing
  47. Local Search is Better than Random Assignment for Bounded Occurrence Ordering k-CSPs
    • Konstantin Makarychev
    • STACS 2013
  48. Sorting Noisy Data with Partial Information
  49. Approximation Algorithm for Non-Boolean MAX k-CSP
  50. Approximation Algorithms for Semi-random Graph Partitioning Problems
  51. Concentration Inequalities for Nonlinear Matroid Intersection
  52. The Grothendieck Constant is Strictly Smaller than Krivine's Bound
  53. How to Play Unique Games Against a Semi-random Adversary
  54. Min-Max Graph Partitioning and Small Set Expansion
  55. Improved Approximation for the Directed Spanner Problem
  56. Maximizing Polynomials Subject to Assignment Constraints
  57. On Parsimonious Explanations For 2-D Tree- and Linearly-Ordered Data
  58. Assembly of Circular Genomes
  59. Metric Extension Operators, Vertex Sparsifiers and Lipschitz Extendability
  60. Maximum Quadratic Assignment Problem
  61. How to Play Unique Games on Expanders
  62. On Hardness of Pricing Items for Single-Minded Bidders
  63. Integrality Gaps for Sherali-Adams Relaxations
  64. Indexing Genomic Sequences on the IBM Blue Gene
    • Amol Ghoting and Konstantin Makarychev
    • SC 2009
    • ACM Gordon Bell Prize Finalist
  65. Serial and Parallel Methods for I/O Efficient Suffix Tree Construction
    • Amol Ghoting and Konstantin Makarychev
    • SIGMOD 2009
    • ACM Transactions on Database Systems (TODS), vol. 35(4), pp. 25:1-25:37
    • IBM Pat Goldberg Best Paper Award
  66. Online Make-to-Order Joint Replenishment Model: Primal Dual Competitive Algorithms
  67. Local Global Tradeoffs in Metric Embeddings
  68. On the Advantage over Random for Maximum Acyclic Subgraph
  69. Near-Optimal Algorithms for Maximum Constraint Satisfaction Problems
  70. A Divide and Conquer Algorithm for d-Dimensional Linear Arrangement
  71. How to Play Unique Games Using Embeddings
  72. Near-Optimal Algorithms for Unique Games
  73. Directed Metrics and Directed Graph Partitioning Problems
  74. Square root log n approximation algorithms for Min UnCut, Min 2CNF Deletion, and directed cut problems
  75. Quadratic Forms on Graphs
  76. Chain Independence and Common Information
    • Konstantin Makarychev and Yury Makarychev
    • IEEE Transactions on Information Theory, 58(8), pp. 5279-5286, 2012
  77. A new class of non Shannon type inequalities for entropies
  78. The Importance of Being Formal
    • Konstantin Makarychev and Yury Makarychev
    • The Mathematical Intelligencer, vol. 23 no. 1, 2001
  79. Proof of Pak's conjecture on tilings by T-tetrominoes (in Russian)

PhD Thesis

  1. Quadratic Forms on Graphs and Their Applications
    • Konstantin Makarychev

Publications

Surveys (3)
STOC (12)
FOCS (10)
SODA (10)
ICALP (5)
NeurIPS (6)
ICML (5)
COLT (2)
AAAI (1)
AISTATS (1)
APPROX (4)
ESA (1)
ICASSP (1)
ITCS (3)
ISIT (1)
SC (1)
SIGCOMM (1)
SIGMOD (1)
SoCG (1)
SOSA (1)
STACS (2)
WAOA (1)
Journals* (6)
*This section excludes journal papers previously published in conference proceedings.
Manuscripts (3)
PhD Thesis (1)

Contact Information

  • Department of Computer Science
  • Northwestern University
  • Mudd Hall, Room 3009
  • 2233 Tech Drive, Third Floor
  • Evanston, IL 60208
  • Email: click to show