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

PhD Program

  • If you are interested in algorithms and theoretical computer science, we encourage you to apply to the PhD program at Northwestern University (more info).

Talks

  • Tomsk University (remote; YouTube), February 15, 2022: Explainable k-means. Don’t be greedy, plant bigger trees!
  • Yahoo! Research (remote), May 29, 2020: Correlation Clustering
  • Tel Aviv University, December 9, 2019: Dimensionality Reduction for k-Means and k-Medians Clustering
  • Technion, December 2, 2019: Dimensionality Reduction for k-Means and k-Medians Clustering
  • Illinois Institute of Technology, November 19, 2019: Dimensionality Reduction for k-Means and k-Medians Clustering
  • FOCS Workshop on Beyond the Worst Case Analysis of Algorithms, November 9, 2019: Perturbation Stability and Certified Algorithms
  • UPenn, October 25, 2019: Dimensionality Reduction for k-Means and k-Medians Clustering
  • TTIC Workshop on Recent Trends in Clustering, September 18, 2019: Correlation Clustering

Teaching

Northwestern University

  • Design and Analysis of Algorithms: Winter 2024, Fall 2022, Fall 2021, Winter 2021, Winter 2020, Winter 2019, Spring 2018, Winter 2018
  • Graduate Algorithms (syllabus): Fall 2024, Spring 2024, Fall 2020, Spring 2020
  • Approximation Algorithms (syllabus): 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.
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  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. Constraint Satisfaction Problems with Advice
  2. Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models
  3. Approximation Scheme for Weighted Metric Clustering via Sherali-Adams (oral presentation)
  4. Higher-Order Cheeger Inequality for Partitioning with Buffers
  5. Random Cuts are Optimal for Explainable k-Medians
    • Konstantin Makarychev and Liren Shan
    • NeurIPS 2023 (oral presentation)
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  6. Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!
    • Sayak Chakrabarty and Konstantin Makarychev
    • NeurIPS 2023
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  7. Phylogenetic CSPs are Approximation Resistant
  8. Approximation Algorithm for Norm Multiway Cut
  9. Explainable k-means. Don’t be greedy, plant bigger trees!
    • Konstantin Makarychev and Liren Shan
    • STOC 2022
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  10. Near-optimal algorithms for explainable k-medians and k-means
    • Konstantin Makarychev and Liren Shan
    • ICML 2021
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  11. Local Correlation Clustering with Asymmetric Classification Errors
  12. Batch Optimization for DNA Synthesis
  13. Two-sided Kirszbraun Theorem
  14. Improved Guarantees for k-means++ and k-means++ Parallel
  15. Correlation Clustering with Asymmetric Classification Errors
  16. Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection
  17. Certified Algorithms: Worst-Case Analysis and Beyond
  18. Correlation Clustering with Local Objectives
  19. Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering
  20. DNA assembly for nanopore data storage readout
  21. Scaling up DNA data storage and random access retrieval
  22. Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
  23. Clustering Billions of Reads for DNA Data Storage
  24. Algorithms for Stable and Perturbation-Resilient Problems
  25. Robust algorithms with polynomial loss for near-unanimity CSPs
  26. Learning Communities in the Presence of Errors
  27. Union of Euclidean Metric Spaces is Euclidean
  28. A bi-criteria approximation algorithm for k-Means
  29. Satisfiability of Ordering CSPs Above Average
  30. Correlation Clustering with Noisy Partial Information
  31. Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete Graphs
  32. Network-Aware Scheduling for Data-Parallel Jobs: Plan When You Can
  33. Solving Optimization Problems with Diseconomies of Scale
  34. Nonuniform Graph Partitioning with Unrelated Weights
  35. Precedence-constrained Scheduling of Malleable Jobs with Preemption
  36. Constant Factor Approximation for Balanced Cut in the PIE Model
  37. Bilu-Linial Stable Instances of Max Cut
  38. Approximation Algorithm for Sparsest k-Partitioning
  39. Speed Regularization and Optimality in Word Classing
  40. Local Search is Better than Random Assignment for Bounded Occurrence Ordering k-CSPs
    • Konstantin Makarychev
    • STACS 2013
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  41. Sorting Noisy Data with Partial Information
  42. Approximation Algorithm for Non-Boolean MAX k-CSP
  43. Approximation Algorithms for Semi-random Graph Partitioning Problems
  44. Concentration Inequalities for Nonlinear Matroid Intersection
  45. The Grothendieck Constant is Strictly Smaller than Krivine's Bound
  46. How to Play Unique Games Against a Semi-random Adversary
  47. Min-Max Graph Partitioning and Small Set Expansion
  48. Improved Approximation for the Directed Spanner Problem
  49. Maximizing Polynomials Subject to Assignment Constraints
  50. On Parsimonious Explanations For 2-D Tree- and Linearly-Ordered Data
  51. Assembly of Circular Genomes
  52. Metric Extension Operators, Vertex Sparsifiers and Lipschitz Extendability
  53. Maximum Quadratic Assignment Problem
  54. How to Play Unique Games on Expanders
  55. On Hardness of Pricing Items for Single-Minded Bidders
  56. Integrality Gaps for Sherali-Adams Relaxations
  57. Indexing Genomic Sequences on the IBM Blue Gene
    • Amol Ghoting and Konstantin Makarychev
    • SC 2009
    • ACM Gordon Bell Prize Finalist
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  58. 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
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  59. Online Make-to-Order Joint Replenishment Model: Primal Dual Competitive Algorithms
  60. Local Global Tradeoffs in Metric Embeddings
  61. On the Advantage over Random for Maximum Acyclic Subgraph
  62. Near-Optimal Algorithms for Maximum Constraint Satisfaction Problems
  63. A Divide and Conquer Algorithm for d-Dimensional Linear Arrangement
  64. How to Play Unique Games Using Embeddings
  65. Near-Optimal Algorithms for Unique Games
  66. Directed Metrics and Directed Graph Partitioning Problems
  67. Square root log n approximation algorithms for Min UnCut, Min 2CNF Deletion, and directed cut problems
  68. Quadratic Forms on Graphs
  69. Chain Independence and Common Information
    • Konstantin Makarychev and Yury Makarychev
    • IEEE Transactions on Information Theory, 58(8), pp. 5279-5286, 2012
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  70. A new class of non Shannon type inequalities for entropies
  71. The Importance of Being Formal
    • Konstantin Makarychev and Yury Makarychev
    • The Mathematical Intelligencer, vol. 23 no. 1, 2001
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  72. 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 (11)
FOCS (10)
SODA (10)
ICALP (5)
NeurIPS (5)
ICML (4)
COLT (2)
AAAI (1)
AISTATS (1)
APPROX (3)
ESA (1)
ICASSP (1)
ITCS (3)
ISIT (1)
SC (1)
SIGCOMM (1)
SIGMOD (1)
SoCG (1)
STACS (2)
WAOA (1)
Journals* (6)
*This section excludes journal papers previously published in conference proceedings.
Manuscripts (1)
PhD Thesis (1)

Contact Information

  • Department of Computer Science
  • Northwestern University
  • Mudd Hall, Room 3009
  • 2233 Tech Drive, Third Floor
  • Evanston, IL 60208
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  • Email: my_first_name [at] northwestern.edu