Konstantin Makarychev's Photo


 

About me

I am an Associate 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 a tentative syllabus for my Graduate Algorithms course here: Fall 2020 Syllabus. This quarter, I also post lectures on algorithms in Russian. You can find my video notes here.

PhD Program and Postdoc Positions in Theoretical CS

  • If you are interested in algorithms and theoretical computer science, we encourage you to apply to the PhD program at Northwestern University (more info).
  • We are looking for postdocs in approximation algorithms, beyond-worst-case analysis of algorithms, and/or high-dimensional data analysis (more info).

Talks

  • Yahoo Research, 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

Events at Northwestern

Teaching

Northwestern University

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

University of Washington

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

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. Batch Optimization for DNA Synthesis
  2. Two-sided Kirszbraun Theorem
  3. Improved Guarantees for k-means++ and k-means++ Parallel
  4. Correlation Clustering with Asymmetric Classification Errors
  5. Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection
  6. Certified Algorithms: Worst-Case Analysis and Beyond
  7. Correlation Clustering with Local Objectives
  8. Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering
  9. DNA assembly for nanopore data storage readout
  10. Scaling up DNA data storage and random access retrieval
  11. Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
  12. Clustering Billions of Reads for DNA Data Storage
  13. Algorithms for Stable and Perturbation-Resilient Problems
  14. Robust algorithms with polynomial loss for near-unanimity CSPs
  15. Learning Communities in the Presence of Errors
  16. Union of Euclidean Metric Spaces is Euclidean
  17. A bi-criteria approximation algorithm for k-Means
  18. Satisfiability of Ordering CSPs Above Average
  19. Correlation Clustering with Noisy Partial Information
  20. Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete Graphs
  21. Network-Aware Scheduling for Data-Parallel Jobs: Plan When You Can
  22. Solving Optimization Problems with Diseconomies of Scale
  23. Nonuniform Graph Partitioning with Unrelated Weights
  24. Precedence-constrained Scheduling of Malleable Jobs with Preemption
  25. Constant Factor Approximation for Balanced Cut in the PIE Model
  26. Bilu-Linial Stable Instances of Max Cut
  27. Approximation Algorithm for Sparsest k-Partitioning
  28. Speed Regularization and Optimality in Word Classing
  29. Local Search is Better than Random Assignment for Bounded Occurrence Ordering k-CSPs
    • Konstantin Makarychev
    • STACS 2013
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  30. Sorting Noisy Data with Partial Information
  31. Approximation Algorithm for Non-Boolean MAX k-CSP
  32. Approximation Algorithms for Semi-random Graph Partitioning Problems
  33. Concentration Inequalities for Nonlinear Matroid Intersection
  34. The Grothendieck Constant is Strictly Smaller than Krivine's Bound
  35. How to Play Unique Games Against a Semi-random Adversary
  36. Min-Max Graph Partitioning and Small Set Expansion
  37. Improved Approximation for the Directed Spanner Problem
  38. Maximizing Polynomials Subject to Assignment Constraints
  39. On Parsimonious Explanations For 2-D Tree- and Linearly-Ordered Data
  40. Assembly of Circular Genomes
  41. Metric Extension Operators, Vertex Sparsifiers and Lipschitz Extendability
  42. Maximum Quadratic Assignment Problem
  43. How to Play Unique Games on Expanders
  44. On Hardness of Pricing Items for Single-Minded Bidders
  45. Integrality Gaps for Sherali-Adams Relaxations
  46. Indexing Genomic Sequences on the IBM Blue Gene
    • Amol Ghoting and Konstantin Makarychev
    • SC 2009
    • ACM Gordon Bell Prize Finalist
    •  
  47. 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
    •  
  48. Online Make-to-Order Joint Replenishment Model: Primal Dual Competitive Algorithms
  49. Local Global Tradeoffs in Metric Embeddings
  50. On the Advantage over Random for Maximum Acyclic Subgraph
  51. Near-Optimal Algorithms for Maximum Constraint Satisfaction Problems
  52. A Divide and Conquer Algorithm for d-Dimensional Linear Arrangement
  53. How to Play Unique Games Using Embeddings
  54. Near-Optimal Algorithms for Unique Games
  55. Directed Metrics and Directed Graph Partitioning Problems
  56. Square root log n approximation algorithms for Min UnCut, Min 2CNF Deletion, and directed cut problems
  57. Quadratic Forms on Graphs
  58. 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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  59. A new class of non Shannon type inequalities for entropies
  60. The Importance of Being Formal
    • Konstantin Makarychev and Yury Makarychev
    • The Mathematical Intelligencer, vol. 23 no. 1, 2001
    •  
  61. 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 (10)
FOCS (9)
ICALP (5)
ICML (1)
ITCS (3)
ISIT (1)
ICASSP (1)
SC (1)
SIGMOD (1)
SoCG (1)
Journals* (6)
Manuscripts (2)
PhD Thesis (1)

Contact Information

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