Assistant professor of Computer Science at Indiana University, Bloomington. Founding director of the Center for Algorithms and Machine Learning (CAML). Adjunct assistant professor at the Department of Statistics. “PROFessional quadrathlete”: ! Apply for a Ph.D. in my group. Check this ad and my papers first. Internships for international undergraduate students. ... Check my diversity statement.
Interests: algorithms and foundations of scalable data analysis, machine learning, data science, privacy and targeted alternatives to bulk data collection.

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Massively Parallel Computation

Theoretical model Model for MapReduce/Spark-like computation. Algorthms for vector data: min spanning tree, min-cost bichromatic matching.

Single-Linkage Clustering Scalable massively parallel algorithms for single-linkage clustering of vector data.

Hierarchical Clustering

Graph Partitioning Multi-dimensional balanced graph partitioning algorithms for Facebook graphs using projected gradient descent.

Clustering Vector Data Projection-based algorithms for hierarchical clustering of high-dimensional vector data.

Private Data Analysis

Private Targeted Search Algorithms for searching for targeted individuals in networks with privacy guarantees for protected subpopulations.

Private Graph Statistics Algorithms for releasing subgraph statistics about large networks while preserving privacy of relationships.

Publications:

• ML and big data conferences: AISTATS, ICDE, ICML, KDD, OPT@NeurIPS, VLDB.
• TCS conferences: APPROX, CCC, ICALP, PODC, RANDOM, SAT, SODA, SOSA@SODA, STOC.
• Journals: Algorithmica, Combinatorica, I&C, IPL, PNAS, TODS.

Program committees:

• AI/ML: AAAI ('20) , AISTATS ('20 – '19), NeurIPS ('19 – '16), ICML ('20 – '18), ICLR ('20 – '18), UAI ('19).
• TCS: APPROX 18, BeyondMR 18, COCOON 17, SODA 17, ESA 16, SOFSEM 15.

Recently organized:

• New! Recent Trends in Clustering and Classification: 3-day workshop at TTI-Chicago, '19.
• Linear Sketching as a Tool for Everything: 1-day workshop at FOCS'17.
• 67th Midwest Theory Day: 2-day workshop at Indiana University, Bloomington, '17.
• Big Data Through the Lens of Sublinear Algorithms: 2-day workshop at Rutgers DIMACS, '15.
• Algorithmic Frontiers of Modern Massively Parallel Computation: 1-day workshop at ACM FCRC '15.
• Sublinear Algorithms and Big Data Day: Sublinear Day #1 (Brown '14), Sublinear Day #2 (MIT '15).

2020

• 01/06: Two papers in AISTATS 2020: ““Bring Your Own Greedy”+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack” and “Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection”.

2019

• 10/24: Talk “Advances in Hierarchical Clustering of Vector Data” at University of Illinois, Urbana-Champaign, CSL Seminar.
• 10/18: Departmental colloquium “Advances in Hierarchical Clustering of Vector Data” at the University of California, Riverside, CA.
• 10/16: Paper in OPT@NeurIPS 2019: “Escaping Saddle Points with Inequality Constraints via Noisy Sticky Projected Gradient Descent”
• 10/16: CMI Seminar “Advances in Linear Sketching over Finite Fields at California Institute of Technology, Pasadena, CA.
• 10/14: Theory Seminar “Advances in Hierarchical Clustering of Vector Data” at the University of California, San Diego, CA.
• 10/10: Theory Lunch “Advances in Hierarchical Clustering of Vector Data” at the University of Southern California, Los Angeles, CA.
• 10/04: Paper in SOSA@SODA 2020: “Fast Fourier Sparsity Testing”.
• 09/18–09/20: Organizing a workshop “Recent Trends in Clustering and Classification” at TTI-Chicago.
• 08/15: Talk “Advances in Hierarchical Clustering of Vector Data” at Google Research, Mountain View, CA.
• 06/21: Paper in RANDOM 2019: “Approximate F₂-Sketching of Valuation Functions”.
• 06/06: Talk “Advances in Hierarchical Clustering of Vector Data” at the Discrete Mathematics and Applications Seminar, University of Warwick, Warwick, UK.
• 05/30: Talk “Advances in Hierarchical Clustering of Vector Data” at the Algorithms and Complexity Seminar, University of Oxford, Oxford, UK.
• 04/30: Paper in CCC 2019: “Optimality of Linear Sketching under Modular Updates”.
• 04/29: Paper in KDD 2019: “Adversarially Robust Submodular Maximization under Knapsack Constraints” as an oral presentation (9.2% acceptance rate).
• 03/15: Paper in VLDB 2019: “Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent”.
• 03/11: Talk “Advances in Hierarchical Clustering of Vector Data” at Facebook Core Data Science, Menlo Park, CA.
• 03/06: Talk “Advances in Hierarchical Clustering of Vector Data” at the Algorithms and Complexity Seminar, Johns Hopkins University, Baltimore, MD.
• 03/01: Talk “Advances in Hierarchical Clustering of Vector Data” at the Computer Science Seminar, Northwestern University, Evanston, IL.
• 02/10: Preprint “Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent”.

2018

• 12/22/18: Paper in AISTATS 2019: “Hierarchical Clustering for Euclidean Data”.
• 10/16/18: Talk “Advances in Linear Sketching over Finite Fields at the Simons Institute for the Theory of Computing, Workshop on Interactive Complexity. Berkeley, CA.
• 09/24/18: Preprint “Optimality of Linear Sketching under Modular Updates”.
• 09/14/18: Talk “Linear Sketching for Functions over Boolean Hypercube” at the Theory Seminar, University of Michigan, Ann Arbor, MI.
• 08/07/18: Talk “Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under ℓ_p-Distances” at IBM Research Almaden, San Jose, CA.
• 07/12/18: Talk “Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under ℓ_p-Distances” at the 35 International Conference on Machine Learning (ICML'18).
• 06/22/18: Talk “Linear Sketching for Functions over Boolean Hypercube” at the 33rd Conference on Computational Complexity (CCC'18).
• 06/14/18: Talk “Badger Rampage: Multidimensional Balanced Partitioning of Facebook-Scale Graphs” at the 2^nd Workshop on Local Algorithms at MIT, Cambridge, MA.
• 05/17/18: Talk “Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under ℓ_p-Distances” at the Stanford Theory Seminar.
• 05/11/18: Paper in ICML'18: Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under ℓ_p-Distances as a long talk (8.6% acceptance rate).
• 04/13/18: Talk “Linear Sketching for Functions over Boolean Hypercube” at the 68th Midwest Theory Workshop, Toyota Technological Institute, Chicago, IL.
• 04/04/18: Paper in CCC'18: Linear Sketching over F₂.
• 03/20/18: Talk “Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under ℓ_p-Distances” at the Workshop on Data Summarization, University of Warwick, UK.
• 03/09/18: The Center for Algorithms and Machine Learning (CAML) at IU has been officially launched!

2017

• 12/19/17: I am on the program committee of APPROX 2018 (21st International Workshop on Approximation Algorithms for Combinatorial Optimization Problems).
• 11/22/17: Thanks to Facebook for supporting me with a Facebook Faculty Research Award!
• 10/19/17: I am on the program committee of BeyondMR 2018 (5th Workshop on Algorithms and Systems for MapReduce and Beyond at SIGMOD/PODS'18).
• 10/14/17: Talk “Linear Sketching for Functions over Boolean Hypercube” at the FOCS'17 workshop Linear Sketching as a Tool for Everything.
• 10/14/17: Co-organizing the Linear Sketching as a Tool for Everything workshop at IEEE FOCS 2017.
• 10/13/17: Talk “Clustering on Clusters 2049: Massively Parallel Algorithms for Clustering Graphs and Vectors” at Facebook, Menlo Park, CA.
• 10/05/17: Preprint Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under ℓ_p-Distances .
• 06/15/17: Departmental Colloquium “Computational and Communication Complexity In Massively Parallel Computing” at ITMO University, St. Petersburg, Russia.
• 06/14/17: Talk “Computational and Communication Complexity In Massively Parallel Computing” at Higher School of Economics, Moscow, Russia. Workshop on Complexity of Computation, Communication, Descriptions and Proofs.
• 06/03/17 – 06/04/17: Crash course “Modern algorithms for parallel, streaming and query-based data processing” at Computer Science Club, St. Petersburg, Russia.
• 06/02/17: Talk Linear Sketching with Parities at St. Petersburg Department of Steklov Institute of Mathematics, St. Petersburg, Russia.
• 05/23/17: Talk Linear Sketching with Parities at Kolmogorov Seminar, Moscow State University, Russia.
• 04/15/17 – 04/16/17: Co-organizing the 67th Midwest Theory Day at Indiana University.
• 03/21/17: Talk Linear Sketching with Parities at Banff Workshop on Communication Complexity and Applications.
• 02/09/17: Talk “Clustering on Clusters: Massively Parallel Algorithms for Clustering Graphs and Vectors” at Facebook, Menlo Park, CA.
• 01/24/17: NSF CRII award “Algorithms for Noise-Tolerant Function Testing with Applications to Deep Learning” as a sole PI.

2016

• 11/21/16: Talk Linear Sketching with Parities at Columbia University, New York, NY.
• 11/07/16: Preprint Linear sketching over F₂. See also a related blog post.
• 10/21/16: Talk Linear Sketching with Parities at University of Pennsylvania, Philadelphia, PA.
• 09/09/16: Talk Linear Sketching with Parities at University of Utah, Salt Lake City, UT.
• 08/12/16: Talk Linear Sketching with Parities at University of Illinois, Urbana, IL.
• 06/29/16: Talk Linear Sketching with Parities at Microsoft Research, Redmond, WA.
• 06/26/16 — 07/02/16: I am visiting the Theory Group at Microsoft Research, Redmond.
• 05/30/16: I am on the program committee of COCOON 2017 (23rd International Computing and Combinatorics Conference).
• 03/09/16: Departmental Colloquium “What's New in The Big Data Theory?” at Drexel University, Philadelphia, PA.
• 03/05/16: Spotlight story “Balancing Security and Privacy in Network Analysis” on the Penn front page.
• 02/29/16: Departmental Colloquium “What's New in The Big Data Theory?” at Boston University, Boston, MA.
• 02/25/16: Departmental Colloquium “What's New in The Big Data Theory?” at University of Colorado, Boulder, CO.
• 02/22/16: Departmental Colloquium “What's New in The Big Data Theory?” at Indiana University, Bloomington, IN.
• 02/18/16: Departmental Colloquium “What's New in The Big Data Theory?” at Georgetown University, Washington, DC.
• 02/08/16: Departmental Colloquium “What's New in The Big Data Theory?” at College of William and Mary, Williamsburg, VA.
• 01/08/16: Talk “L_p-Testing” at the Johns Hopkins University workshop on sublinear algorithms. See also open problems.

2015

• 12/22/15: I am on the program committee of SODA 2017 (28th Annual ACM-SIAM Symposium on Discrete Algorithms).
• 11/24/15: Paper “Privacy for the Protected (Only)” accepted to PNAS (Proceedings of the National Academy of Sciences) via direct submission.
• 10/19/15: Algorithms for MapReduce and Beyond: tutorial at CIKM'15 in Melbourne.
• 09/11/15: Paper in SODA'16: Tight Bounds on Linear Sketches of Approximate Matchings.
• 08/27/15 — 08/28/15: Co-organizing the “Big Data Through the Lens of Sublinear Algorithms” workshop at Rutgers University, DIMACS.
• 08/26/15: I am teaching CIS 700: algorithms for Big Data at the University of Pennsylvania in Fall'15.
• 08/22/15: I am on the program committee of ESA 2016, Track A (24th European Symposium on Algorithms).
• 08/06/15: Talk “Fast Fourier Sparsity Testing over the Boolean Hypercube” at the Univeristy of Wisconsin, Madison.
• 07/15/15: Talk “Parallel Algorithms for Geometric Graph Problems” at ISMP 2015 (22nd International Symposium on Mathematical Programming).
• 06/14/15: Co-organizing the “Algorithmic Frontiers of Modern Massively Parallel Computation” workshop at FCRC 2015 (Federated Computing Research Conference).
• 06/01/15: Preprint: Privacy for the Protected (Only).
• 05/06/15: Preprint: Tight Bounds on Linear Sketches of Approximate Matchings.
• 05/04/15: Talk “Near Optimal LP Rounding for Correlation Clustering” at Cornell University, Ithaca, NY.
• 04/14/15: Paper in ICALP'15: Amplification of One-Way Information Complexity via Codes and Noise Sensitivity.
• 04/10/15: Second “Sublinear Algorithms and Big Data Day” at MIT.
• 04/09/15: Talk “Near Optimal LP Rounding for Correlation Clustering” at MIT, Boston, MA.
• 03/12/15: Talk “Near Optimal LP Rounding for Correlation Clustering” at Microsoft Research, Redmond, WA.
• 03/06/15: Preprint: Amplification of One-Way Information Complexity via Codes and Noise Sensitivity.
• 02/17/15: Google Tech Talk “Near Optimal LP Rounding for Correlation Clustering” at Google, NYC.
• 02/03/15: Paper in STOC'15: Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete and Complete k-partite Graphs.
• 01/28/15: Talk “Near Optimal LP Rounding for Correlation Clustering” at Rutgers University, New Brunswick, NJ.
• 01/21/15: Talk “ Near Optimal LP Rounding for Correlation Clustering” at Carnegie Mellon University, Pittsburgh, PA.
• 01/20/15: Talk “ Near Optimal LP Rounding for Correlation Clustering ” at Pennsylvania State University, State College, PA.

2014

• 12/02/14: Preprint: Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete and Complete k-partite Graphs.
• 11/19/14: Talk “Parallel Algorithms for Geometric Graph Problems” at Johns Hopkins University, Baltimore, MD.
• 10/30/14: Talk “Parallel Algorithms for Geometric Graph Problems” at University of Maryland, College Park, MD.
• 10/24/14: Talk “L_p-Testing” at Columbia University, New York, NY.
• 10/12/14: I have started a blog The Big Data Theory, which will cover algorithms for large data processing.
• 09/12/14: I am organizing the Big Data Reading Group at the University of Pennsylvania.
• 08/29/14: Talk “Parallel Algorithms for Geometric Graph Problems” at the University of Pennsylvania, Philadelphia, PA.
• 08/15/14: I am organizing the Computer and Information Sciences Theory Seminar at the University of Pennsylvania.
• 07/28/14 — 08/01/14: I was teaching a 15-hour crash course “Sublinear Algorithms for Big Data” at the 28th International School on Informatics ECI 2014 at the University of Buenos Aires, Argentina.
• 07/30/14: Preprint: Going for Speed: Sublinear Algorithms for Dense r-CSPs.
• 07/16/14: Talk “Beyond Set Disjointness: the Communication Complexity of Finding the Intersection” at the 33rd Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC 2014), Paris, France.
• 06/13/14: Talk “Lower Bounds for Testing Properties of Functions over Hypergrids” at the 29th IEEE Conference on Computational Complexity (CCC 2014), Vancouver, BC.
• 06/06/14: Paper in RANDOM'14: Certifying Equality with Limited Interaction.
• 06/01/14: Talk “L_p-Testing” at the 46th ACM Symposium on the Theory of Computing (STOC 2014), New York, NY.
• 05/19/14: Talk “Parallel Algorithms for Geometric Graph Problems” at the University of Massachusetts, Amherst, MA.
• 05/16/14: Talk “Beyond Set Disjointness: the Communication Complexity of Finding the Intersection ” at MIT Theory of Distributed Systems Seminar, Boston, MA.
• 04/28/14: I am on the PC for the 41st International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM'15), Foundations of Computer Science Track (chair: Roger Wattenhofer). Deadlines are here.
• 04/20/14: Paper in PODC'14: Beyond Set Disjointness: The Communication Complexity of Finding the Intersection.
• 04/18/14: I organized the Sublinear Algorithms and Big Data Day at Brown ICERM.
• 04/03/14: Google Tech Talk “ Parallel Algorithms for Geometric Graph Problems ” at Google Research, NYC.
• 03/27/14: Talk “Parallel Algorithms for Geometric Graph Problems” at Stanford University, Stanford, CA.
• 03/25/14: Talk “Parallel Algorithms for Geometric Graph Problems” at Sandia Labs, Livermore, CA.
• 03/05/14: Talk “The Big Data Theory and Randomized Algorithms” at Georgia Tech, Atlanta.
• 02/25/14: Talk “Approximating Graph Problems: The Old and The New” at Yahoo! Research, NYC.
• 02/19/14: Talk “Approximating Graph Problems: The Old and The New” at MIT Algorithms and Complexity Seminar.
• 02/17/14: Talk “Approximating Graph Problems: The Old and The New” at Toyota Technological Institute at Chicago.
• 02/09/14: I will be teaching a crash course "Sublinear Algorithms for Big Datasets" at the University of Buenos Aires (July 28 – August 01)
• 02/04/14: 2 papers in STOC'14, 1 paper in CCC'14:
  • First “MapReduce” paper in STOC/FOCS.
  • First property testing paper for noisy real-valued data.
  • First paper with communication complexity lower bounds for testing properties of functions on hypergrids.

grigory@grigory.us Blog Scholar GitHub LinkedIn Facebook Twitter Quora Vkontakte YouTube channel Download CV

• '16 –      . Assistant Professor, IUB.
  • S'19. Alan Turing Institute, London, UK.
• '14 – '16. Warren Center Fellow, UPenn.
  CIS and Wharton Statistics.
• '13 – '14. ICERM Institute Fellow, Brown.
• '10 – '13. Ph.D., Penn State.
  • S'13 Intern. Microsoft Research, WA.
  • F'12 Intern. Microsoft Research, CA.
  • S'12 Intern. IBM Research, CA.
  • S'11 Intern. AT&T Research, NJ.

Selected Achievements and Awards

• Facebook Faculty Research Award, '17.
• NSF CRII Award, '17.
• Warren Center Fellow, UPenn, '14-'16.
• ICERM Institute Fellow, Brown, '13-'14.
• Graduate Research Award, Penn St. CSE, '12.
• College of Eng. Fellow, Penn St., '10-'13.
• University Graduate Fellow, Penn St., '10-'11.
• TopCoder Open Algorithm (Top-24), '10.

Group & Alumni

• Samson Zhou (Postdoc, '18 – '19 → Postdoc, Carnegie Mellon University)
• Dmitrii Avdiukhin (PhD student, '17 – )
• Farid Arthaud, ENS Paris (Intern, S'19)
• Jakub Boguta, U. Warsaw (Intern, S'19)
• Stanislav Naumov, SPb ITMO U. (Intern, F'19)

Teaching

• F'19: B505 Applied Algorithms @ IUB
• S'19: H343 Data Structures (honors) @ IUB
• F'18: C343 Data Structures @ IUB
• S'18: H343 Data Structures (honors) @ IUB
• F'17: B505 Applied Algorithms @ IUB (with Funda Ergun)
• F'17: B609 Foundations of Data Science @ IUB
• F'16: B609 Foundations of Data Science @ IUB
• F'15: CIS 700 algorithms for Big Data @ Penn
• S'15: CIS 625 Computational Learning Theory @ Penn (with Michael Kearns)

Papers (alphabetical order unless marked with^*)

Selected Publications

Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent
D. Avdiukhin, S. Pupyrev, G. Yaroslavtsev
VLDB 2019, Research track (45th International Conference on Very Large Data Bases).

Hierarchical Clustering for Euclidean Data
M. Charikar, V. Chatziafratis, R. Niazadeh, G. Yaroslavtsev
AISTATS 2019 (22nd International Conference on Artificial Intelligence and Statistics).

Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under ℓ_p-Distances
G. Yaroslavtsev^*, A. Vadapalli
ICML 2018 (35th International Conference on Machine Learning). Long talk (8.6% acceptance rate).
Code on Github (by Adithya Vadapalli).

Linear Sketching over F₂
S. Kannan, E. Mossel, S. Sanyal, G. Yaroslavtsev
CCC 2018 (33rd Conference on Computational Complexity).
Blog post on “The Big Data Theory”.

Private Algorithms for the Protected in Social Network Search
M. Kearns, A. Roth, S. Wu, G. Yaroslavtsev
PNAS 2016 (Proceedings of the National Academy of Sciences), via direct submission.
Spotlight story on the Penn front page: Balancing Privacy and Security in Network Analysis.
External media coverage: PBS Newshour, ACM Tech News / The Daily Pennsylvanian, Schneier on Security, AAU, Quartz, Pacific Standard, Wired (German), The Naked Scientists Podcast and Vice Motherboard.

Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete and Complete k-partite Graphs
S. Chawla, K. Makarychev, T. Schramm, G. Yaroslavtsev
STOC 2015 (47th ACM Symposium on the Theory of Computing).
See the Wikipedia article on correlation clustering for more details.
For applications in data mining see a Yahoo! KDD'14 tutorial by Francesco Bonchi, David Garcia-Soriano and Edo Liberty.

L_p-Testing
P. Berman, S. Raskhodnikova, G. Yaroslavtsev
STOC 2014 (46th ACM Symposium on the Theory of Computing).
Simple introduction by Sofya Raskhodnikova in the Encyclopedia of Algorithms (Springer).
Open problems from JHU workshop on Sublinear Algorithms [pptx, pdf].
Oded Goldreich's review on “My Choices”. Property Testing Review Post 1, Post 2.

Parallel Algorithms for Geometric Graph Problems
A. Andoni, A. Nikolov, K. Onak, G. Yaroslavtsev
STOC 2014 (46th ACM Symposium on the Theory of Computing).
Notes by Thomas Steinke from Jelani Nelson's Algorithms for Big Data Course at Harvard
Notes by Kui Tang from Alex Andoni's Algorithmic Techniques for Massive Data Course at Columbia.

Private Analysis of Graph Structure
V. Karwa, S. Raskhodnikova, A. Smith, G. Yaroslavtsev
VLDB 2011, Research track (37th International Conference on Very Large Data Bases).
Full version in ACM Transactions on Database Systems.

Approximation Algorithms for Spanner Problems and Directed Steiner Forest
P. Berman, A. Bhattacharyya, K. Makarychev, S. Raskhodnikova, G. Yaroslavtsev
ICALP 2011, Track A (38th International Colloquium on Automata, Languages and Programming), special issue of “Information and Computation”.
Runner-up for the Best Paper Award.
Open Problem #2 from the Princeton Workshop on Approximation Algorithms. See also my slides.

Finding Efficient Circuits Using SAT-solvers
A. Kojevnikov, A. Kulikov, G. Yaroslavtsev
SAT 2009 (12th International Conference on Theory and Applications of Satisfiability Testing).
Cited multiple times in Volume 4B of Donald E. Knuth's “The Art of Computer Programming”.
Notes by Ryan Williams from his class Topics in Circuit Complexity at Stanford.
Extension in Information Processing Letters as E.Demenkov, A.Kojevnikov, A.Kulikov, G.Yaroslavtsev “New upper bounds on the Boolean Circuit Complexity of Symmetric Functions” [Full text: (pdf)]

Other Publications

^* Indicates papers with non-alphabetical ordering of authors.

Preprints

• I've been teaching CSCI-H343 (Data Structures, honors) at Indiana University, Bloomington in Spring 2018.
• I've been teaching CSCI-B505 (Applied Algorithms) at Indiana University, Bloomington in Fall 2017.
• Short tutorial Clustering on Clusters: Massively Parallel Algorithms for Clustering Graphs and Vectors at Facebook, Menlo Park, CA, Spring 2017.
• I have developed CSCI-B609, Foundations of Data Science (graduate) at Indiana University, Bloomington, Fall 2016 and Fall 2017.
• Guest lecturer at CIS 399, Foundations of Data Science (undergraduate) at the University of Pennsylvania, Spring 2016.
  • Massively Parallel Algorithms. Slides (pdf).
• I have developed CIS 700, Algorithms for Big Data (graduate) at the University of Pennsylvania, Fall 2015.
• Co-teaching CIS 625, Computational Learning Theory (graduate, together with Michael Kearns) at the University of Pennsylvania, Spring 2015.
  • Lecture 1: Fourier Analysis and Learning, Part 1. Slides (pdf).
  • Lecture 2: Fourier Analysis and Learning, Part 2. Slides (pdf).
  • Lecture 3: Learning Submodular Functions. Slides (pdf).
  • Lecture 4: L_p-Testing. Slides (pdf).
• I have developed Sublinear Algorithms for Big Data: 15-hour crash course at the University of Buenos Aires, July 2014.
• Guest lecturer at CMPSC 464, Introduction to the Theory of Computation (undergraduate) at Pennsylvania State University, Fall 2010.

• 

  Triathlon and Duathlon

  I caught the triathlon bug while interning at IBM and Microsoft Research in Bay area in 2012. Since then if I am not working then I am probably practicing for the next year's (old pictures). 5xIM so far: Woodlands, TX (2013), Lake Tahoe, CA (2014 cancelled -> 2015), Lake Placid, NY (2016), Santa Rosa, CA (2017), Cairns, AUS (2018). Let's do it together! ;)

  In 2017 I was representing Team USA in the M30-34 age group at the ITU Standard Distance Duathlon World Championship in Penticton, Canada. In 2019 I was representing Team USA in the M30-34 age group at the ITU Long Distance Triathlon World Championship in Pontevedra, Spain.

• 

  Algorithms competitions

  I participated in ACM ICPC and TopCoder competitions (as griffon) competing and setting problems in TopCoder Open Algorithms Finals.

  After making it to the TopCoder Algorithms Finals 2010 in Las Vegas (Top-24 worldwide individually), I retired from competitive programming to focus on research and triathlon.

• 

  Teaching algorithms for high-school students

  I was teaching advanced classes in algorithms for high-school students for ~5 years, coaching teams for algorithmic competitions. I participated in preparation of training camps and contests for the Russian Olympiad in Informatics and the International Olympiad in Informatics (both in Russia and in the U.S.).

  My proudest accomplishments as a coach are leading my high-school team to a victory in St. Petersburg Olympiad in Informatics in 2008-2009 and the fact that our sloppy coaching didn't stop Team USA from earning a bunch of medals in 2011.

• 

  Non-profit in education for high-school students: Homepage, VK group

  In 2009 I co-founded a non-profit organization focused on advanced extracurricular education in algorithms for high-school students (5 – 10 grades) in St. Petersburg, Russia. In 2013 it was expanded to Moscow and Yekaterinburg.

  In the early days we were supported by , and . Donations are welcome!

• 

  Software Engineering

  In 2007–2008 I was lucky to be a part of the FBReader team. This is an open source free e-Book Reader project.

  We developed the first version of FBReader for Android (now > 10M downloads worldwide on Google play).

  
• 

  Very nerdy!

  There are some things I can't prove but rather just believe in. E.g. this logo I designed and proposed for the CSTheory website.

• 

  Education

  St. Petersburg Academic University is a unique center for continuous education in physics and engineering, run by Zhores Alferov, a Nobel Prize winner in Physics. In 8 years there I finished high school, B.S. and M.S. (a pilot class in theoretical computer science where I was the first student). I am forever grateful to all my teachers during those happy years!

  Here is a recent video (in Russian) about the new bachelors programs at the Academic University.

Graduate School and Academia

• Grad School Application:
  • How to Apply to Graduate School in the United States (by Andrew Ng).
  • Applying to Ph.D. Programs in Computer Science (by Mor Harchol-Balter)
• Job Search:
  • Tips on the Interview Process (by Jeanette Wing, CMU/Microsoft Research)
  • Success in the Job Search, see also “How to Get a Faculty Job” (Part 1a, Part 1b, Part 2 and Part 3) (by Matt Welsh, Harvard/Google)
  • Career Planning in a Research Lab (by Laura Haas, IBM Research)
  • Getting a Job in an Industrial Lab (by Mary Fernandez, AT&T Labs – Research/MentorNet)
  • Reflections on My Tenure-Track Assistant Professor Job Search (by Philip Guo, University of Rochester)
  • Getting a Research Internship (an entry on my blog)
  • Slides from CRA Career Mentoring Workshops (see e.g. slides by Jeanette Wing from 2012)
• Funding:
  • NSF Update Subscription
  • Slides from DARPA Proposer's Day 2015
  • Advice on Writing a Research Grant Proposal (by Sanjeev Arora)
  • CAREER Proposal Examples + Comments (by Adam Klivans, James Lee, Moses Charikar, Piotr Indyk , Salil Vadhan and Sariel Har-Peled)
• Wisdom and Career Advice:
  • You and Your Research (by Richard Hamming)
  • Advice to a Beginning Graduate Student (by Manuel Blum)
  • Essays and Opinions (by Oded Goldreich)
  • Career Advice and On Writing (by Terry Tao)
  • Principles of Effective Research (by Michael Nielsen)
  • Lecture on Getting Rich (by Writing a Book) (by Jeff Ullman)
  • Advice for Graduate Students and Academics (by Matt Might, see relevant topics, e.g. “Graduate School”, “Productivity”, “Writing”, “Giving Presentations”, etc).
  • What Qualities Characterize a Great PhD Student? (on Quora, see David Karger's answer).
  • A Few Words on Research for Graduate Students (by Fan Chung Graham)
  • A Survival Guide to a Ph.D (by Andrej Karpathy)
• Problem Solving: Solving Mathematical Problems (by Terry Tao).
• Problem Archives (if research problems are not enough): IMO, IOI, ACM-ICPC Live Archive, TopCoder, Kaggle.
• Online Seminars on Theoretical Computer Science: TCS+.
• Workshops: Dagstuhl, BIRS, Bertinoro, Simons Center, ICERM, IPAM, MSRI, Shonan Center, Barbados, Fields Institute, Cargese, Oberwolfach, New York Area Theory Day.
• Social: TCS community on Google+.
• Blogs: TCS Blog Aggregator, List of Math Blogs.
• Q&A: CSTheory, MathOverflow. See also top questions on CSTheory and MathOverflow, including “What Papers Should Everyone Read”, “What Books Should Everyone Read”, “What Lecture Notes Should Everyone Read”, “Advice on Good Research Practices”, “Core Algorithms Deployed”, “Algorithms from the Book”, “Examples of Common False Beliefs in Mathematics”, “Refereeing a Paper”, etc.
• Richard Feynman: “Surely You're Joking, Mr. Feynman!” and “What Do You Care What Other People Think?” (both by Ralph Leighton). IMHO, you should better read this when you are a teenager and not take it too seriously, but it's so much fun! ;)
• Comics and Other Fun Stuff (I don't read these, but I know many of my friends like them): XKCD, Abstruse Goose, Ph.D. Comics, Research in Progress.

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