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Research Interests

• Algorithms for Big Data: communication-efficient distributed computation/monitoring, streaming/sketching algorithms, lower bounds.
  
• Theoretical Foundations of Machine Learning: collaborative learning, distributed learning, reinforcement learning.
  
• Quantum Information and Computing: quantum data management.
  

Projects

• Parallel Reinforcement Learning with Communication and Adaptivity Constraints
• Foundation of Communication-Efficient Distributed Computation and Monitoring
• Efficient Distributed Computation of Large-Scale Graph Problems in Epidemiology and Contagion Dynamics
• Efficient Algorithms for Querying Noisy Distributed/Streaming Datasets

Current Teaching

• Fall 2024: B609 Sublinear Algorithms for Big Data
• Fall 2024: B503 Algorithm Design and Analysis

Services

• Program Committees (selected):
         SIGMOD 2025, NeurIPS 2024(area chair), ICML 2024(area chair),
         NeurIPS 2023(area chair), ICML 2023(area chair), IPDPS 2023, PODS 2023,
         NeurIPS 2022(area chair), ICML 2022, ICDE 2022,
         NeurIPS 2021(area chair), ICML 2021, SIGMOD 2021, ICDE 2021, VLDB 2021, SOSA 2021,
         NeurIPS 2020, ICML 2020, PODS 2020, AAAI 2020,
         NeurIPS 2019, ISAAC 2019, IJCAI 2019, IPDPS 2019, ICML 2019,
         NeurIPS 2018, ICML 2018, AAAI 2018, ICLR 2018, BeyondMR 2018,
         NIPS 2017, WAOA 2017, WADS 2017, PODS 2017,
         NIPS 2016, SODA 2016, CIKM 2016,
         CIKM 2015, CIKM 2014, TAMC 2013

Students

• Seba Villalobos (since 2023)
• Artem Iurchenko (since 2023; co-advising with Prof. Amr Sabry)
• Kaiwen Liu (since 2022)
• Nikolai Karpov (PhD 2023; first employment: Oxford Nanopore)
• Chao Tao (PhD 2021; first employment: Google)
• Haoyu Zhang (PhD 2020; first employment: Meta)
• Jiecao Chen (PhD 2019; first employment: Google AI)

Some Papers [ Full List ][ DBLP ]

1. Quantum Data Sketches
   Q. Zhang and M. Heidari (by contribution)
   Proc. International Conference on Database Theory (ICDT 25), to appear. Barcelona, Spain, March 2025.
   
   • We propose succinct quantum data sketches to support basic database operations such as search and selection.
   • We view our work as an initial step towards the development of quantum data management model.

2. Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits (preliminary full version, 37 pages) [conf. talk]
   with C. Tao and Y. Zhou
   Proc. IEEE Symposium on Foundations of Computer Science (FOCS 19), to appear. Baltimore, MD, U.S.A., November 2019.
   
   • We conduct a systematic study of the best arm identification problem in the setting of collaborative learning with limited interaction.
   • We obtain almost tight round-speedup tradeoffs for both fixed-time and fixed-confidence settings, and show a complexity separation for the two variants.
   • We develop two new techniques for proving round lower bounds for multi-agent collaborative learning.
   
   This follow-up work (FOCS 20) extends the work to top-k arm identifications, and shows a separation between the top-1 arm identification and the top-k arm identifications.
   This follow-up work (SPAA 24) extends the work to the heterogeneous collaborative learning (CL) model, and shows a separation between the homogeneous CL model and the heterogeneous CL model for adaptive algorithms.

3. Distributed Partial Clustering (preliminary full version, 20 pages) [conf. talk]
   with S. Guha and Y. Li
   Proc. ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 17), pages 143-152. Washington D.C., U.S.A., July 2017.
   Invited to special issue for SPAA 2017 papers in ACM Transactions on Parallel Computing (TOPC)
   Best Paper Award
   
   • We give the first systematic study of communication/round-efficient distributed partial clustering, by providing almost tight bounds for partial k-center, k-median, and k-means in both deterministic and stochastic settings.
   • As a byproduct, we develop the first algorithms for the partial k-median and k-means objectives that run in subquadratic time.
   
   This follow-up work (NIPS 18) gives a more practical algorithm for distributed clustering with outliers.

4. Edit Distance: Sketching, Streaming and Document Exchange (preliminary full version, 30 pages) [conf. talk]
   with D. Belazzougui
   Proc. IEEE Symposium on Foundations of Computer Science (FOCS 16), pages 51-60. New Brunswick, NJ, U.S.A., October, 2016.
   
   • We achieve nearly optimal communication with almost linear time recovery for document exchange.
   • We obtain the first exact sketching and streaming algorithms for edit distance with sublinear size/space under small distance thresholds.
   • Our technique of aligning strings using multiple random walks may be of independent interest.

5. Tight Bounds for Distributed Functional Monitoring (preliminary full version, 50 pages) [conf. talk]
   with D. P. Woodruff
   Proc. ACM Symposium on Theory of Computing (STOC 12), pages 941-960. New York, NY, U.S.A., May 2012.
   
   • We resolve several fundamental questions in the area of distributed functional monitoring (a.k.a. distributed streaming).
   • Surprisingly, the total communication required to keep monitoring a function is often similar to the corresponding one-shot computation!
   • A new technique called "composition" is proposed for randomized multiparty communication complexity.
   
   This follow-up work (SODA 14) resolves the communication complexity of distinct elements in all parameters.

6. Lower Bounds for Number-in-Hand Multiparty Communication Complexity, Made Easy (preliminary full version, 22 pages) [conf. talk]
   with J. M. Phillips and E. Verbin
   Proc. ACM-SIAM Symposium on Discrete Algorithms (SODA 12), pages 486-501. Kyoto, Japan, January 2012.
   Journal version in SIAM Journal of Computing (SICOMP), volume 45, issue 1, pages 174-196, February 2016 [Link].
   • A new technique called "symmetrization" is proposed for randomized multiparty communication complexity.

7. Optimal Sampling From Distributed Streams (preliminary full version, 25 pages) [talk]
   with G. Cormode, S. Muthukrishnan and K. Yi
   Proc. ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 10), pages 77-86. Indianapolis, IN, U.S.A., June 2010.
   Invited to Journal of the ACM (JACM), volume 59, issue 2, pages 10:1-10:25, April 2012 [Link].
   • In the distributed streaming model, random sampling is even easier than counting.

8. The Limits of Buffering: A Tight Lower Bound for Dynamic Membership in the External Memory Model (preliminary full version, 18 pages) [talk]
   with E. Verbin
   Proc. ACM Symposium on Theory of Computing (STOC 10), pages 447-456. Cambridge, MA, U.S.A., June 2010.
   Journal version in SIAM Journal of Computing (SICOMP), volume 42, issue 1, pages 212-229, January 2013 [Link].
   • We resolve several fundamental problems in the area of external memory data structures.
   • For many basic problems, buffering is impossible to achieve in the external memory model with sublogarithmic query time.
   • The external memory model is clearner than the RAM model in certain perspectives.
   Note: In all papers, except mentioned otherwise, authors are ordered alphabetically.