Jun Li

Associate Professor Department of Computer Science, Queens College and Graduate Center City University of New York Email: jun.li AT qc DOT cuny DOT edu

About Me

Jun Li is an Associate Professor in the Department of Computer Science at Queens College and a doctoral faculty in the Graduate Center, City University of New York (CUNY). Before joining CUNY, he was an Assistant Professor at the School of Computing and Information Sciences, Florida International University. He received his Ph.D. in Electrical and Computer Engineering from the University of Toronto in 2017, and his B.S. and M.S. degrees in Computer Science from Fudan University, China, in 2009 and 2012, respectively.

Merging the gap between theory and practice, his research explores both the theoretical foundations and practical challenges of coding techniques in distributed storage systems, distributed computing, and large-scale machine learning. More recently, he has extended this line of work to include large language models and computer science education, investigating how AI systems can both empower teachers and improve student learning outcomes. His work has been supported by the National Science Foundation, Google, AWS, and CUNY.

Research interests

• Large language models and computer science education

• Coding theory and erasure codes for distributed systems

• Distributed and parallel computing

• Machine learning and distributed training

• Distributed storage systems and data center architectures

Opening

• If you are already an undergraduate/master student in CUNY, you are welcome to contact me if you are interested in working with me as a research assistant.

Publications

Journal Articles

• Local Re-encoding for Coded Matrix Multiplication
  Xian Su, Jared Parker, Xiaomei Zhong, Xiaodi Fan, Jun Li
  IEEE Open Journal of the Communications Society, vol. 3, pp. 1265-1279, 2022.
  [source code]

• Rook Coding for Batch Matrix Multiplication
  Pedro Soto, Xiaodi Fan, Angel Saldivia, Jun Li
  IEEE Transactions on Communications, vol. 70, no. 6, pp. 3641-3654, 2022.
  [source code]

• Demand-aware Erasure Coding for Distributed Storage Systems
  Jun Li, Baochun Li, Bo Li
  IEEE Transactions on Cloud Computing, vol. 9, no. 2, pp. 532-545, 2021.

• Mist: Efficient Dissemination of Erasure-coded Data in Data Centers
  Jun Li, Baochun Li, Bo Li
  IEEE Transactions on Emerging Topics in Computing, vol. 7, no. 8, pp. 468-480, 2019.

• Beehive: Erasure Codes for Fixing Multiple Failures in Distributed Storage Systems
  Jun Li, Baochun Li
  IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 5, pp. 1257-1270, May 2017.

• Erasure Coding for Cloud Storage Systems: A Survey
  Jun Li, Baochun Li
  Tsinghua Science and Technology, vol. 18, no. 3, pp. 259-272, June 2013.

Conference and Workshop Papers

• Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education
  Xinming Yang, Haasil Pujara, Jun Li
  2nd Conference on Language Modeling (COLM), October 2025.
  [source code]

• Sequence-aware Coding for Matrix Multiplication with Arbitrary Recoverability
  Yuchun Zou, Jun Li
  IEEE International Conference on Communications (ICC), June 2024.

• Design Considerations and Analysis of Multi-Level Erasure Coding in Large-Scale Data Centers
  Meng Wang, Jiajun Mao, Rajdeep Rana, John Bent, Serkay Olmez, Garrett Wilson Ransom, Anjus George, Jun Li, Haryadi S. Gunawi
  International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), November 2023.

• On Arbitrary Ignorance of Stragglers with Gradient Coding
  Xian Su, Brian Sukhnandan, Jun Li
  43rd IEEE International Conference on Distributed Computing Systems (ICDCS), July 2023.

• Sequence-Aware Coding for Leveraging Stragglers in Coded Matrix Multiplication
  Xiaodi Fan, Pedro Soto, Yuchun Zou, Xian Su, Jun Li
  IEEE International Conference on Communications (ICC), May 2023.
  [source code]

• Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
  Pedro Soto, Ilia Ilmer, Haibin Guan, Jun Li
  The 39th International Conference on Machine Learning (ICML), July 2022.
  [source code]

• Coded Matrix Chain Multiplication
  Xiaodi Fan, Angel Saldivia, Pedro Soto, Jun Li
  2021 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS), June 2021.
  [full version][source code]

• Leveraging Stragglers in Coded Computing with Heterogeneous Servers
  Xiaodi Fan, Pedro Soto, Xiaomei Zhong, Dan Xi, Yan Wang, Jun Li
  2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS), June 2020.
  [source code]

• Straggler-free Coding for Concurrent Matrix Multiplications
  Pedro Soto, Jun Li
  2020 IEEE International Symposium on Information Theory (ISIT), June 2020.
  [full version]

• Local Re-encoding for Coded Matrix Multiplication
  Xian Su, Xiaomei Zhong, Xiaodi Fan, Jun Li
  2020 IEEE International Symposium on Information Theory (ISIT), June 2020.
  [full version]

• Dynamic Coding for Distributed Matrix Multiplication
  Xian Su, Xiaodi Fan, Jun Li
  NeurIPS 2019 Workshop on Information Theory and Machine Learning, December 2019.

• Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
  Pedro Soto, Jun Li, Xiaodi Fan
  Thirty-sixth International Conference on Machine Learning (ICML), June 2019.
  [slides] [poster] [source code]

• Parallelism-Aware Locally Repairable Code for Distributed Storage Systems
  Jun Li, Baochun Li
  IEEE International Conference on Distributed Computing Systems (ICDCS), July 2018.

• On Data Parallelism of Erasure Coding in Distributed Storage Systems
  Jun Li, Baochun Li
  IEEE International Conference on Distributed Systems (ICDCS), June 2017.

• Multi-Resource Fair Sharing for Datacenter Jobs with Placement Constraints
  Wei Wang, Baochun Li, Ben Liang, Jun Li
  The International Conference for High Performance Computing, Networking, Storage and Analysis (SC), November 2016.

• Zebra: Demand-aware Erasure Coding for Distributed Storage Systems
  Jun Li, Baochun Li
  IEEE/ACM International Symposium on Quality of Service (IWQoS), June 2016.

• Towards Multi-Resource Fair Allocation with Placement Constraints
  Wei Wang, Baochun Li, Ben Liang, Jun Li
  ACM SIGMETRICS 2016 (2-page poster paper), June 2016.

• Beehive: Erasure Codes for Fixing Multiple Failures in Distributed Storage Systems
  Jun Li, Baochun Li
  USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage), July 2015.

• Cooperative Repair with Minimum-Storage Regenerating Codes for Distributed Storage
  Jun Li, Baochun Li
  IEEE Conference on Computer Communications (INFOCOM), April 2014.

• Cooperative Pipelined Regeneration in Distributed Storage Systems
  Jun Li, Xin Wang, Baochun Li
  IEEE Conference on Computer Communications (INFOCOM), April 2013.

• Pipelined Regeneration with Regenerating Codes for Distributed Storage Systems
  Jun Li, Xin Wang, Baochun Li
  International Symposium on Network Coding (NetCod), July 2011.

• Building Regeneration Trees in Distributed Storage Systems with Asymmetric Links
  Jun Li, Shuang Yang, Xin Wang
  International Conference on Collaborative Computing: Networking, Applications, and Worksharing (CollaborateCom 2010), October 2010.

• Generalized DCell Structure for Load-Balanced Data Center Network
  Markus Kliegl, Jason Lee, Jun Li, Xinchao Zhang, Chuanxiong Guo, David Rincon
  IEEE Conference on Computer Communications (INFOCOM), Work-In-Progress Track, March 2010.
  The first four authors share equal contributions.

• Tree-structured Data Regeneration in Distributed Storage Systems with Regenerating Codes
  Jun Li, Shuang Yang, Xin Wang, Baochun Li
  IEEE Conference on Computer Communications (INFOCOM), March 2010.

• Tree-structured Data Regeneration with Network Coding in Distributed Storage Systems
  Jun Li, Shuang Yang, Xin Wang, Xiangyang Xue, Baochun Li
  IEEE International Workshop on Quality of Service (IWQoS), July 2009.

Technical Reports

• The Generalized DCell Network Structures and Their Graph Properties
  Markus Kliegl, Jason Lee, Jun Li, Xinchao Zhang, David Rincon, Chuanxiong Guo
  Microsoft Research TechReport, MSR-TR-2009-140.