Highlighted Recent Papers

• [NeurIPS’26] Fangru Linghu, Jackson Runtian Ye, Jieying Wang, Alexandre V. Morozov, Ian T. Foster, and Zhao Zhang. “Crafter: Towards Automated Reproducible Machine Learning via Agentic Code Generation”, to appear in NeurIPS 2026.
• [PEARC’26] Mingkai Zheng, Fangru Linghu, Sikan Li, Marty Charles Kandes, Niall Gaffney, Ian T. Foster, and Zhao Zhang. 2026. “A Comparative Study on LLM-enabled HPC User Support”, Proceedings of the Practice and Experience in Advanced Research Computing 2026: Resilient Roots + Empowered Communities. Association for Computing Machinery, New York, NY, USA, Article 9, 1–8. Best Student Paper.
• [TPDS’26] Michael Mogilevsky, Hazem Zaky, Yu Sun, Mingkai Zheng, Lishan Yang, Zhao Zhang. “Quantifying Performance Variability in GPU Clusters” in IEEE Transactions on Parallel and Distributed Systems, vol. 37, no. 6, pp. 1400-1411, June 2026, doi: 10.1109/TPDS.2026.3684387.
• [FGCS’26] Jingxin Wang, Shuyuan Fan, Zhao Zhang. “Ciri: Scaling-aware Job Scheduling for Small Language Models on Over-committed GPU Clusters”, in Future Generation Computer Systems 183, 108523
• [SC’25] Yu Sun, Zachary Coalson, Shiyang Chen, Hang Liu, Zhao Zhang, Sanghyun Hong, Bo Fang and Lishan Yang. “Demystifying the Resilience of Large Language Models: An End-to-End Perspective.” In the International Conference for High-Performance Computing, Networking, Storage, and Analysis (SC25), 2025
• [eScience’25] Xie, Haotian, Rohan Marwaha, Minu Mathew, Song Bian, Gengcong Yang, Minghao Yan, Yadu Babuji, Owen Price, Yinzhi Wang, Volodymyr Kindratenko, Shivaram Venkataraman, Kyle Chard, Ian T. Foster, Zhao Zhang. “Diamond: Harnessing GPU Resources for Scientific Deep Learning.” In 2025 IEEE International Conference on eScience (eScience), pp. 196-204. IEEE, 2025.
• [HPDC’25] Yu Sun, Zhu Zhu, Cherish Mulpuru, Roberto Gioiosa, Zhao Zhang, Bo Fang, Lishan Yang. “FT2: First-Token-Inspired Online Fault Tolerance on Critical Layers for Generative Large Language Models” In Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing, pp. 1-14, 2025.
• [MLSys’25] Zheng, Mingkai, and Zhao Zhang. “Radius: Range-based Gradient Sparsity for Large Foundation Model Pre-training.” In Eighth Conference on Machine Learning and Systems. 2025.
• [Nature Methods’24] Ahdritz, Gustaf, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J. O’Donnell et al. “OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.” Nature Methods (2024): 1-11.
• [NeurIPS’23] Mozaffari, Mohammad, Sikan Li, Zhao Zhang, and Maryam Mehri Dehnavi. “MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates.”. Advances in Neural Information Processing Systems 36 (2024).
• [SC’23] Q. Ding, P. Zheng, S. Kudari, S. Venkataraman, Z. Zhang. “Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-13. 2023.
• [SC’23] E. Karrels, L. Huang, Y. Kan, I. Arora, Y. Wang, D. S. Katz, B. D. Gropp, Z. Zhang. “Fine-grained Policy-driven I/O Sharing for Burst Buffers.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-12. 2023.
• [TPDS’22] J. G. Pauloski, L. Huang, W. Xu, I. T. Foster, Z. Zhang. “Deep Neural Network Training with Distributed K-FAC.” in IEEE Transactions on Parallel and Distributed Systems, doi: 10.1109/TPDS.2022.3161187.
• [SC’21] J. G. Pauloski, Q. Huang, L. Huang, S. Venkataraman, K. Chard, I. T. Foster, Z. Zhang. “KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-14. 2021.
• [Nature Methods’21] Fang, Linjing, Fred Monroe, Sammy Weiser Novak, Lyndsey Kirk, Cara R. Schiavon, Seungyoon B. Yu, Tong Zhang et al. “Deep learning-based point-scanning super-resolution imaging.” Nature methods 18, no. 4 (2021): 406-416.
• [SC’20] J. G. Pauloski, Z. Zhang, L. Huang, W. Xu, I. T. Foster. “Convolutional Neural Network Training with Distributed K-FAC” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-14. 2020.
• [IPDPS’20] Z. Zhang, L. Huang, J. G. Pauloski, I. T. Foster. “Efficient I/O for Neural Network Training with Compressed Data” In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 409-418. IEEE, 2020.
• [CLUSTER’19] Z. Zhang, L. Huang, R. Huang, W. Xu, D. S. Katz. “Quantifying the Impact of Memory Errors in Deep Learning.” In 2019 IEEE International Conference on Cluster Computing (CLUSTER), p.1. IEEE, 2019.
• [TPDS’19] Y. You, Z. Zhang, J. Demmel, K. Keutzer, C. Hsieh. “Fast Deep Neural Network Training on Distributed Systems and Cloud TPUs.” IEEE Transactions on Parallel and Distributed Systems 30, no. 11 (2019): 2449-2462.

Other Papers

• [ISC’21] Evans, Richard Todd, Matthew Cawood, Stephen Lien Harrell, Lei Huang, Si Liu, Chun-Yaung Lu, Amit Ruhela, Yinzhi Wang, and Zhao Zhang. “Optimizing GPU-Enhanced HPC System and Cloud Procurements for Scientific Workloads.” In International Conference on High Performance Computing, pp. 313-331. Springer, Cham, 2021.
• [ICPP’18] Y. You, Z. Zhang, J. Demmel, K. Keutzer, C. Hsieh. “ImageNet Training in Minutes.” In Proceedings of the 47th International Conference on Parallel Processing, p. 1. ACM, 2018. Best Paper Award.
• [HPDC’17] Z. Zhang, E. R. Sparks, and M. J. Franklin. “Diagnosing machine learning pipelines with fine-grained lineage.” In Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing, pp. 143-153. ACM, 2017.
• [FGCS’16] D. S. Katz, A. Merzky, Z. Zhang, S. Jha. “Application Skeletons: Construction and Use in eScience.” Future Generation Computer Systems 59 (2016): 114-124.
• [IPDPS’16] M. Turilli, F. Liu, Z. Zhang, A. Merzky, M. Wilde, J. Weissman, D. S. Katz, and S. Jha. “Integrating abstractions to enhance the execution of distributed applications.” In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 953-962. IEEE, 2016.
• [BigData’16] Z. Zhang, K. Barbary, F. A. Nothaft, E. R. Sparks, O. Zahn, M. J. Franklin, D. A. Patterson, and S. Perlmutter. “Kira: Processing astronomy imagery using big data technology.” IEEE Transactions on Big Data (2016).
• [SIGMOD’15] F. A. Nothaft, M. Massie, T. Danford, Z. Zhang, U. Laserson, Carl Yeksigian, J. Kattalam, A. Ahuja, J. Hammerbacher, M. Linderman, M. J. Franklin, A. D. Joseph, D. A. Patterson. “Rethinking Data-Intensive Science Using Scalable Analytics Systems.”” In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 631-646. ACM, 2015.
• [CIDR’15] D. Crankshaw, P. Bailis, J. E. Gonzalez, H. Li, Z. Zhang, M. J. Franklin, A. Ghodsi, M. I. Jordan. “The Missing Piece in Complex Analytics: Low Latency.” Scalable Model Management and Serving with Velox, 7th Biennial Conference on Innovative Data Systems Research (CIDR), 2015.
• [eScience’14] Z. Zhang, and D. S. Katz. “Using application skeletons to improve escience infrastructure.” In 2014 IEEE 10th International Conference on e-Science, vol. 1, pp. 111-118. IEEE, 2014.
• [SC’13] Z. Zhang, D. S. Katz, T. G. Armstrong, J. Wozniak, I. Foster. “Parallelizing the Execution of Sequential Scripts.” In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, p. 31. ACM, 2013.
• [HPDC’13] Z. Zhang, D. S. Katz, M. Wilde, J. Wozniak, I. Foster. “MTC Envelope: Defining the Capability of Large Scale Computers in the Context of Parallel Scripting Applications.” In Proceedings of the 22nd international symposium on High-performance parallel and distributed computing, pp. 37-48. ACM, 2013.
• [IPDPS’13] T. Li, X. Zhou, K. Brandstatter, D. Zhao, K. Wang, A. Rajendran, Z. Zhang, I. Raicu. “ZHT: A Light-weight Reliable Persistent Dynamic Scalable Zero-hop Distributed Hash Table.” In Parallel & distributed processing (IPDPS), 2013 IEEE 27th international symposium on, pp. 775-787. IEEE, 2013.
• [SC’12] Z. Zhang, D. S. Katz, J. Wozniak, A. Espinosa, I. Foster. “Design and Analysis of Data Management in Scalable Parallel Scripting.” In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, p. 85. IEEE, 2012.
• [CLUSTER’10] I. Raicu, I. Foster, M. Wilde, Z. Zhang, Y. Zhao, A. Szalay, P. Beckman, K. Iskra, P. Little, C. Moretti, A. Chaudhary, D. Thain. “Middleware Support for Many-Task Computing.” Cluster Computing 13, no. 3 (2010): 291-314.
• [Computer’09] M. Wilde, I. Foster, K. Iskra, P. Beckman, Z. Zhang, A. Espinosa, M. Hategan, B. Clifford, I. Raicu. “Parallel Scripting for Applications at the Petascale and Beyond.” Parallel scripting for applications at the petascale and beyond.” Computer 42, no. 11 (2009).
• [SC’08] Ioan Raicu, Zhao Zhang, Mike Wilde, Ian Foster, Pete Beckman, Kamil Iskra, Ben Clifford, “Toward Loosely Coupled Programming on Petascale Systems.” In SC’08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, pp. 1-12. IEEE, 2008.