About Me

I am an assistant professor in the Department of Computer Science & Engineering (CSE), also affiliated with World Sustainable Development Institute, at the Hong Kong University of Science and Technology (HKUST). I am leading the Relaxed System Lab. I am working on research topics, including machine learning for data management and distributed/decentralized machine learning systems. Currently, I am mainly focusing on recruiting top talent students to work on AReaL system. Please email me your latest resume if you are interested in joining us.

Before joining HKUST, I was a Postdoctoral Researcher in the Computer Science Department at ETH Zurich, under the supervision of Prof. Ce Zhang. I completed my Ph.D. program in the Computer Science Department at Rice University. My adviser was Prof. Chris Jermaine and I was co-advised by Prof. Anastasios Kyrillidis for my Ph.D. thesis. I got my master degree from the Computer Science Department at Rice University, supervised by Prof. Ron Goldman, and a bachelor degree from the Computer Science Department at Fudan University.

Teaching

• COMP4551 (Spring 2026)
• COMP4091Y (Spring 2024, Spring 2025)
• COMP6211J (Fall 2024, Fall 2025)

Current Group Members at CSE, HKUST

PhD Students:

• Ran Yan (2023-Fall, BS@Peking University)
• Tianyi Bai (2023-Fall, BS@Beijing Institute of Technology)
• Fangyu Ding (2024-Fall, BS,MS@Shanghai Jiao Tong University)
• Guangxin He (2024-Fall, BS@University of Chinese Academy of Sciences, MS@Chinese Academy of Sciences)
• Zipeng Qiu (2024-Fall, BS@Fudan University)
• You Peng (2024-Fall, BS@University of Toronto)
• Chenyue Li (2024-Fall, BS@University of Toronto)
• Yukun Zhou (2024-Fall, BS@Nanjing University, MS@Tsinghua University, Co-supervised with Prof. Wei Wang)
• Ding Pan (2025-Spring, BS@Shanghai Jiao Tong University, MS@Peking University)
• Xu Xu (2025-Fall, BS@Harbin Institute of Technology, MS@Peking University)
• Jiayi Cheng (2025-Fall, BS,MS@Cornell University)
• Xiaochen Ma (2026-Spring, BS@Sichuan University)
• Daifeng Li (2026-Fall, BS@University of Science and Technology of China)
• Wenshuang Li (2026-Fall, BS@Tsinghua University)

Research Assistants:

• Youhe Jiang (Now PhD@University of Cambridge)

Research Interests

My main research focuses are data management for machine learning and distributed/decentralized machine learning systems. Concretely, I build systems to support giant foundation models over distributed, heterogeneous, and decentralized environments.

Selected Talks

• 2024 AI Tide Tianchi AI Developer Workshop @ HKUST “2024 - Accommodating LLM Service over Heterogeneous Computational Resources” [Slides].

• 2024 Huawei STW @ Shenzhen “On the Opportunities of Designing the Next Generation of Collective Communication Libraries for AI-centric Workflows” [Slides].

• 2024 Climate, Weather and Water Forum @ HKUST “On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models” [Slides].

• 2023 MLsys Symposium @ Miami “Accommodating LLM Training over Decentralized Computational Resources” [Slides].

Selected Publications

• Xiaozhe Yao, Youhe Jiang, Ilia Badanin, Qinghao Hu, Binhang Yuan, Imanol Schlag, Eiko Yoneki, and Ana Klimovic. “LLMFabric: Unifying Decentralized HPC Clusters for Heterogeneous LLM Serving” To Appear in 20th USENIX Symposium on Operating Systems Design and Implementation, 2026. (OSDI 2026)

• You Peng*, Youhe Jiang*, Wenqi Jiang, Chen Wang, and Binhang Yuan “Hexgen-Flow: Optimizing LLM Inference Request Scheduling for Agentic Text-to-SQL” To Appear in the 42nd IEEE International Conference on Data Engineering, 2026. (ICDE 2026)

• Ran Yan*, Youhe Jiang *, Xiaonan Nie, Fangcheng Fu, Bin Cui, and Binhang Yuan “HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware.” To Appear in 9th Annual Conference on Machine Learning and Systems (MLSys 2026)

• Guangxin He, Shen Nie, Fengqi Zhu, Yuankang Zhao, Tianyi Bai, Ran Yan, Jie Fu, Chongxuan Li, and Binhang Yuan “UltraLLaDA: Scaling the Context Length to 128K for Diffusion Large Language Models.” To Appear in the 14th International Conference on Learning Representations 2026. (ICLR 2026)

• Di Chai, Pengbo Li, Feiyuan Zhang, Yilun Jin, Han Tian, Kaiqiang Xu, Binhang Yuan, Dian Shen, Junxue Zhang, and Kai Chen “Unlocking Full Efficiency of Token Filtering in Large Language Model Training.” To Appear in the 14th International Conference on Learning Representations 2026. (ICLR 2026)

• Ran Yan*, Youhe Jiang*, Zhuoming Chen, Haohui Mai, Beidi Chen, and Binhang Yuan.”FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel.” To Appear in the 14th International Conference on Learning Representations 2026. (ICLR 2026)

• Youhe Jiang*, Fangcheng Fu*, Wanru Zhao*, Stephan Rabanser, Jintao Zhang, Nicholas D. Lane, and Binhang Yuan “Cascadia: An Efficient Cascade Serving System for Large Language Models.” To Appear in the 14th International Conference on Learning Representations 2026. (ICLR 2026)

• Lingfei Zeng, Fengdi Che, Xuhan Huang, Fei Ye, Xu Xu, Binhang Yuan, and Jie Fu “VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally.” To Appear in the 14th International Conference on Learning Representations 2026. (ICLR 2026)

• Fangyu Ding, Ding Ding, Sijin Chen, Kaibo Wang, Peng Xu, Zijin Feng, Haoli Bai, Kai Han, Youliang Yan, Binhang Yuan, and Jiacheng Sun “Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes.” To Appear in the 14th International Conference on Learning Representations 2026. (ICLR 2026)

• Xu Xu*, Xin Li*, Xingwei Qu, Jie Fu, and Binhang Yuan “Local Success Does Not Compose: Benchmarking Large Language Models for Compositional Formal Verification.” To Appear in the 14th International Conference on Learning Representations 2026. (ICLR 2026)

• Yuxiang Huang, Binhang Yuan, Xu Han, Chaojun Xiao, and Zhiyuan Liu. “Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade Devices” in Transactions on Machine Learning Research, 2025. (TMLR 2025)

• Wei Fu, Jiaxuan Gao, Xujie Shen, Chen Zhu, Zhiyu Mei, Chuyi He, Shusheng Xu, Guo Wei, Jun Mei, Jiashu Wang, Tongkai Yang, Binhang Yuan, and Yi Wu. “AReaL: Asynchronous Reinforcement Learning for Efficient and Scalable Language Reasoning.” In Advances in Neural Information Processing Systems 39 (2025). (NeurIPS 2025)

• Chenyue Li, Wen Deng, Mengqian Lu, and Binhang Yuan. “AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model for Atmospheric Science” In Advances in Neural Information Processing Systems 39 (2025). (NeurIPS 2025)

• Tianyi Bai, Zengjie Hu, Fupeng Sun, Qiu Jiantao, Yizhen Jiang, Guangxin He, Bohan Zeng, Conghui He, Binhang Yuan, and Wentao Zhang. “Multi-step Visual Reasoning with Visual Tokens Scaling and Verification.” In Advances in Neural Information Processing Systems 39 (2025). (NeurIPS 2025)

• Tianyi Bai, Yuxuan Fan, Qiu Jiantao, Fupeng Sun, Jiayi Song, Junlin Han, Zichen Liu, Conghui He, Wentao Zhang, and Binhang Yuan. “Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning.” In Advances in Neural Information Processing Systems 39 (2025). (NeurIPS 2025)

• Guoliang He, Youhe Jiang, Wencong Xiao, Jiang Kaihua, Shuguang Wang, Jun Wang, Du Zixian, Zhuo Jiang, Xinlei Zhang, Binhang Yuan, and Eiko Yoneki. “Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs.” In Advances in Neural Information Processing Systems 39 (2025). (NeurIPS 2025)

• Rizhen Hu, Yutong He, Ran Yan, Mou Sun, Binhang Yuan, and Kun Yuan. “MeCeFO: Enhancing LLM Training Robustness via Fault-Tolerant Optimization.” In Advances in Neural Information Processing Systems 39 (2025). (NeurIPS 2025)

• Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Guoliang HE, Xupeng Miao, Ana Klimovic, Bin Cui, Binhang Yuan, and Eiko Yoneki. “Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs.” In the International Conference on Machine Learning. (ICML 2025)

• Tianyi Bai, Ling Yang, ZhenHao Wong, Fupeng Sun, Xinlin Zhuang, Jiahui Peng, Chi Zhang, Lijun Wu, Jiantao Qiu, Wentao Zhang, Binhang Yuan, and Conghui He “Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration.” In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, pp. 9465-9491. (ACL 2025).

• Suyi Li, Hanfeng Lu, Tianyuan Wu, Minchen Yu, Qizhen Weng, Xusheng Chen, Yizhou Shan, Binhang Yuan, and Wei Wang. “Toppings: CPU-Assisted, Rank-Aware Adapter Serving for LLM Inference.” In the 2025 USENIX Annual Technical Conference, pp. 613-629. (ATC 2025).

• Wenjie Qu*, Yuguang Zhou*, Yongji Wu, Tingsong Xiao, Binhang Yuan, Yiming Li, and Jiaheng Zhang. “Prompt Inversion Attack against Collaborative Inference of Large Language Models.” In IEEE Symposium on Security and Privacy, pp. 1695-1712, 2025 (S&P 2025).

• Jinwei Yao, Kaiqi Chen, Kexun Zhang, Jiaxuan You, Binhang Yuan, Zeke Wang, and Tao Lin. “DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference.” In the 13th International Conference on Learning Representations 2025. (ICLR 2025 Selected as Spotlight)

• Youhe Jiang*, Ran Yan*, and Binhang Yuan. “HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment.” In the 13th International Conference on Learning Representations 2025. (ICLR 2025)

• Changyue Liao, Mo Sun, Zihan Yang, Jun Xie, Kaiqi Chen, Binhang Yuan, Fei Wu, and Zeke Wang. “Ratel: Optimizing Holistic Data Movement to Fine-tune 100B Model on a Consumer GPU.” In the 41st IEEE International Conference on Data Engineering, pp. 292-306, 2025. (ICDE 2025)

• Yongjun He, Roger Waleffe, Zhichao Han, Johnu George, Binhang Yuan, Zitao Zhang, Yinan Shan, Yang Zhao, Debojyoti Dutta, Theodoros Rekatsinas, and Ce Zhang. “MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage.” In the 41st IEEE International Conference on Data Engineering, pp. 4134-4141. 2025 (ICDE 2025)

• Xinyu Zhao*, Guoheng Sun*, Ruisi Cai*, Yukun Zhou*, Pingzhi Li*, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, and Tianlong Chen. “Model-Glue: Democratized LLM Scaling for A Large Model Zoo in the Wild.” In Advances in Neural Information Processing Systems 38 (2024), 13349-13371. (NeurIPS 2024)

• Youhe Jiang*, Ran Yan*, Xiaozhe Yao*, Yang Zhou, Beidi Chen, and Binhang Yuan. “HexGen: Generative Inference of Large-Scale Foundation Model over Heterogeneous Decentralized Environment. “ In International Conference on Machine Learning (pp. 21946-21961). PMLR. (ICML 2024)

• Lin Lu*, Chenxi Dai*, Wangcheng Tao, Binhang Yuan, Yanan Sun, and Pan Zhou. “Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training.” In International Conference on Machine Learning (pp. 32978-32989). PMLR. (ICML 2024)

• Alexandre E Eichenberger, Qi Lin, Saif Masood; Hong Min, Alex Sim, Yida Wang, Kesheng Wu, Binhang Yuan, Lixi Zhou, and Jia Zou. “Serving Deep Learning Model in Relational Databases.” In 27th International Conference on Extending Database Technology 2024. EDBT, pp. 717-724. (EDBT 2024)

• Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, and Ce Zhang. “High-throughput Generative Inference of Large Language Models with a Single GPU.” In International Conference on Machine Learning (pp. 31094-31116). PMLR. (ICML 2023 Selected as Oral).

• Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, and Beidi Chen. “Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time.” In International Conference on Machine Learning (pp. 22137-22176). PMLR. (ICML 2023 Selected as Oral).

• Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, and Ce Zhang. “CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks.” In International Conference on Machine Learning (pp. 36058-36076). PMLR. (ICML 2023）

• Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, and Chris Jermaine. “Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning.” In International Conference on Machine Learning (pp. 33581-33598). PMLR. (ICML 2023)

• Binhang Yuan*, Yongjun He*, Jared Quincy Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Re, and Ce Zhang. “Decentralized Training of Foundation Models in Heterogeneous Environments.” In Advances in Neural Information Processing Systems 36 (2022), 25464-25477. (NeurIPS 2022 Selected as Oral)

• Jue Wang*, Binhang Yuan*, Luka Rimanic*, Yongjun He, Tri Dao, Beidi Chen, Christopher Re, and Ce Zhang. “Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees.” In Advances in Neural Information Processing Systems 36 (2022), 19215-19230. (NeurIPS 2022)

• Rui Pan, Yiming Lei, Jialong Li, Zhiqiang Xie, Binhang Yuan, and Yiting Xia. “Efficient Flow Scheduling in Distributed Deep Learning Training with Echelon Formation.” In Proceedings of the twenty-first ACM Workshop on Hot Topics in Networks (2022). (HotNets 2022)

• Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, and Ji Liu. “Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters.” In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 3288-3298. 2022 (SIGKDD 2022)

• Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cedric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, and Ce Zhang. “In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle.” In Proceedings of the 2022 International Conference on Management of Data, pp. 1286-1300. 2022. (SIGMOD 2022)

• Binhang Yuan, Cameron R. Wolfe, Chen Dun, Yuxin Tang, Anastasios Kyrillidis, and Chris Jermaine. “Distributed Learning of Deep Neural Networks using Independent Subnet Training.” In Proceedings of the VLDB Endowment, 15(8). (VLDB 2022)

• Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, and Ce Zhang. “BAGUA: Scaling up Distributed Learning with System Relaxations.” In Proceedings of the VLDB Endowment, 15(4). (VLDB 2022)

• Shangyu Luo, Dimitrije Jankov, Binhang Yuan, and Chris Jermaine. “Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra.” In Proceedings of the 2021 International Conference on Management of Data(pp. 1222-1234). ACM. (SIGMOD 2021)

• Binhang Yuan, Dimitrije Jankov, Jia Zou, Yuxin Tang, Daniel Bourgeois, and Chris Jermaine. “Tensor Relational Algebra for Machine Learning System Design.” In Proceedings of the VLDB Endowment, 14(8), 1338-1350 (VLDB 2021)

• Jia Zou, Pratik Barhate, Amitabh Das, Arun Iyengar, Binhang Yuan, Dimitrije Jankov, and Chris Jermaine. “Lachesis: Automatic Partitioning for UDF-Centric Analytics.” In Proceedings of the VLDB Endowment, 14(8), 1262-1275 (VLDB 2021)

• Dimitrije Jankov, Binhang Yuan, Shangyu Luo, and Chris Jermaine. “Distributed Numerical and Machine Learning Computations via Two-Phase Execution of Aggregated Join Trees.” In Proceedings of the VLDB Endowment, 14(7), 1228-1240. (VLDB 2021)

• Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, and Zekai J Gao. “Declarative recursive computation on an RDBMS: or, why you should use a database for distributed machine learning.” In Proceedings of the VLDB Endowment, 12(7), 822-835. (VLDB 2019 Best Paper Honorable Mention Award, SIGMOD 2020 Reserch Highlight)

• Jia Zou, R Matthew Barnett, Tania Lorido-Botran, Shangyu Luo, Carlos Monroy, Sourav Sikdar, Kia Teymourian, Binhang Yuan, and Chris Jermaine. “PlinyCompute: A platform for high-performance, distributed, data-intensive tool development.” In Proceedings of the 2018 International Conference on Management of Data(pp. 1189-1204). ACM. (SIGMOD 2018)

• Binhang Yuan, Vijayaraghavan Murali, and Chris Jermaine. “Abridging source code.” In Proceedings of the ACM on Programming Languages 1. OOPSLA (2017): 58. (OOPSLA 2017)

• Bo Yan, Binhang Yuan, and Bo Yang. “Effective video retargeting with jittery assessment”. In IEEE Transactions on Multimedia, Vol. 16, Issue 1, pp. 272-277, Jan. 2014. (TMM 2014)

Education

• Ph.D. Computer Science Department Rice University (2016/08 - 2020/12)

• M.S. Computer Science Department Rice University (2013/08 - 2016/05)

• B.S. Computer Science Department Fudan University (2009/09 - 2013/07)

Work Experience

• Research Intern Microsoft Research Asia (2017/07 - 2017/12)

• SDE Intern Tableau Software (2016/05 - 2016/08)

• SDE Intern EMC Software (2015/05 - 2015/08)

Academic Service

• Conference:
  • AAAI Reviewer: 2020, 2021
  • ICDE Publication and Proceedings Chair: 2025
  • ICLR Reviewer: 2022, 2023, 2024, 2025
  • ICML Reviewer: 2021, 2022, 2023, 2024, 2025
  • MLsys Reviewer: 2024, 2025, Symposium Organizer: 2023, AE PC member: 2022
  • NeurIPS Reviewer: 2020, 2021, 2022, 2023, AC: 2024, 2025
• Journal:
  • IEEE Access Reviewer: 2020
  • IEEE TKDE Reviewer: 2022
  • JMLR Reviewer: 2023
  • IEEE BigData 2023
  • PVLDB Reviewer: 2022-2023
  • TMLR Reviewer: 2023-2024, 2024-2025, Actor Editor: 2025-2026

Hobbies

Most of my spare time (if there is any) is spent on football. I am a big fan of Liverpool. As Klopp once said, “98% of football is about dealing with failure and still being able to smile and find joy in the game the next day.”, which is also true for research, I believe.