Research Affiliate at MIT
Software Engineer at GoogleI am a Ph.D. graduated from the University of California, Berkeley who are interested in high performance computing and artificial intelligence. I was advised by Prof. James Demmel, Prof. Raja Sengupta, and Prof. Alexandre Bayen. My research interests ligns in the intersection of high performance computing and artificial intelligence.
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University of California, BerkeleyPh.D.2020 - 2024 -
Tongji UniversityUndergraduate2016 - 2020
Experience
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Massachusetts Institute of TechnologyResearch Affiliate at MITMay. 2023 - Present -
GoogleSoftware EngineerMay. 2024 - Present
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Lawrence Berkeley National LaboratoryGraduate Student ResearcherJun. 2021 - Jun. 2023
Academic Services
IEEE Transactions on Intelligent Transportation Systems, Frontiers in Psychology, Scientific Reports
ICLR 2026, ICML 2026, CVPR 2026, NeurIPS 2025, AAAI 2025, ITSC 2025, TRB Annual Meeting 2025
Selected Awards
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Google Software Engineering Evolution and Transformation (SWEETY) Award2025
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ICRAT Best Paper Award2024
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ASCE ICTD AI in Transportation Committee Outstanding Session Organizer Award2023
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NSF AI Workshop Phase II Travel Award2022
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Joseph M. Sussman Best Paper Prize2021
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Gold Award, 6th China International "Internet+" College Students' Innovation and Entrepreneurship Competition2020
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Outstanding Student Leader, Tongji University2018
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Shanghai Municipal Innovation and Entrepreneurship Project Award2017
Mentored
Selected Publications (view all )
Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation
Xuan Jiang, Raja Sengupta, James Demmel, Samuel Williams
Transportation Research Part C: Emerging Technologies 2024
Proposes a large-scale multi-GPU based parallel traffic simulation framework that significantly accelerates traffic assignment and propagation computations for metropolitan-scale transportation networks.
Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation
Xuan Jiang, Raja Sengupta, James Demmel, Samuel Williams
Transportation Research Part C: Emerging Technologies 2024
Proposes a large-scale multi-GPU based parallel traffic simulation framework that significantly accelerates traffic assignment and propagation computations for metropolitan-scale transportation networks.
Designing a Time-Driven Simulation Framework for Large-Scale Traffic Networks
ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS) 2024
Presents the design and implementation of a time-driven simulation framework specifically optimized for large-scale traffic network simulation.
Designing a Time-Driven Simulation Framework for Large-Scale Traffic Networks
ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS) 2024
Presents the design and implementation of a time-driven simulation framework specifically optimized for large-scale traffic network simulation.
Simulation-Based Optimization for Vertiport Location Selection: A Surrogate Model With Machine Learning Method
Xuan Jiang, Shangqing Cao, Baichuan Mo, Junzhe Cao, Hao Yang, Yuhan Tang, Mark Hansen, Jinhua Zhao, Raja Sengupta
Transportation Research Record 2024
Develops a simulation-based optimization framework using machine learning surrogate models to efficiently optimize vertiport locations for Urban Air Mobility systems.
Simulation-Based Optimization for Vertiport Location Selection: A Surrogate Model With Machine Learning Method
Xuan Jiang, Shangqing Cao, Baichuan Mo, Junzhe Cao, Hao Yang, Yuhan Tang, Mark Hansen, Jinhua Zhao, Raja Sengupta
Transportation Research Record 2024
Develops a simulation-based optimization framework using machine learning surrogate models to efficiently optimize vertiport locations for Urban Air Mobility systems.
Simulating Integration of Urban Air Mobility into Existing Transportation Systems: Survey
Xuan Jiang, Yuhan Tang, Junzhe Cao, Vishwanath Bulusu, Hao Yang, Xin Peng, Yunhan Zheng, Jinhua Zhao, Raja Sengupta
Journal of Air Transportation 2024
Survey on simulation methods for integrating Urban Air Mobility into existing transportation systems, covering key challenges and approaches in UAM operations.
Simulating Integration of Urban Air Mobility into Existing Transportation Systems: Survey
Xuan Jiang, Yuhan Tang, Junzhe Cao, Vishwanath Bulusu, Hao Yang, Xin Peng, Yunhan Zheng, Jinhua Zhao, Raja Sengupta
Journal of Air Transportation 2024
Survey on simulation methods for integrating Urban Air Mobility into existing transportation systems, covering key challenges and approaches in UAM operations.
Devastator: A Scalable Parallel Discrete Event Simulation Framework for Modern C++
John Bachan, Jianlan Ye, Xuan Jiang, Tan Nguyen, Mahesh Natarajan, Maximilian Bremer, Cy Chan
ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS) 2024
Introduces Devastator, a scalable parallel discrete event simulation framework designed for modern C++ that enables efficient simulation of large-scale systems.
Devastator: A Scalable Parallel Discrete Event Simulation Framework for Modern C++
John Bachan, Jianlan Ye, Xuan Jiang, Tan Nguyen, Mahesh Natarajan, Maximilian Bremer, Cy Chan
ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS) 2024
Introduces Devastator, a scalable parallel discrete event simulation framework designed for modern C++ that enables efficient simulation of large-scale systems.
Sparse Matrix in Large Language Model Fine-Tuning
Haoze He, Juncheng Billy Li, Xuan Jiang, Heather Miller
International Conference on Learning Representations (ICLR) 2025
Introduces Sparse Matrix Tuning (SMT), a parameter-efficient fine-tuning method that selects sparse sub-matrices to minimize the performance gap between PEFT and full fine-tuning while reducing computational and memory costs by 67% compared to full fine-tuning.
Sparse Matrix in Large Language Model Fine-Tuning
Haoze He, Juncheng Billy Li, Xuan Jiang, Heather Miller
International Conference on Learning Representations (ICLR) 2025
Introduces Sparse Matrix Tuning (SMT), a parameter-efficient fine-tuning method that selects sparse sub-matrices to minimize the performance gap between PEFT and full fine-tuning while reducing computational and memory costs by 67% compared to full fine-tuning.
ReactionBench: Evaluating Models on Fine-Grained Human Reaction Understanding from Video Stimuli
Yibo Zhao*, Ao Qu*, Xuan Jiang, Keane Ong, Hang Jiang, Zhaofeng Wu, Dingyi Zhuang, Yihong Tang, Kaichen Zhou, Jinhua Zhao, Paul Liang (* equal contribution)
Under review with CVPR 2026
ReactionBench: Evaluating Models on Fine-Grained Human Reaction Understanding from Video Stimuli
Yibo Zhao*, Ao Qu*, Xuan Jiang, Keane Ong, Hang Jiang, Zhaofeng Wu, Dingyi Zhuang, Yihong Tang, Kaichen Zhou, Jinhua Zhao, Paul Liang (* equal contribution)
Under review with CVPR 2026
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
Yuhan Tang*, Kangxin Cui*, Jung Ho Park*, Yibo Zhao*, Xuan Jiang, Haoze He, Jiangbo Yu, Haris Koutsopoulos, Jinhua Zhao (* equal contribution)
Under review with ICML 2026
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
Yuhan Tang*, Kangxin Cui*, Jung Ho Park*, Yibo Zhao*, Xuan Jiang, Haoze He, Jiangbo Yu, Haris Koutsopoulos, Jinhua Zhao (* equal contribution)
Under review with ICML 2026
CloudAnoAgent: Anomaly Detection for Cloud Sites via LLM Agent with Neuro-Symbolic Mechanism
Xinkai Zou, Xuan Jiang, Ruikai Huang, Haoze He, Parv Kapoor, Jiahua Zhao
Under review with ICLR Trustworthy AI 2026
Proposes CloudAnoAgent, the first neuro-symbolic LLM-based agent for anomaly detection in cloud environments that jointly processes structured metrics and textual log data, leveraging symbolic verification to validate detection hypotheses and reduce false positive rates.
CloudAnoAgent: Anomaly Detection for Cloud Sites via LLM Agent with Neuro-Symbolic Mechanism
Xinkai Zou, Xuan Jiang, Ruikai Huang, Haoze He, Parv Kapoor, Jiahua Zhao
Under review with ICLR Trustworthy AI 2026
Proposes CloudAnoAgent, the first neuro-symbolic LLM-based agent for anomaly detection in cloud environments that jointly processes structured metrics and textual log data, leveraging symbolic verification to validate detection hypotheses and reduce false positive rates.
DRBO—A Regional Scale Simulator Calibration Framework Based on Day-to-Day Dynamic Routing and Bayesian Optimization
Xuan Jiang, Yibo Zhao, Chonghe Jiang, Junzhe Cao, Alexander Skabardonis, Alexander Kurzhanskiy, Raja Sengupta
Smart Cities 2025
DRBO—A Regional Scale Simulator Calibration Framework Based on Day-to-Day Dynamic Routing and Bayesian Optimization
Xuan Jiang, Yibo Zhao, Chonghe Jiang, Junzhe Cao, Alexander Skabardonis, Alexander Kurzhanskiy, Raja Sengupta
Smart Cities 2025
Integrating the traffic science with representation learning for city-wide network congestion prediction
Wenqing Zheng, Hao Frank Yang, Jiarui Cai, Peihao Wang, Xuan Jiang, Simon Shaolei Du, Yinhai Wang, Zhangyang Wang
Information Fusion 2023
Investigates how to integrate urban science domain priors into sequential prediction models and proposes the customized Traffic-informed Transformer (TinT) for city-wide network congestion prediction.
Integrating the traffic science with representation learning for city-wide network congestion prediction
Wenqing Zheng, Hao Frank Yang, Jiarui Cai, Peihao Wang, Xuan Jiang, Simon Shaolei Du, Yinhai Wang, Zhangyang Wang
Information Fusion 2023
Investigates how to integrate urban science domain priors into sequential prediction models and proposes the customized Traffic-informed Transformer (TinT) for city-wide network congestion prediction.
All publications
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