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
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.
2025
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.
From Patchwork to Network: A Comprehensive Framework for Demand Analysis and Fleet Optimization of Urban Air Mobility
Xuan Jiang, Xuanyu Zhou, Yibo Zhao, Shangqing Cao, Jinhua Zhao, Mark Hansen, Raja Sengupta
Transportation Research Board (TRB), accepted. 2025
From Patchwork to Network: A Comprehensive Framework for Demand Analysis and Fleet Optimization of Urban Air Mobility
Xuan Jiang, Xuanyu Zhou, Yibo Zhao, Shangqing Cao, Jinhua Zhao, Mark Hansen, Raja Sengupta
Transportation Research Board (TRB), accepted. 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
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
2024
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.
2023
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.
2022
A Metrics-based Method for Evaluating Corridors for Urban Air Mobility Operations
Xuan Jiang, Xin Peng, Vishwanath Bulusu, Cristian Poliziani, Gano Chatterji, Raja Sengupta
IEEE International Smart Cities Conference (ISC2) 2022
Proposes a metrics-based methodology for evaluating and selecting air corridors for Urban Air Mobility operations in urban environments.
A Metrics-based Method for Evaluating Corridors for Urban Air Mobility Operations
Xuan Jiang, Xin Peng, Vishwanath Bulusu, Cristian Poliziani, Gano Chatterji, Raja Sengupta
IEEE International Smart Cities Conference (ISC2) 2022
Proposes a metrics-based methodology for evaluating and selecting air corridors for Urban Air Mobility operations in urban environments.
2021
Quantifying the resilience of the US domestic aviation network during the COVID-19 pandemic
Aleksandar Bauranov, Steven Parks, Xuan Jiang, Jasenka Rakas, Marta C González
Frontiers in Built Environment 2021
Quantifies the resilience of the US domestic aviation network during the COVID-19 pandemic.
Quantifying the resilience of the US domestic aviation network during the COVID-19 pandemic
Aleksandar Bauranov, Steven Parks, Xuan Jiang, Jasenka Rakas, Marta C González
Frontiers in Built Environment 2021
Quantifies the resilience of the US domestic aviation network during the COVID-19 pandemic.