Publications

First authors with * are my supervised students

Preprints

• Longchao Da*, Justin Turnau, Thirulogasankar Pranav Kutralingam, Alvaro Velasquez, Paulo Shakarian, Hua Wei. A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models.
• Longchao Da*, Tiejin Chen, Zhuoheng Li, Shreyas Bachiraju, Huaiyuan Yao, Li Li, Yushun Dong, Xiyang Hu, Zhengzhong Tu, Dongjie Wang, Yue Zhao, Ben Zhou, Ram Pendyala, Benjamin Stabler, Yezhou Yang, Xuesong Zhou, Hua Wei. Generative AI in Transportation Planning: A Survey.
• Hua Wei, Guanjie Zheng, Vikash Gayah, Zhenhui Li. A Survey on Traffic Signal Control Methods.
• Longchao Da*, Tiejin Chen, Lu Cheng, Hua Wei. LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentation.
• Xiaoou Liu*, Zhen Lin, Longchao Da, Chacha Chen, Shubhendu Trivedi, Hua Wei. MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels.
• Tiejin Chen*, Xiaoou Liu, Longchao Da, Jia Chen, Evangelos E. Papalexakis, Hua Wei. Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses.

Selected Publications

• Longchao Da*, T Pranav Kutralingam, Lirong Xiang, Hua Wei. Latent Adaptation of Foundation Policies for Sim-to-Real Transfer. In the Fourteenth International Conference on Learning Representations (ICLR’26). April 2026.
• Rana Shahroz, Zhen Tan, Ruichen Zhang, Hua Wei, Tianlong Chen, Charles Fleming. CAR-LoRA: Training Compression-Aware and Robust LoRA Adapters for Evolving LLMs. In the Fourteenth International Conference on Learning Representations (ICLR’26). April 2026.
• Huaiyuan Yao*, Wanpeng Xu*, Justin Turnau, Nadia Kellam, Hua Wei. Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design. In the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL’26 Main Conference). March 2026.
• Tiejin Chen*, Kaishen Wang*, Hua Wei. Zer0-Jack: A memory-efficient gradient-based jailbreaking method for black box Multi-modal Large Language Models. In the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL’26 Main Conference). March 2026.
• Tiejin Chen*, Xiaoou Liu, Vishnu Nandam, Kuan-Ru Liou, Hua Wei. Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignment. In the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL’26 Findings). March 2026.
• Longchao Da*, David Isele, Hua Wei, Manish Saroya. Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving. In Proceedings of the Fourtieth AAAI Conference on Artificial Intelligence (AAAI’26). Feb 2026. (Acceptance rate: 17.6%)
• Tiejin Chen*, Wenwang Huang, Linsey Pang, Dongsheng Luo, Hua Wei. Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape. In ACM SIGKDD Explorations, Dec 2025.
• Arpitsinh Vaghela, Duo Lu, Aayush Atul Verma, Bharatesh Chakravarthi, Hua Wei, Yezhou Yang. MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring, In Proceedings of 28th IEEE International Conference on Intelligent Transportation Systems (ITSC’25). Nov 2025.
• Anirudh Satheesh, Keenan Powell, Hua Wei. cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending, In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM’25). Nov 2025.
• Longchao Da*, Xiangrui Liu*, Mithun Shivakoti, Thirulogasankar Pranav Kutralingam, Yezhou Yang, Hua Wei. DeepShade: Enable Shade Simulation by Text-conditioned Image Generation, in Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI’25).
• Longchao Da*, Xiaoou Liu, Jiaxin Dai, Lu Cheng, Yaqing Wang, Hua Wei. Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology. In Proceedings of the Second Conference on Language Modeling (COLM’25). Oct 2025.
• Tiejin Chen*, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei. Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges. Findings of the Association for Computational Linguistics ACL 2025 (ACL’25 Findings). July 2025.
• Tiejin Chen*, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei. Vision Language Model Helps Private Information De-Identification in Vision Data. Findings of the Association for Computational Linguistics ACL 2025 (ACL’25 Findings). July 2025.
• Justin Turnau*, Longchao Da, Khoa Vo, Ferdous Al Rafi, Shreyas Bachiraju, Tiejin Chen, Hua Wei. Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control. In Proceedings of the 2nd Reinforcement Learning Conference (RLC’25). Aug 2025.
• Jiaxing Zhang*, Xiaoou Liu, Dongsheng Luo, Hua Wei. Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks. In the Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’25). Aug 2025.
• Longchao Da*, Rui Wang, Xiaojian Xu, Parminder Bhatia, Taha Kass-Hout, Hua Wei, Cao Xiao. FlanS – A Foundation Model for Free-Form Language-based Segmentation in Medical Images. In the Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’25). Aug 2025.
• Xiaoou Liu*, Tiejin Chen*, Longchao Da, Chacha Chen, Zhen Lin, Hua Wei. Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey. In the Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’25, Survey Track). Aug 2025.
• Spencer Young, Porter Jenkins, Longchao Da, Jeff Dotson, Hua Wei. Fully Heteroscedastic Count Regression with Deep Double Poisson Networks. In the Proceedings of the 42nd International Conference on Machine Learning (ICML’25). July 2025.
• Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Xufeng Liu, Hua Wei, Haibin Ling, Yi Liu. RISE: Radius of Influence-based Subgraph Extraction for 3D Molecular Graph Explanation. In the Proceedings of the 42nd International Conference on Machine Learning (ICML’25). July 2025.
• Longchao Da*, Parth Mitesh Shah, Kuan-Ru Liou, Jiaxing Zhang, Hua Wei. GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs. In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI’25). Aug 2025.
• Yiran Zhang*, Khoa Vo, Longchao Da, Tiejin Chen, Xiaoou Liu, Hua Wei. Reproducible and Low-cost Sim-to-real Environment for Traffic Signal Control. In the International Conference on Cyber-Physical Systems (ICCPS’25). May 2025.
• Tianshu Bao, Xiaoou Liu*, Meiyi Ma, Taylor T Johnson, Hua Wei. Uncertainty Quantification for Physics-Informed Traffic Graph Networks. In the International Conference on Cyber-Physical Systems (ICCPS’25). May 2025. (Best Artifact Award) [code]
• Huaiyuan Yao*, Longchao Da*, Vishnu Nandam, Justin Turnau, Zhiwei Liu, Linsey Pang, Hua Wei. CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic. In the SIAM International Conference on Data Mining (SDM’25).
• Tiejin Chen*, Longchao Da, Huixue Zhou, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei. Protecting Privacy against Membership Inference Attack with LLM Fine-tuning through Flatness. In the SIAM International Conference on Data Mining (SDM’25). (Short version presented at ICLR 2024 Workshop on Secure and Trustworthy Large Language Models)
• Jiaxing Zhang*, Zhuomin Chen, Hao Mei, Longchao Da, Dongsheng Luo, Hua Wei. 2024. RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks. In the Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NeurIPS’24). (Short version is presented in Learning on Graphs Conference 2023)
• Hua Wei*, Paulo Shakarian*, Christian Lebiere, Bruce Draper, Nikhil Krishnaswamy, Sergei Nirenburg. Metacognitive AI: Framework and the Case for a Neurosymbolic Approach. In International Conference on Neural-Symbolic Learning and Reasoning (NeSy). Springer.
• Longchao Da*, Rohan Chhibba, Rushabh Jaiswal, Ariane Middel, Hua Wei. Shaded Route Planning Using Active Segmentation and Identification of Satellite Images. In Proceedings of 33rd ACM International Conference on Information and Knowledge Management (CIKM’24).
• Qinchen Yang, Zejun Xie, Hua Wei, Desheng Zhang and Yu Yang. MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions. In Proceedings of 33rd ACM International Conference on Information and Knowledge Management (CIKM’24).
• Shuxin Zhong, Hua Wei, Wenjun Lyu, Guang Yang, Zhiqing Hong, Guang Wang, Yu Yang and Desheng Zhang. AdaTrans: Adaptive Transfer Time Prediction for Multi-modal Transportation Modes. In Proceedings of 33rd ACM International Conference on Information and Knowledge Management (CIKM’24).
• Tiejin Chen*, Prithvi Parag Shirke*, Bharatesh Chakravarthi, Arpitsinh Rohitkumar Vaghela, Longchao Da, Duo Lu, Yezhou Yang, Hua Wei. SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras. In Proceedings of 27th IEEE International Conference on Intelligent Transportation Systems (ITSC’24). [code]
• Qinrun Dai, Tiejin Chen, Zicheng Wang, Hua Wei, Yueqi Chen. Stop! Sandboxing Exploitable Functions and Modules Using In-Kernel Machine Learning. BlackHat USA 2024 Briefing.
• Longchao Da*, Chen Chu, Weinan Zhang, Hua Wei. CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2024 (ECML-PKDD 2024), Sep 2024. [code]
• Tianshu Bao, Hua Wei, Junyi Ji, Daniel Work, Taylor Johnson. Spatial-Temporal PDE Networks for Traffic Flow Forecasting. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2024 (ECML-PKDD 2024), Sep 2024
• Jingqing Ruan, Ziyue Li, Hua Wei, Haoyuan Jiang, Jiaming Lu, Xuantang Xiong, Hangyu Mao, Rui Zhao. CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control. In Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2024), Aug 2024
• Longchao Da*, Kuanru Liou, Tiejin Chen, Xuesong Zhou, Xiangyong Luo, Yezhou Yang, Hua Wei. Open-TI: Open Traffic Intelligence with Augmented Language Model. International Journal of Machine Learning and Cybernetics (IJMLC, IF: 5.6).（Presented at ICLR 2024 Workshop on Large Language Model Agents)[code]
• Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Isam, Ananda Mondal, Hua Wei, Dongsheng Luo. 2024. Generating In-Distribution Proxy Graphs for Explainable Graph Neural Networks. In Forty-first International Conference on Machine Learning (ICML’24).
• Dimitris M. Vlachogiannis, Hua Wei, Scott Moura, Jane Macfarlane. HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control. Transportation Research Part C: Emerging Technologies (TR-C), Volume 162, May 2024
• Aayush Atul Verma, Bharatesh Chakravarthi, Arpitsinh Vaghela, Hua Wei, Yezhou Yang. 2024. eTraM: Event-based Traffic Monitoring Dataset. In Conference on Computer Vision and Pattern Recognition 2024 (CVPR’24). [code]
• Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, Dongsheng Luo. 2024. Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks. In International Conference on Learning Representations 2024 (ICLR’24). (Best Paper Runner-up in TrustLOG Workshop@WWW 2024)
• Longchao Da*, Minchiuan Gao, Hao Mei, Hua Wei. 2024. Prompt to transfer: Sim-to-real Transfer for Traffic Signal Control with Prompt Learning. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI’24). (Acceptance rate: 23.75%) [code]
• Longchao Da*, Porter Jenkins, Trevor Schwantes, Jeffrey Dotson, Hua Wei. 2024. Probabilistic Offline Policy Ranking with Approximate Bayesian Computation. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI’24). (Acceptance rate: 23.75%)
• Kai Ye, Tiejin Chen, Hua Wei, Liang Zhan. 2024. Uncertainty Regularized Evidential Regression. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI’24). (Acceptance rate: 23.75%) [code]
• Yuefei Wu, Bin Shi, Bo Dong, Qinghua Zheng, Hua Wei. 2024. The Evidence Contraction Issue in Deep Evidential Regression: Discussion and Solution. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI’24). (Safe, Robust and Responsible AI Track, Acceptance rate: 21.3%)
• Hao Mei*, Xiaoliang Lei, Longchao Da, Bin Shi, Hua Wei. 2023. LibSignal: An Open Library for Traffic Signal Control. Machine Learning (IF: 7.5). 2023. (Short version is presented in NeurIPS 2022 Reinforcement Learning for Real Life Workshop).
• Hao Mei*, Junxian Li, Zhiming Liang, Guanjie Zheng, Bin Shi, Hua Wei. 2023. Uncertainty-aware Traffic Prediction under Missing Data. In Proceedings of the 2023 IEEE International Conference on Data Mining (ICDM 2023). Dec 2023. [code]
• Longchao Da*, Hao Mei, Romir Sharma, Hua Wei. 2023. Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control. In Proceedings of 62nd IEEE Conference on Decision and Control (CDC 2023), Dec 2023. [code]
• Shuya Li, Hao Mei, Jianwei Li, Hua Wei, Dongkuan Xu. 2023. Toward Efficient Traffic Signal Control: Smaller Network Can Do More. In Proceedings of 62nd IEEE Conference on Decision and Control (CDC 2023), Dec 2023.
• Wenlu Du*, Ankan Dash, Jing Li, Hua Wei, Guiling Wang. 2023. Safety in Traffic Management Systems: A Comprehensive Survey. Designs 2023, 7, 100.
• Yuefei Wu, Bin Shi, Jiarun Chen, Yuhang Liu, Bo Dong, Qinghua Zheng, Hua Wei. 2023. Rethinking Sentiment Analysis under Uncertainty. In Proceedings of 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023), Nov 2023.
• Zhanyu Liu, Chunming Liang, Guanjie Zheng, Hua Wei. 2023. FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph. In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023), Sep 2023.
• Longchao Da*, Hao Mei, Romir Sharma, Hua Wei. 2023. Sim2Real Transfer for Traffic Signal Control. In Proceedings of the 19th IEEE International Conference on Automation Science and Engineering (CASE 2023), Aug 2023.
• Jiaxing Zhang*, Dongsheng Luo, Hua Wei. 2023. MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation. In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023), Aug 2023. [code, poster, video]
• Hao Mei*, Junxian Li, Bin Shi, Hua Wei. 2023. Reinforcement Learning Approaches for Traffic Signal Control under Missing Data. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Aug 2023. (Acceptance rate: ~15%) (Short version is presented in NeurIPS 2022 Reinforcement Learning for Real Life Workshop) [code]
• Wanpeng Xu*, Hua Wei. 2023. Learning to Calibrate Hybrid Hyperparameters: a Study on Traffic Simulation. In Proceedings of the ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS’23).
• Zhiyuan Feng*, Kai Qi*, Bin Shi, Hao Mei, Qinghua Zheng, Hua Wei. 2023. Deep Evidential Learning in Diffusion Convolutional Recurrent Neural Network. Electronic Research Archive 2023, Volume 31, Issue 4. [code]
• Longchao Da*, Hua Wei. 2023. CrowdGAIL: a Spatiotemporal Aware Method for Agent Navigation. Electronic Research Archive 2023, Volume 31, Issue 2.
• Wenlu Du*, Junyi Ye, Jingyi Gu, Jing Li, Hua Wei, Guiling Wang. 2023. SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI’23). (Acceptance rate: ~19.6%)
• Hua Wei, Jingxiao Chen, Xiyang Ji, Hongyang Qin, Minwen Deng, Siqin Li, Liang Wang, Weinan Zhang, Yong Yu, Lin Liu, Lanxiao Huang, Deheng Ye, Qiang Fu, Wei Yang. Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning. In Proceedings of the 26th Annual Conference on Neural Information Processing Systems Benchmark and Dataset Track (NeurIPS 2022). [code, slides, poster, bilibili]
• Xiaoliang Lei*, Hao Mei*, Bin Shi, Hua Wei. 2022. Modeling Network-level Traffic Flow Transitions on Sparse Data. In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022), Aug 2022. (Acceptance rate: ~14.9%) [code, slides, poster, video]
• Porter Jenkins, Hua Wei, Stockton Jenkins, Zhenhui Li. 2022. Bayesian Model-based Offline Reinforcement Learning for Product Allocation. The Thirty-Fourth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22).
• Haoyu Geng, Guanjie Zheng, Zhengqing Han, Hua Wei, Zhenhui Li. HMES: A Scalable Human Mobility and Epidemic Simulation System With Fast Intervention Modeling. In Proceedings of the Nineteenth IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2022).
• Porter Jenkins, Hua Wei, J. Stockton Jenkins, and Zhenhui Li. 2021. Probabilistic Simulation of Spatial Demand for Intelligent Product Allocation. In Proceedings of 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, Beijing, China, November 2, 2021 (GeoSim’21)
• Hua Wei, Deheng Ye, Zhao Liu, Hao Wu, Bo Yuan, Bo Yuan, Qiang Fu, Wei Yang, Zhenhui Li. Boosting Offline Reinforcement Learning with Residual Generative Modeling. In Proceedings of the The 30th International Joint Conference on Artificial Intelligence (IJCAI’21), Aug 2021. (Acceptance rate: ~19.3%)
• Guanjie Zheng, Chang Liu, Hua Wei, Porter Jenkins, Chacha Chen, Tao Wen, Zhenhui Li. Knowledge-based Residual Learning. In Proceedings of the The 30th International Joint Conference on Artificial Intelligence (IJCAI’21), Aug 2021. (Acceptance rate: ~19.3%)
• Hua Wei, Dongkuan Xu, Junjie Liang, Zhenhui Li. How Do We Move: Modeling Human Movement with System Dynamics. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI’21), Feb 2021. (Acceptance rate: ~21.4%)
• Dongkuan Xu, Junjie Liang, Wei Cheng, Hua Wei, Haifeng Chen, Xiang Zhang. Transformer-Style Relational Reasoning with Dynamic Memory Updating for Temporal Network Modeling. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI’21), Feb 2021. (Acceptance rate: ~21.4%)
• Hua Wei, Guanjie Zheng, Vikash Gayah, Zhenhui Li. Recent Advances in Reinforcement Learning for Traffic Signal Control: A Survey of Models and Evaluation. In ACM SIGKDD Explorations, Dec 2020.
• Guanjie Zheng*, Chang Liu*, Hua Wei, Chacha Chen, Zhenhui Li. Rebuilding City-wide Traffic Origin Destination from Road Speed Data. In Proceedings of the Thirty-seventh IEEE International Conference on Data Engineering (ICDE’21), Chania, Greece, April 2021.
• Hua Wei*, Xian Wu*, Wenbo Guo*, Xinyu Xing. Adversarial Policy Training against Deep Reinforcement Learning. In Proceedings of the 30th USENIX Security Symposium (USENIX Security) , Vancouver, Canada, Aug 2021. [code]
• Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li. Learning to Simulate on Sparse Trajectory Data. In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020), Ghent, Belgium, Sep 2020. (Acceptance rate: ~27.5%, Best Applied Data Science Paper Award) [code & post]
• Chacha Chen, Hua Wei, Nan Xu, Guanjie Zheng, Ming Yang, Yuanhao Xiong, Kai Xu and Zhenhui Li. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, USA, Feb 2020. (Acceptance rate: ~20.6%)
• Chacha Chen, Guanjie Zheng, Hua Wei, Zhenhui Li. Physics-informed Generative Adversarial Networks. In Proceedings of the NeurIPS Workshop on Interpretable Inductive Biases and Physically Structured Learning (IIBPS, NeurIPS 2020)
• Porter Jenkins, Hua Wei, J. Stockton Jenkins, Zhenhui Li. A Probabilistic Simulator of Spatial Demand for Product Allocation. In the AAAI-20 Workshop on Intelligent Process Automation.
• Hua Wei*, Nan Xu*, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu and Zhenhui Li. CoLight: Learning Network-level Cooperation for Traffic Signal Control. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), Beijing, China, Nov 2019. (Research track with full paper, acceptance rate: ~200/1030=19.4%) [code & post]
• Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu and Zhenhui Li. Learning Phase Competition for Traffic Signal Control. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), Beijing, China, Nov 2019. (Research track with full paper, acceptance rate: ~200/1030=19.4%)
• Hua Wei, Chacha Chen, Guanjie Zheng, Kan Wu, Vikash V. Gayah, Kai Xu and Zhenhui Li. PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, AK, USA, Aug 2019. (Research track with poster presentation, acceptance rate: ~170/1200=14.2%)[code & demo]
• Hongjian Wang, Porter Jenkins, Hua Wei, Fei Wu and Zhenhui Li. Learning Task-Specific City Region Partitions. In Proceedings of the 2019 the Web Conference (WWW 2019), San Francisco, CA, May 2019.
• Hua Wei*, Guanjie Zheng*, Huaxiu Yao and Zhenhui Li. IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, UK, Aug 2018. (Research track with long presentation, acceptance rate: 181/983=18.4%)[code & demo]
• Yuandong Wang, Xuelian Lin, Hua Wei, Tianyu Wo, Zhou Huang, Yong Zhang, Jie Xu. A Unified Framework with Multi-source Data for Predicting Passenger Demands of Ride Services. ACM Transactions on Knowledge Discovery from Data (TKDD).
• Huaxiu Yao*, Xianfeng Tang*, Hua Wei, Guanjie Zheng and Zhenhui Li. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, Jan. 2019.
• Yong Zhang, Hua Wei, Xuelian Lin, Fei Wu, Zhenhui Li, Kaiheng Chen, Yuandong Wang and Jie Xu. Context-aware Location Annotation on Mobility Records through User Grouping. In Proceedings of the 22nd Pacific-Asia Conference on Know-ledge Discovery and Data Mining (PAKDD 2018), Melbourne, Australia, June 2018. (Regular paper with oral presentation, acceptance rate: 59/590=10%)
• Hua Wei, Yuandong Wang, Tianyu Wo, Yaxiao Liu and Jie Xu. ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN, USA, October 2016
• Zhongyu Lu, Weiren Yu, Richong Zhang, Jianxin Li and Hua Wei. Discovering Event Evolution Chain in Microblog. 17th IEEE International Conference on High Performance Computing and Communications (HPCC 2015). New York, USA.
• Borui Yang, Jianxin Li, Lu Liu, Yingjie Cao, Hua Wei, Peiyuan Sun, Nannan Wu and Bo Li. ShutterRoller: Preserving Social Network Privacy towards High-Speed Domain Gateway, the 13th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2015), Liverpool, UK.