Congjing ZhangI am a third-year Ph.D. student in the Department of Industrial and Systems Engineering at the University of Washington, advised by Shuai Huang. I am a recipient of the 2025-2027 Amazon AI Ph.D. Fellowship. I received my Master's degree in Industrial Engineering from University of Washington in 2023 and Bachelor's degree in Materials Science and Engineering from ShanghaiTech University in 2021. My research interests center on large language models (LLMs) and machine learning, with a focus on uncertainty quantification, anomaly detection and synthetic data generation. Email / CV / Google Scholar / LinkedIn |
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Work Experience |
| Instacart |
Machine Learning Engineer, Ph.D. Intern Bellevue, WA · June 2025 – Sept. 2025 Developed an off-policy evaluation framework to optimize incentive targeting policies for maximizing gross transaction value. |
| Wyze |
AI Scientist Intern Kirkland, WA · June 2024 – Sept. 2024 Developed the first benchmark for video anomaly detection in smart homes and proposed a novel LLM chaining framework to enhance detection accuracy. |
Publications |
LIFT: Agentic LLMs for Lifecycle-Based Workflow Testing in Healthcare Risk PredictionCongjing Zhang*, Feng Lin*, Noelle So, Meera Rajeev Patel, Mingqian Li, Huiling Liao, Lin Chen, Xiaoning Qian, Shuai Huang The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track (Under Review), 2026 |
Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data GenerationCongjing Zhang*, Feng Lin*, Ruoxuan Bao, Shuai Huang Forty-third International Conference on Machine Learning (Under Review), 2026 Paper / 2026 Best Student Paper Award (1st Place), IISE Data Analytics & Information Systems |
A Role-Based LLM Framework for Structured Information Extraction from Healthy Food PoliciesCongjing Zhang*, Ruoxuan Bao*, Jingyu Li, Yoav Ackerman, Shuai Huang, Yanfang Su Digital Government: Research and Practice (Under Review), 2026 Paper / |
CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative ModelsFeng Lin*, Keyu Tian*, Hanming Zheng, Congjing Zhang, Li Zeng, Shuai Huang INFORMS Journal on Data Science, 2026 Paper / 2025 Best Paper Award Finalist (Runner-Up), INFORMS Quality Statistics and Reliability |
ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty QuantificationCongjing Zhang*, Ryan Feng Lin*, Xinyi Zhao, Pei Guo, Wei Li, Lin Chen, Chaoyue Zhao, Shuai Huang INFORMS Journal on Data Science, 2026 Paper / arXiv / Code / |
Mitigating Misinformation in Health Policy Data Extraction with LLMsCongjing Zhang, Jingyu Li, Xianshan Qu, Shuai Huang, Yanfang Su APPAM Fall Research Conference, 2025 Paper / |
Tools for Policy Insight: Exploring the Healthy Food Policy Project DatabaseJingyu Li, Yoav Ackerman, Congjing Zhang, Shuai Huang, Yanfang Su APPAM Fall Research Conference, 2025 Paper / |
SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language ModelsCongjing Zhang*, Xinyi Zhao*, Pei Guo, Wei Li, Lin Chen, Chaoyue Zhao, Shuai Huang Computer Vision and Pattern Recognition Conference (CVPR) Workshops, 2025 Paper / Code / |
A Pilot Study to Integrate Mobility Data Collection APPs with Personalized Recommendation SystemsCongjing Zhang, Shuai Huang University Transportation Centers, 2025 Paper / |
Genetic Risk Score for Type 1 Diabetes across Ethnic Populations via Multitask LearningMingqian Li, Feng Lin, Congjing Zhang, Kendra Vehik, Hemang M Parikh, Richard A Oram, Xiaoning Qian, Shuai Huang Diabetes, 2024 Paper / |
Incorporating Expert Knowledge into Rule Learning via Reinforcement LearningCongjing Zhang Master’s Thesis, 2023 Paper /
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Service |
| Reviewers |
IISE Transactions on Healthcare Systems Engineering IEEE International Conference on Multimedia and Expo (ICME) IEEE Transactions on Artificial Intelligence |
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Design and source code from Jon Barron's website |