Congjing Zhang

I 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.

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Work Experience

Publications

Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation

ALARM: Automated LLM-Based Anomaly Detection in SmaRt-Home Monitoring with Uncertainty Quantification

Mitigating Misinformation in Health Policy Data Extraction with LLMs

SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models

Genetic Risk Score for Type 1 Diabetes across Ethnic Populations via Multitask Learning

Incorporating Expert Knowledge into Rule Learning via Reinforcement Learning

Manuscripts in Preparation

An LLM-Based Integration Framework for Heterogeneous Healthy Food Policy Data Extraction

Benchmarking Fairness of Genetic Risk Score Models for Early-Stage Prediction of Type 1 Diabetes

CrowdLLM: A Synthetic Crowd of LLM-Based Decision-Makers Augmented with Generative Models

*These authors contributed equally.

Design and source code from Jon Barron's website