CV
Education
- Doctor of Philosophy (PhD) in Biostatistics - University of California, Los Angeles
September 2024 -
Courses: Uncertainty in LLMs, Agent-based AI
- Master of Science in Computer Science - Northeastern University
January 2023 - May 2024
Courses: Algorithm, Distributed Database
- Master of Science in Statistics and Operations Research - University of North Carolina at Chapel Hill
August 2018 - July 2020
Courses: Applied Statistics, Machine Learning, Time Series Forecasting
Work experience
Software Engineer Intern at Amazon
June 2024 - August 2024
Built features for Amazon B2B on AWS to automate event-driven workflows using EventBridge
Enhanced the Visibility Service using Java, JavaScript, and TypeScript to support real-time monitoring of business transactions
Developed and executed integration tests for outbound services and events to validate service behavior before deployment
Designed and validated CloudWatch dashboards to monitor key service performance metrics
Data Scientist at ByteDance Ltd.
February 2021 - August 2022
Applied XGBoost to evaluate approximately 30 candidate product features, identifying approximately 5 key features associated with order volume and daily user activity to guide product development
Partnered with cross-functional teams to deploy a dynamic subscription tool to improve customer experience
Applied interrupted time-series analysis to estimate potential revenue impact and assess risks of restrictions on creators’ quoting strategies
Used attribution analysis to evaluate approximately 10 proposed marketing campaigns, estimating expected revenue to prioritize approximately 3 campaigns for launch
Data Scientist at Blingby
August 2020 - December 2020
Built data ETL pipelines through Apache Spark to transform raw data into features by combining business sense and statistical knowledge
Developed, maintained web-based dashboards with Tableau to update daily data analysis report, which increased 20% daily work efficiency
Machine Learning Intern at TouchSuite
June 2020 - August 2020
Queried and cleaned terabyte-sized order data from Azure SQL using pyodbc
Conducted online analytical processing (OLAP) to display critical sales performance from different dimensions
Developed item-based approaches to handle cold-start problems and tuned the model hyper-parameters through SparkML cross-evaluation toolbox which reduced root mean square errors by 10%
Skills
Programming Languages: Python (pandas, NumPy, scikit-learn), Java, JavaScript, TypeScript, SQL, R
Tools and Platforms: AWS (EventBridge, CloudWatch), Spark, XGBoost, MySQL, Tableau
Publications
- Multi-Layer Kernel Machines: Fast and Optimal Nonparametric Regression with Uncertainty Quantification
Under R&R at Journal of Machine Learning Research (JMLR), January 2024
Wenlu Xu†, Tong Zhu†, Huiying Zhong, and Xiaowu Dai. [Code]
- Common-Agency Games for Multi-Objective Test-Time Alignment
Preprint, January 2026
Baiting Chen†, Tong Zhu†, Rui Yu†, and Xiaowu Dai. A game-theoretic framework for multi-objective test-time alignment of language models using common-agency games. [Code]
- Mechanism Design Meets Large Language Models: Foundations and Frontiers
Preprint, January 2026
Baiting Chen, Tong Zhu, Xuanang Li, Yichi Zhang, and Xiaowu Dai. A survey of the intersection of mechanism design theory and large language models. [Code]
- Incentivizing Truthful Language Models via Peer Elicitation Games
NeurIPS 2025, September 2025
Tong Zhu†, Baiting Chen†, Jiale Han, Lexin Li, Gang Li, and Xiaowu Dai. Introduces PEG, a training-free framework that uses game-theoretic incentives to elicit truthful LLM behavior without ground-truth labels or fine-tuning.
- Competitive Multi-Agent Delegation For LLM Reasoning
Working paper
This paper introduces COMMAND, a competitive multi-agent delegation framework that treats LLM reasoning as a principal-agent delegation process, using competition and alignment incentives to elicit higher-quality answers.
- LightAgent: Production-level Open-source Agentic AI Framework
arXiv preprint, September 2025
Weige Cai, Tong Zhu, Jinyi Niu, Ruiqi Hu, Lingyao Li, Tenglong Wang, Xin Guo, Zhaowei Liu, Dongpo Cheng, Xiaowu Dai, Weining Shen, and Liwen Zhang. A lightweight, open-source agentic AI framework integrating memory, tool use, and Tree of Thought.