Overview

I am a Ph.D. student in Data Science at the University of Virginia, where I am fortunate to be advised by Dr. Sheng Li at the RISE Lab and Dr. Tom Hartvigsen. My research focuses on three key areas in developing trustworthy Large Language Models (LLMs): (1) developing adaptive reasoning mechanisms to make LLM reasoning more effective and efficient, (2) investigating how reasoning relates to other properties of LLMs that have a strong social impact like fairness and uncertainty estimation, and (3) developing socially intelligent LLM agents empowered by the reasoning capabilities for real-world applications like healthcare. .

Prior to UVA, I completed my M.S. in Biostatistics at Harvard University, where I worked with Dr. William La Cava on fairness-aware machine learning in healthcare applications. This experience laid the foundation for my current research in responsible AI development.

I am seeking summer 2025 internship opportunities to drive innovation in trustworthy, efficient, and socially intelligent LLM development and deployment in industry.

Professional Experience

Chewy — Boston, MA

Data Scientist Intern (06/2022 - 08/2022)

Boston Children Hospital — Boston, MA

Machine Learning Research Intern (Computational Health Informatics Program) (01/2022 - 04/2023)

National Center for Super Computing Application — Champaign, IL

Machine Learning Research Intern (Student Pushing Innovation Program) (06/2019 - 12/2020)

Education

University of Virginia

08/2023 - 05/2027 (Expected)

Doctor of Philosophy in Data Science

Research Focus: LLM Responsible Reasoning & Agents & Social Intelligence

Advisors: Dr. Sheng Li & Dr. Tom Hartvigsen

Harvard University

08/2021 - 05/2023

Master of Science in Biostatistics (Concentration in Data Science)

GPA: 3.89

Relevant courses: Machine Learning in Healthcare, Natural Language Processing, Parallel Computing, Computer Vision

University of Illinois-Urbana, Champaign

09/2017 - 12/2020

B.S. in Statistics (Summa Cum Laude & James Scholar)

Minor: Computer Science & Mathematics

GPA: 3.94

Relevant courses: Data Mining, Data Structures & Algorithm, Database System, Information Retrieval, Machine Learning

Academic Service

Teaching & Review Experience

Teaching Assistant

  • University of Virginia
    • CS 5012: Foundations of Computer Science (Summer 2024)
    • DS 6310: Statistics Inference Theory II: Inference & Prediction (Spring 2024)
    • DS 6030: Statistical Learning (Fall 2023)
  • Harvard University
    • CS 109B: Data Science 2: Advanced Topics in Data Science (Spring 2023)

Conference & Journal Reviews

  • Conference Reviews
    • Neural Information Processing Systems (NeurIPS) 2024
    • International Conference on Machine Learning (ICML) 2025
    • International Conference on Learning Representations (ICLR) 2025
    • International Conference on Artificial Intelligence and Statistics (AISTATS) 2025
  • Journal Reviews
    • IEEE Computational Intelligence Magazine (CIM)

Publications

  1. Wan G, Lu Y, Wu Y, Hu M, Li S. "Bridging causal discovery and large language models: A comprehensive survey of integrative approaches and future directions." IJCAI Under Review [Paper]

  2. Zhou R*, Wan G*, Gabriel S, Li S, Gates AJ, Sap M, Hartvigsen T. (*Equal Contribution) "Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English." ARR Review [Paper]

  3. Wan G, Wu Y, Chen J, Li S. "Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling." NAACL 2025 [Paper]

  4. Wan G, Wu Y, Wang H, Zhao S, Chen J, Li S. "Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning." ARR Under review [Paper]

  5. Wu Y*, Wan G*, Li J, Zhao S, Ma L, Ye T, Pop I, Zhang Y, Chen J. (*Equal Contribution) "ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis." ARR Under Review [Paper]

  6. McCoy JA, Levine LD, Wan G, et al. "Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning." American Journal of Obstetrics and Gynecology (2024) [Paper]

  7. Wan G, Allen J, Ge W, et al. "Two-step light gradient boosted model to identify human west nile virus infection risk factor in Chicago." PLOS ONE 19(1): e0296283 (2024) [Paper]

  8. La Cava WG, Lett E, Wan G. "Fair admission risk prediction with proportional multicalibration." Conference on Health, Inference, and Learning, 350-378 (2023) [Paper]