CV
Education
- M.S. in Applied Data Science, University of Chicago, 2025(expected)
- B.S. in Financial Mathematics, Beijing-Normal-Hong Kong Baptist University(BNBU), 2024
- Summer School: University of Oxford, 2022
Research experience
- 4/2025 – Present: Accelerating Adaptive Algorithms At Scale through Reparameterization
- Supervisor: Prof. Rebecca Willett (The University of Chicago)
- Role: Research Assistant
- Implemented scalable approximations of EGOP: built low-rank estimation and periodic reparameterization modules based on auxiliary EGOP and randomized SVD, enabling faster convergence of Adam/Adagrad in large-scale model training without significant computational overhead.
- Conducted hyperparameter search and ablation studies on Fashion-MNIST, TinyMNIST, and EMNIST (Letters/Digits), comparing models with/without reparameterization, different optimizers, and learning-rate schedules; reported convergence speed, validation accuracy, memory footprint, and runtime to ensure reproducibility of results.
- Related work: Faster Adaptive Optimization via Expected Gradient Outer Product Reparameterization
- 4/2025 – Present: Deep Assortment Optimization
- Supervisor: Prof. Rui Gao (The University of Texas at Austin)
- Role: Research Intern
- Transformed discrete assortment selection into subset distribution learning, applying continuous relaxations (STGS/SIMPLE/NCPSS/SFESS) and permutation-invariant autoregressive modeling with RNN, enabling differentiable optimization under k-subset constraints.
- 4/2025 – Present: Foundation Model Fine-tuning for Breast Cancer Biomarker Prediction
- Supervisor: Prof. Utku Pamuksuz (The University of Chicago)
- Role: Data Scientist
- Adapting Direct Preference Optimization from natural language processing to computational pathology through self-supervised preference construction, enabling parameter-efficient fine-tuning of the TITAN foundation model for improved breast cancer biomarker prediction from whole slide images.
Hackthon Project
- 10/2025: AI-Assisted Clinical Data Navigation Platform
- Host: St. Jude Children’s Research Hospital
- We built an LLM agentic chat interface that lets researchers explore the Childhood Cancer Survivor Study (CCSS) via natural-language questions, automatically translating them to SQL and returning instant summary stats and visualizations across 15k+ de-identified variables and two cohorts (survivors, siblings). Deployed a secure PostgreSQL backend with controlled access, and wired it to LangChain agents, and Azure Cognitive Search to accelerate pre-proposal exploration and variable discovery.
Work experience
- Summer 2023: Assistant for Marketing and Sales Analysis
- Exion Asia (HuiZhou) Co.,Ltd
- Duties includes: analyze large datasets, derive key insights and patterns
- Summer 2022: Customer Service Manager
- Bank of Communications Huizhou Branch
- Duties included: Guided customers in their banking services
Publications