Virginia Tech

College of Engineering
PhD candidate in Computer Science

University of North Carolina at Chapel Hill

Gillings School of Public Health
Master of Science in Biostatistics

Zhejiang University

School of Mathematical Sciences
Bachelor of Science in Statistics

Graduate Research Assistant

Computer Science, Virginia Tech, Blacksburg, USA

• • Applied rigorous statistical methods and core computer science principles to investigate large language model (LLM) behavior, bridging the gap between theoretical guarantees and real-world performance in open-world settings.

Visiting Scholar

Computer Science, Virginia Tech, Blacksburg, USA

• • Conducted research at the intersection of statistical learning theory and foundation models, developing principled probabilistic frameworks to characterize LLM generalization and reliability—laying the theoretical groundwork for subsequent work on model selection and uncertainty quantification.

Graduate Research Assistant

Biostatistics, UNC-Chapel Hill, Chapel Hill, USA

• • Architected scalable AI/ML frameworks that unify heterogeneous biomedical data (single-cell & bulk RNA-seq, EHRs) to generate precise patient-level insights and streamline large-scale comparative analyses.

Student Researcher

Statistics, Zhejiang University, Hangzhou, China

• • Applied advanced ML/DL techniques to both quantitative finance and computational biology, delivering state-of-the-art market-forecasting/trading systems and precision RNA-seq & biomarker pipelines that markedly boost predictive accuracy and operational efficiency.

Software Design Engineer Intern

Alibaba, Hangzhou, China

• • Replaced a legacy static-path system with a Java-based Dynamic Source Routing (DSR) algorithm, embedding a node-energy score that raised end-to-end packet delivery from 88 % to 97 % and cut average latency by 30 % during 500-node stress tests under highly dynamic traffic.

Quantitative Research Intern

Tenbagger Capital Management, Hangzhou, China

• • Built and maintained a web-scraped financial warehouse (5 M+ records across 1,000 equities) capturing key metrics—transaction rate, gross-profit margin, leverage ratio, etc.—to enable rapid factor research.
• • Developed an automated Python long-short strategy driven by those factors, delivering ≈15 % annualized alpha and Sharpe 1.3 in 3-year walk-forward tests.