About
I am a Senior Computational Statistician at Eli Lilly and Company, responsible for data processing and analysis of early-phase clinical trials in immunology and internal medicine. I earned MS in Biostatistics from Columbia University. Previously, I received a BS in Mathematics from Shandong University in China and a first-class honours in Mathematics from the University of Manchester in the UK.
At Columbia, I worked under the guidance of Dr. Ying Wei and Dr. Tianying Wang on Data-driven dynamic modeling for Alzheimer’s disease progression; Dr. Bin Cheng and Dr. Min Qian on Adaptive Treatment Design and Multi-Armed Bandit Optimization; Dr. Tian Gu on Enhancing Breast Cancer Risk Prediction in Hispanic Women Through Transfer Learning; and Dr. Annie Lee on Broadening gene discovery for AD by incorporating additional cardiovascular and cerebrovascular risk factors and examining the multi-omics profiles of genes to unravel their mechanisms and causal pathways. During undergraduate, I also worked under Dr. Song Yu on Edible Tableware based on Finite Element Analysis and Dr. Jianliang Chen on Black-Scholes Pricing Model Data Simulation by Multilevel Monte-Carlo Method.
Research Interests
My research interests center on developing statistical methodologies to address challenges in precision medicine, genomics, and clinical trials. I am particularly focused on integrating multi-omics data to uncover molecular mechanisms underlying complex diseases like Alzheimer’s and breast cancer, using techniques such as similarity network fusion, causal mediation analysis, and transfer learning. In clinical trials, I am interested in response-adaptive randomization (RAR) and Bayesian methods to optimize treatment allocation, improve statistical power, and reduce sample sizes. Additionally, I aim to advance Bayesian methodologies for modeling high-dimensional genetic data and dynamic processes, enabling robust inferences and actionable insights in biomedical research.
Keywords
• High-dimensional Data; Multi-omics Integration; Spatial Transcriptomics; Precision Medicine; Heterogeneity in Common Conditions (e.g. Alzheimer’s disease, breast cancer, etc.)
• Clinical Trials; Adaptive Design; Response-Adaptive Randomization
• Bayesian Methods; Transfer Learning; Dynamic Processes
Awards
• Sunshine Award (Top 44/3692), “AAAI-2022 AIC Phase VIII: Data-Centric Robust Learning on ML Models.” Jointly organized by Alibaba Security, Tsinghua University, RealAI, and Association for the Advancement of Artificial Intelligence (AAAI) (01/2022)
• Annual Outstanding Individual (Top 1/25) from project “Edible Tableware based on Finite Element Analysis.” Enactus (12/2018)
• Third-level Academic Scholarship (Top 30% in GPA), Shandong University (10/2018)