Wenjie Lan
About Me
Wenjie Lan is a Master of Science student in Statistics at Duke University. She holds a Bachelor of Science degree in Financial Technology from the Southwestern University of Finance and Economics. Wenjie’s academic and research interests includes data privacy, probabilistic Machine Learning, and bayesian Statistics.
Check my online CV for the most recent update: https://www.overleaf.com/read/whhvmfkhzccj#33418f
During her master’s studies, Wenjie’s research focuses on differential privacy, probabilistic machine learning, and Bayesian statistics. Her summer 2025 research on differential privacy computation for the Gini Index is under the supervision of Prof. Jerome Reiter. Her ongoing 2025–2026 project examines the Hierarchical Conditional Diffusion model, under the guidance of Prof. Jerome Reiter and Prof. David Dunson.
During her undergraduate studies, Wenjie explored two main research tracks: (1) machine learning for risk assessment and prediction (credit risk prediction in ML competitions, 2022–2023; financial distress prediction using GCN+LSTM, summer 2022; systemic risk measurement using CoES, independent study 2023) and (2) applied methods for internship and project problems (bank position management optimization, internship project/patent 2023; high-frequency factor construction via signal processing, internship project 2024; privacy-preserving finance via federated learning, research assistant 2024).
Most importantly, she is grateful to her advisors, collaborators, and friends for their guidance and support.
Technical Skills：
- Programming Languages: Python (Pandas, NumPy, Dask, Scikit-learn, PyTorch), R (dplyr, tidyr, tidymodels, sf, shiny), SQL, C&C++
- Key Areas of Expertise: Probabilistic Machine Learning, Data privacy, Bayesian Statistics
