CV

Machine Learning Quant Research Intern

Bloomberg, NY

• Time Series
• Financial Data
• Missing Data
• Out-of-distribution Generalization

Applied Scientist Intern

Amazon, WA

• Deep Generative Models
• Counterfactual
• CausalML

Computer Engineering

Purdue University

Physics

Purdue University

• Transfer to ECE

Physics

Wuhan University

Counterfactual Fairness

Built dedicated deep generative models and corresponding post-processing algorithms for achieving counterfactual fairness.

Fault-tolerant Machine Learning

Designed algorithms that are robust to missing data in the setting of Vertical Federated Learning.

Domain Counterfactual

Proposed a novel design of deep generative models for estimation of counterfactual samples.

Federated Domain Generalization

Developed algorithms for training generative models in a federated setting, enabling the use of SOTA Domain Generalization methods.

Efficient Distribution Matching

Proposed efficient algorithms for mapping between high-dimensional distributions.

Fault-tolerant Quantum Computation

Tested dynamically error-corrected gates for designing robust unitary gates and implementing fault-tolerant quantum computing architectures.

National Scholarship

Wuhan University

Award Top 2 in the major

National Scholarship

Wuhan University

Award Top 2 in the major