CV

Liyuan HU

London School of Economics and Political Science | 2022.09 - 2026
Ph.D. in Statistics | Reinforcement Learning Track

Sun Yat-sen University | 2018.09 - 2022.06
B.S. in Statistics, School of Mathematics

GPA: 4.3/5.0 (Ranked 2/78)
Relevant Coursework: Statistical Learning, Complex Data Analysis, Data Structures, Mathematical Statistics
Honors: National Scholarship (2×), First-Class University Scholarship (3×), National Second Prize in Chinese Mathematical Contest in Modeling (2019)

TikTok Global Monetization Product and Technology | July 2025 - Present
Research Scientist, Commerce Ads Technology

Participated in TikTok e-commerce platform GMV Max product development, conducting automated development, data analysis, and A/B testing to improve overall ROI revenue

Huatai Securities Research Institute | April 2025 - July 2025
Alpha Team Researcher, Financial Engineering Group

Enhanced value factors using large language models based on annual report data
Balanced context length limitations, cost constraints, and training effectiveness in machine learning methods for LLM training
Implemented end-to-end factor mining using Graph Neural Networks to model cross-sectional stock return factors

Invesco Great Wall Fund | October 2024 - February 2025
Quantitative Researcher, Quantitative and Index Investment Department

Developed end-to-end index enhancement strategies
Reproduced and improved LinSAT (a differentiable combinatorial optimization neural network component), optimizing training time by nearly 10× while maintaining equivalent performance
Achieved 10%-40% improvement in Information Ratio (IR) for enhancement strategies on CSI 300, CSI 500, and CSI 1000 indices compared to traditional non-end-to-end multi-factor stock selection frameworks

Q-Function Strategy Optimization Addressing Inter-Group Data Correlation | January 2023 - May 2025
First Author

Investigated applications of Generalized Estimating Equations in reinforcement learning
Proposed a novel Fitted Q-iteration algorithm that improves learning strategy effectiveness by estimating inter-group data correlations
Submitted to Statistics Journal

Deterministic Linear Reinforcement Learning Strategy Optimization | August 2023 - Present
First Author

Developed linear deterministic reinforcement learning strategies suitable for device-constrained environments
Addressed existing device limitations in storage and design aspects
Conducted simulation validation on medical school simulators

Strategy Optimization for Non-Stationary Heterogeneous Data | April 2022 - February 2025
First Author

Developed novel reinforcement learning algorithms for temporally non-stationary and individually heterogeneous data
Enhanced reinforcement learning applicability and efficiency in dynamic environments
Preparing submission to Journal of the Royal Statistical Society Series B

COVID-19 County-Level Mortality Risk Analysis in the United States | April 2020 - September 2021
First Author

abess: Fast Best Subset Selection Package (PyPI & R CRAN) | December 2020 - September 2021
First Author

Co-developed the abess library, implementing and extending core algorithms based on C++ kernel
Developed corresponding R interface for the library
Created efficient toolkit for best subset selection problems in machine learning (linear regression, classification, PCA)
Achieved 20× speed improvement compared to existing tools
Published in The Journal of Machine Learning Research

bestridge: Best Subset Selection with Ridge Penalty Package (R CRAN) | February 2020 - March 2021

Programming Languages: Python, C++, R
Specializations: Machine Learning, Reinforcement Learning, Quantitative Finance, Statistical Modeling
Languages: English (TOEFL 108), Chinese (Native), CET-6

Published research in Infectious Diseases of Poverty and The Journal of Machine Learning Research
Multiple submissions to top-tier statistics journals in progress
National-level competition recognition in mathematical modeling
Consistent academic excellence with multiple scholarship awards