A Research Scientist with a strong foundation in Statistics, Data Science, and Machine Learning.
I am a fifth-year PhD Candidate in Statistics at The University of British Columbia.
My academic experience as a PhD researcher and a Statistical Consultant at an Applied Statistics and Data Science consulting group, coupled with my industrial experience as a Student Machine Learning Researcher at Statistics Canada, and a Data Analyst at BOSCH have equipped me with extensive experience of collaborating with multi-functional teams including senior statisticians and data scientists, engineers, and business partners.
Over 10 years of experience working on various statistical problems has equipped me with a rigorous understanding of statistical principles, methodologies, real-life applications on various fields, and efficient computation and reproducible and reliable implementation through software development.
Beyond the world of data, I enjoy staying active through lifting weights and cardios, fostering community by organizing graduate student seminars, and exploring various other interests in my daily life. I used to be a member in a debate team and a dance team.
This section will showcase some of my projects and experiences. Feel free to explore my repositories to see my work in action!
- Graphical Data Analysis through Convex Optimization with High-Order Divided Difference Regularization with General Loss Beyond Mean Squared Errors.
- Manuscript in preparation.
- Open-source implementation under development.
- Collaborators: @dajmcdon et al.
- Path-Structured Data Analysis through Convex Optimization with High-Order Divided Difference Regularization.
- Manuscript in preparation.
- Open-source R & Python softwares with C++ backend under development.
- Implementation on univariate data in glmgen-verse trendfilter.
- Collaborators: @dajmcdon et al.
- Time Series Analysis through Convex Optimization with
$\ell_1$ Trend Filtering Regularization with Application in Epidemiology.- rtestim: Time-Varying Reproduction Number Estimation with Trend Filtering Publication on PLoS Computational Biology, August 2024.
- Open-source R software with C++ backend rtestim.
- Collaborators: @dajmcdon, @zcaiElvis, PG.
- Fine-Tuned Large Language Models-Based AI-Generated Text Detection for Microsoft Fabric and AI Learning Hackathon 2024.
- Open-Source HuggingFace Model e5-small-lora.
- Ranked TOP on RAID benchmark leaderboard.
- Collaborators: @menglinzhou, BZ.
- Financial text analysis hub for semantic analysis, sentiment understanding, time sereis forecasting, and more.
- Open-source resources fintext-forecasting under active development.
- Statflix & Chill: statflix-n-chill
- A list of useful things that require low efforts for statistical researchers to do while in low energy ๐ชซ.
- Welcome collaborators!
- Statistics Graduate Student Seminar Organizer, UBC, Vancouver in 2023-2025.
- Peer contributors: @xijohnny, JH.
- Statistical Consultant at Applied Statistics and Statistcal Consultant group, UBC, Vancouver in 2021-2023.
- Senior collaborators: BB, NSK at UBC, Vancouver.
- Master's thesis on Theoretical Exploration of Generative Adversarial Networks in 2019-2020.
- Supervisor: Maia Fraser, The University of Ottawa.
- Student Researcher Project at Statistics Canada: An End-to-End Project on Machine Learning for Large-Scale and Multi-Source Record Linkage in 2019.
- Supervisor: AS at Methodology Department, Statistics Canada, Ottawa.
- Data Analysis Internship at BOSCH China in 2017-2018.
- Supervisor: BZ at Hangzhou, BOSCH China.
- Survey studies on various topics including usage of cloud computing for big data for various national competitions in China in 2015-2017.
- Collaborators: JJ, YY, SY, et al.
Thank you for visiting my profile! Latest update at April 11, 2025.