I am a final-year PhD Candidate in Statistics at The University of British Columbia. My research focuses on Computational Statistics, Machine Learning Algorithm, Time Series Analysis, which allows me to design convex optimization models on sequential time series (and beyond), develop scalable machine learning algorithms, and solve real-life problems with guaranteed numeric accuracy and high-performance computing.

My academic experience as a PhD Researcher and Senior Academic and Teaching Assistant coupled with my industrial experience as a Risk Modeling Analyst at Royal Bank of Canada, Statistical Consultant at applied statistics and data analysi group (UBC), Student Machine Learning Researcher at Statistics Canada, and a Data Analyst Intern at BOSCH have equipped me with extensive experience of collaborating with multi-functional teams including senior researchers, statisticians, data scientists, engineers, business partners, and healthcare professionals.

Over 1️⃣0️⃣ years of experience working on various data-related problems has equipped me with a rigorous understanding of statistical principles, machine learning methodologies, real-life applications on various fields, and efficient computation and reproducible and reliable implementation through software development.

I am passionate about leveraging reliable data-driven methods to drive business impact. I aspire to build my career at the intersection of product data science, machine learning, and business strategy. I thrive in collaborative, cross-functional environments and enjoy solving complex business challenges through analytical insights.

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. Feel free to connect with me on LinkedIn!

This section will showcase some of my projects and professional experiences. Feel free to explore my repositories! Links provided below.

• Retail Risk Modeling Analyst at Royal Bank of Canada (2025)
  • Coming soon.🎥

• Project: Algorithm Development for Accurate Ill-Conditioned Linear System Solver. (2023 - 2025)

  • Derived a modified Kalman filter algorithm in math to solver linear system based on divided difference/discrete derivatives.
  • Coded the algorithm from scratch, 0% -- 💯%. Implementation in C++ is available in glmgen/trendfilter.
  • Finished draft manuscript writing.
  • Tested in simulation showing better numeric accuracy on solving the ill-conditioned linear system compared to existing solvers and staying correct when competitors fail.
  • Collaborators: @dajmcdon et al.

• Project: Methodology Development for Time Series Trend Filtering. (2022 - 2024)

  • Publication on PLoS Computational Biology, August 2024: rtestim: Time-Varying Reproduction Number Estimation with Trend Filtering
  • ✍️Finished the paper writing and revision.
  • 🖥️Coded the machine learning algorithm (proximal Newton, ADMM, and dynamic programming) in C++ from scratch.
  • An R package wrapper is availble in rtestim for scalable computation! 🎉
  • Validated to be more accurate than competitors in synthetic data experiments.
  • Applied the method in Covid-19 transmissibility filtering with adaptive changepoint detection.
  • Collaborators: @dajmcdon, @zcaiElvis, PG.

• Side Project: LLM Fine-Tuning for AI-Generated Text Detection. (2024)

  • Open-Source HuggingFace Model e5-small-lora.
  • Ranked TOP1️⃣ on RAID benchmark leaderboard.
  • Collaborators: @menglinzhou, BZ.

• Internship Project: Machine Learning in Multi-Source Record Linkage. (2019)

  • Applied various classification methods and optimized using nested cross validation.
  • Improved record matching accuracy from the logistic regression baseline of 84% to 99.5%, effectively handling over 100,000 records with 10+ matching features and 30% missing data.
  • Collaborators: Methodology Department, Statistics Canada.

• Side Project: Fincial LLM on Financial textual data analysis

  • Main tasks: semantic analysis, sentiment understanding, time sereis forecasting, and more.
  • Open-source resources fintext-forecasting under active development.

• Side Project: Statflix & Chill: statflix-n-chill

  • A list of useful things that require low efforts for statistical researchers to do while in low energy🪫.
  • Welcome collaborators!👋

• Graduate Academic Assistant on Master's of Data Science, UBC, Vancouver. (2025)
  • Regression Cookbook Writing.
• Senior/Graduate Teaching Assistant at Department of Statistics, UBC, Vancouver. (2020 - 2025)
• Statistics Graduate Student Seminar Organizer, UBC, Vancouver. (2023 - 2025)
  • Peer contributors: @xijohnny, JH.
• Statistical Consultant at Applied Statistics and Statistcal Consultant group, UBC, Vancouver. (2021 - 2023)
  • Senior collaborators: BB, NSK at UBC, Vancouver.
• Master's thesis on Statistial Learning. (2019 - 2020)
  • Thesis Topic on Theoretical Exploration of Generative Adversarial Networks.
  • Supervisor: Professor Maia Fraser, The University of Ottawa.
• Student Machine Learning Researcher project at Statistics Canada. (May - August 2019)
  • Supervisor: AS at Methodology Department, Statistics Canada, Ottawa.
• Data Analysis Intern at BOSCH. (2017 - 2018)
  • Supervisor: BZ at Hangzhou, BOSCH.

Thank you for visiting my profile! Latest update at August 29, 2025.