Background

I am a Statistics Lecturer at the University of Chicago. There, I designed a course on Data Science in Quantitative Finance and Risk Management and taught it to undergraduate students of all majors and backgrounds. What I enjoy most about teaching is that I am able to engage with students on both individual and team levels. My vision is to create a learning community for students to embrace uncertainty, obstruction, and frustration throughout their learning journey. I also practice data science and statistics outside of teaching. On the finance side, my expertise involves designing statistical learning algorithms to evaluate portfolio risks, econometric outcomes, and market microstructures. On the healthcare side, my responsibility as a Business Intelligence Developer includes designing query models to attribute health equity factors, building visualization dashboards that reflect hospital operation patterns, and communicating technical logic to laymen.

Job experience

• June 2021 - present
  Business Intelligence Developer
  Edward-Elmhurst Health
  

  Warrenville, IL

  ● Increase provider care by 30% to patients who lacked medical access due to their health equity profiles, such as financial, social connection, and depression statuses
  ● Innovating Power BI and SQL products to show trend/distribution patterns such as patient risks, health equities scores, and payor claims with the goal to improve healthcare information
  ● Guiding clinicians, analysts, and engineers to deliver concise business requirements and assumptions on the ambulatory side of the operations
  

• June 2022 - present
  Statistics Lecturer
  The University of Chicago
  

  Chicago, IL

  ● Teaching a self-designed course, "Data Science in Quantitative Finance and Risk Management", to 60 pre-college students covering topics ranging from machine learning to risk profiling
  ● Inspiring students to demonstrate leadership and presentation skills through stock market games
  ● Successfully recommended 10 students for admission to renowned STEM and Business programs
  

• May 2020 - April 2021
  Machine Learning Research Assistant
  University of Michigan
  

  Ann Arbor, MI

  ● Innovated machine learning models (e.g. logistic regression) and methods (e.g. principal component analysis) for predicting low-frequency-high-impact events, such as geomagnetic storm and solar flare phenomena
  ● Refined the models by balancing their biases and variances through cross-validation
  ● Guided space weather scientists in framing research questions and utilizing models

• September 2020 - April 2021
  Statistics and Data Analytics Instructor/Mentor
  University of Michigan
  

  Ann Arbor, MI

  ● Instructed over 150 undergraduate Statistics students on theoretical and applied statistical topics including univariate inference and analysis of variance using R programming
  ● Engage students in solving business-related cases using the analytical skills taught
  ● Led new student instructors in teaching reflection and homework planning sessions
  

Education

• University of Michigan
  MS Data Science
  

  2019 - 2021
• University of Connecticut
  BA Mathematics and Actuarial Science, Economics
  

  2016 - 2019