With a master's degree in Statistics from UC Berkeley, I am specialized in machine learning, deep learning, and reinforcement learning, having extensive experience with programming languages such as Python and C++.
Instructed two sections of 6 hours per week to roughly 55 students about probability, hypothesis testing, estimation, regression, and single variable calculus, creating biweekly quizzes to test students’ understandings of course materials.
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Taught students how to use R to manipulate data frames by base R or the dplyr package, make plots with the ggplot2 package, and use advanced functions such as lm, sapply, etc.
Avoided overlooking potential financial crimes by conducting analysis on mortality grading, term NPR, and deterministic reserve, resolving discrepancies in 3 insurance companies’ reports, and presenting the result to the entire department.
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Boosted work efficiency significantly by 50% and eased compliance checks through generating reports that consist of key statistics, summary data, evaluations, and visualizations based on 35 insurance companies’ VM-31 reports.
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Created a guidebook according to the Actuarial Standard of Practice that standardized the working procedures of all actuaries.
Scraped and cleaned data of political candidates, municipalities, and election results covering 14 years for local offices in California from the website SmartVoter in R to investigate the determinants of female representation in politics.
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Separated more than 45,000 observations into different spreadsheets from 1997 to 2010 and created functions in R to use regular expressions to classify candidates based on education levels.
