University of Illinois at Urbana-Champaign

  • Master of Science – MS, Statistics – Analytics
    Relevant Coursework: *Statistical Data Management, *Mathematical Statistics, *Multivariate Analysis

University of Illinois at Urbana-Champaign

  • Bachelor of Science – BS, Statistics, Minor in Mathematics
    Relevant Coursework: Statistical Computing (R/Rstudio), Unsupervised Learning (Python/Jupyter Lab), Basics of Statistical Learning (R/Rstudio), Professional Statistics, Applied Regression and Design (R/Rstudio), Methods of Applied Statistics (R/Rstudio), Statistics & Probability, Statistical Analysis, Linear Algebra, Fundamental Mathematics

Parkland College

  • Associate of Science – AS, Computer Science
    Relevant Coursework: Data Structures (Python), Computer Science (C/C++, Java), Discrete Mathematics, Calculus, Statistics

Work Experience

Graduate Teaching Assistant
Department of Statistics, University of Illinois at Urbana-Champaign
Aug 2021 – Present

  • Support student learning objectives through teaching lab/discussion sections and in office hours
  • Develop new content for weekly homework assignments and other assessments
  • Assist instructors with other labs, grading, interacting with students through discussion forums, and other related tasks


  • Stat 107: Data Science Discovery
    • [Fall 2021]
      Course Description: Data Science Discovery is the intersection of statistics, computation, and real-world relevance. As a project-driven course, students perform hands-on-analysis of real-world datasets to analyze and discover the impact of the data. Throughout each experience, students reflect on the social issues surrounding data analysis such as privacy and design.
      Supervisors: Karle Flanagan and Wade Fagen-Ulmschneider

Data Science Lead Instructor
Discovery Partners Institute, University of Illinois at Chicago CHANCE Program, University of Illinois at Chicago
May 2021 – August 2021

  • Digital Scholars "Discovering Data Science" Course [Summer 2021]
    Program Description: Developed and taught the new "Discovering Data Science" course of DPI and UIC CHANCE's Digital Scholars Program, which is a five-week intensive summer program for high school and incoming college students of Chicago to support and develop promising and diverse tech talent in Illinois.
    Supervisors: Mark Harris

Undergraduate Teaching Assistant
Department of Statistics, University of Illinois at Urbana-Champaign
Jan 2019 – May 2021

  • Supported student learning objectives through personalized and small group assistance in in-person and virtual office hours, and via email for courses of 50 to 1000+ students
  • Assisted instructor with test administration, curriculum development, website development (Jekyll, Jupyter Notebook), grading (~8500 assignments/projects/exams graded), office hours, coding tasks (R/Rstudio, LaTex, Python), and substitute teaching for lectures
  • Conducted a lecture to a class of 150 undergraduate students on random variables and mathematical expectation for STAT 400 on February 4, 2020.
  • Average course sizes: ~285 students per section. ~615 students per course.


  • The David H. Blackwell Summer Scholars Program
    • [Summer 2021]
      Program Description: The David H. Blackwell Summer Scholars Program is designed to increase the access and equity in the pipeline for graduate degrees in statistics, data science, and mathematics. The David H. Blackwell Summer Scholars Program will offer research experiences during the summer to 8-12 students. Students from historically marginalized minority populations in the United States, which includes African Americans, Hispanic/Latinos, American Indian/Alaska Natives, are especially encouraged to apply. The goal of the project is advance the scholars' knowledge of statistics and mathematics to prepare them for success in statistics or mathematics graduate programs. The Department of Statistics and the Department of Mathematics at the University of Illinois are well poised and dedicated to fulfill programmatic efforts that are exemplary of Professor Blackwell's name.
      Supervisors: Jeff Douglas & James Balamuta

  • Stat 100: Introduction to Statistics
    • [Spring 2021], [Fall 2020], [Summer 2020], [Spring 2020], [Fall 2019], [Spring 2019]
      Course Description: First course in probability and statistics at a precalculus level; emphasizes basic concepts, including descriptive statistics, elementary probability, estimation, and hypothesis testing in both nonparametric and normal models.
      Instructors: Karle Flanagan (S21, F20, Su20, S20, F19, S19), Ha Khanh Nguyen (Su20)

  • Stat 400: Statistics & Probability I
    • [Spring 2020], [Spring 2019]
      Course Description: Introduction to mathematical statistics that develops probability as needed; includes the calculus of probability, random variables, expectation, distribution functions, central limit theorem, point estimation, confidence intervals, and hypothesis testing. Offers a basic one-term introduction to statistics and also prepares students for STAT 410.
      Instructors: Albert Yu (S20), Shiwei Lan (S19)

  • Stat 410: Statistics & Probability II
    • [Spring 2020]
      Course Description: Continuation of STAT 400. Includes moment-generating functions, transformations of random variables, normal sampling theory, sufficiency, best estimators, maximum likelihood estimators, confidence intervals, most powerful tests, unbiased tests, and chi-square tests.
      Instructor: Alex Stepanov

  • Stat 420: Methods of Applied Statistics
    • [Fall 2019], [Summer 2019]
      Course Description: Systematic, calculus-based coverage of the more widely used methods of applied statistics, including simple and multiple regression, correlation, analysis of variance and covariance, multiple comparisons, goodness of fit tests, contingency tables, nonparametric procedures, and power of tests; emphasizes when and why various tests are appropriate and how they are used.
      Instructors: Alex Stepanov (F19), David Unger (Su19)

  • Stat 432: Basics of Statistical Learning
    • [Fall 2020]
      Course Description: Topics in supervised and unsupervised learning are covered, including logistic regression, support vector machines, classification trees and nonparametric regression. Model building and feature selection are discussed for these techniques, with a focus on regularization methods, such as lasso and ridge regression, as well as methods for model selection and assessment using cross validation. Cluster analysis and principal components analysis are introduced as examples of unsupervised learning.
      Instructor: David Dalpiaz

Additional experience can be found on my LinkedIn page.

Projects & Publications

Awards & Accomplishments

  • LAS Impact Award (STAT 100 Course Assistant Team), LAS College, University of Illinois at Urbana-Champaign (April 2021)
    • Recognized for inspiring efforts during COVID-19
  • Eagle Scout Award, Boy Scouts of America (Feb 2014)
  • Second Degree Black Belt, American Taekwondo Association (July 2012)
  • First Degree Black Belt, American Taekwondo Association (June 2011)