Education
University of Illinois at UrbanaChampaign

Master of Science – MS, Statistics – Analytics
2021–2023
Relevant Coursework: *Statistical Data Management, *Mathematical Statistics, *Multivariate Analysis
University of Illinois at UrbanaChampaign

Bachelor of Science – BS, Statistics, Minor in Mathematics
2018–2021
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
20152018
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 UrbanaChampaign
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
Courses:
 Stat 107: Data Science Discovery

[Fall 2021]
Course Description: Data Science Discovery is the intersection of statistics, computation, and realworld relevance. As a projectdriven course, students perform handsonanalysis of realworld 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 FagenUlmschneider

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 fiveweek 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 UrbanaChampaign
Jan 2019 – May 2021
 Supported student learning objectives through personalized and small group assistance in inperson 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.
Courses:
 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 812 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 oneterm 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 momentgenerating functions, transformations of random variables, normal sampling theory, sufficiency, best estimators, maximum likelihood estimators, confidence intervals, most powerful tests, unbiased tests, and chisquare tests.
Instructor: Alex Stepanov

 Stat 420: Methods of Applied Statistics

[Fall 2019], [Summer 2019]
Course Description: Systematic, calculusbased 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
 Discovering Data Science Course Website (May 2021 – Aug 2021)
 Movies on Netflix, Prime Video, Hulu and Disney+ Cluster Data Analysis (Jan 2021 – May 2021)
 Gas Turbine Carbon Monoxide Emissions Data Analysis (Jan 2021 – May 2021)
 Visual Statistics Shiny App (Feb 2021 – Current)
 UIUC STAT 100 Course Website (Aug 2019 – Current)
 Walmart Sales During Stormy Weather Data Analysis (Fall 2019)
 Reger, W. & Kilian K. (2013). Using peptide surfaces to study the reprogramming of malignant melanoma cells towards identification of a cancer stem cell state. American Cancer Society Summer High School Research Program, 251263
Awards & Accomplishments
 LAS Impact Award (STAT 100 Course Assistant Team), LAS College, University of Illinois at UrbanaChampaign (April 2021)
 Recognized for inspiring efforts during COVID19
 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)