About Me

I am currently a Statistics PhD student at the University of Illinois at Urbana-Champaign, where I also obtained my MS in Statistics, advised by Ruoqing Zhu. Prior to this, I received my BS in Mathematical Sciences from Carnegie Mellon University, where I worked with David Offner.

Research
My current primary research interest is in using machine learning methods (mainly reinforcement learning) for personalized medicine.
Modeling and Visualizing Compositional Data with the Fisher-Bingham distributions
Eugene Han and Ruoqing Zhu
Joint Statistical Meetings, 2013 (Oral Presentation)
Linear \(d\)-polychromatic \(Q_{d-1}\)-colorings of the Hypercube
Eugene Han and David Offner
Graphs and Combinatorics, 2018 `

Professional Experience
Intern Year Round - R&D Grad
Math & Analytics Graduate Intern
Sandia National Laboratories

At Sandia, I primarily worked on anomaly detection methods. One of the main projects I worked on was building failure forecasting models in R using Isolation Forests for lithium-ion batteries that were cycled to failure (defined as a certain percentage of the nominal capacity) under different fast-charging conditions. Other projects I worked on were primarily developing anomaly detection methods for acoustic signals and images in Python. I also provided an advisory role for other engineering summer interns on developing anomaly detection methods for acoustic signals from a statistical perspective.

Aug 2022 - Sep 2023
May 2022 - Aug 2022
(Remote) Albuquerque, NM
Data Analyst Intern
Locus Analytics

During my internship, I worked on two separate projects that made use of the firm's proprietary Functional Information System (FIS). In the first project, I developed classification models using algorithms typical for natural language processing in order to classify job postings to its appropriate FIS code. In the second project, I analyzed geographically proximate communities for patterns in functional distributions. This work was ultimately presented to the entire company and investors.

Jun 2018 - Aug 2018
New York, NY
Data Science Intern
Opticlose

Opticlose is a startup aiming to provide high volume sales teams the tools and technology to more effeciently make sales. I specifically analyzed sales data using a combination of Excel and R. I created models to predict the success of a sales closure at each step of the process. In addition, I created a company specific library of R scripts to better automate the process in the future. I also created and presented a slide deck consisting of my analytical results to investors.

Sep 2014 - Aug 2015
New York, NY

Teaching Experience
I have been a teaching assistant for the following courses:
University of Illinois at Urbana-Champaign, Department of Statistics
STAT 510: Mathematical Statistics
Fall 2021, Spring 2022
STAT 424: Statistical Modeling in R
Summer 2021
Carnegie Mellon University, Department of Computer Science
15-351/15-650/02-613: Algorithms & Advanced Data Structures
Fall 2018
15-122: Principles of Imperative Computation
Spring, Summer*, Fall 2017
* - semester spent as head TA