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 research interest lies in developing methodological approaches for personalized medicine using single-cell RNA-seq data. I am also broadly interested in utilizing reinforcement learning for biomedical applications.

Publications

Policy Learning with Continuous Actions Under Unmeasured Confounding
Yuhan Li, Eugene Han, Yifan Hu, Wenzhuo Zhou, Zhengling Qi, Yifan Cui, and Ruoqing Zhu
Submitted October 2024
Nasomucosal and systemic viral shedding-correlated responses following influenza A/H1N1 challenge in people with complex preexisting immunity
Kathie-Anne Walters, Charles A. Blatti III, Ruoqing Zhu, Barbara Banbury, Luca T. Giurgea, Rachel Bean, Eugene Han, Yuhan Li, Kelsey Scherler, Jenna Sherry, Sarah Formentini, Wenzhuo Zhou, Adriana Cervantes-Medina, Monica Gouzoulis, Luz Angela Rosas, Alison Han, Lisa Gatzke, Colleen Bushell, Ned Sherry, Jeffery K. Taubenberger, Matthew J. Memoli, and John C. Kash
Submitted March 2024 (In Revision)
Linear \(d\)-polychromatic \(Q_{d-1}\)-colorings of the Hypercube
Eugene Han and David Offner
Graphs and Combinatorics, 2018

Presentations

In Search of the Holy Grail of Relationship Success: Using Machine Learning Methods to Understand Adaptive Relationship Strategies
Yifan Hu, Yuhan Li, Eugene Han, Shitao Shi, Maya Carter, Ghada Kawas, Evaline Y. Li, Matthew Rivas-Koehl, Zixing Deng, Ruoqing Zhu, and Brian G. Ogolsky
International Association of Relationship Research, 2024
Modeling and Visualizing Compositional Data with the Fisher-Bingham distributions
Eugene Han and Ruoqing Zhu
Joint Statistical Meetings, 2023
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 for developing anomaly detection methods for acoustic signals and images in Python using TensorFlow. 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 400: Statistics and Probability I
Fall 2024
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