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Jungeum Kim

Machine Learning for every one

About me

I am a Principal Researcher at the University of Chicago Booth School of Business. I am working with Professor Veronika Rockova in the Econometrics and Statistics group. Currently, I am interested in bringing deep learning into Bayesian statistics with theoretical guarantees. I received my Ph.D. degree from Purdue University under the supervision of Dr. Xiao Wang, and I obtained my Master’s and Bachelor’s degrees from Seoul National University under the supervision of Dr. Hee-seok Oh. I will be on the job market in fall 2024.

Interests

  • Artificial Intelligence for Bayesian Statistics including inference
  • Robust and Principled Deep Learning for Science
  • High-dimensional data analysis
  • Manifold Learning and Data Visualization
  • Nonparametric Bayes

Education

  • PhD in Statistics, 2022, Purdue University
  • MSc in Statistics, 2017, Seoul National University
  • BSc in Statistics, 2015, Seoul National University
  • BSc in Social Welfare, 2015, Seoul National University

NEWS

Apr 17 2024 (NEW PAPER) Check out our draft on the generative tree bandits (with Sean and Veronika).

June 26 2024 (TALK) Invited to present at ISBA satellite 2024.

Dec 17 2023 (TALK) Invited to present at CMStatistics 2023.

Dec 06 2023 (NEW PAPER) Check out our draft of deep learning for Bayesian inference (with Veronika Rockova).

Oct 04 2023 (TALK) Presented at the Statistics Department Colloquium at the University of Wisconsin–Madison.

Aug 10 2023 (TALK) Invited to present in a BART section at JSM 2023.

Jul 19 2023 (TALK) Invited to present at the International Statistical Institute (ISI) 2023 conference.

May 6 2023 (NEW PAPER) Check out our draft on the mixing rate of Bayesian CART (with Veronika Rockova).

Mar 31 2023 (TALK) Presented at the Statistics Department Colloquium at Auburn University.

RESEARCH

Sean O’Hagan, Jungeum Kim and Veronika Rockova (2024)

Jungeum Kim and Veronika Rockova (2023)

Jungeum Kim and Veronika Rockova (2023)

Jungeum Kim and Xiao Wang (2023)

Manifold learning for visualization purposes by constructing and preserving locally adaptive global distances. Our algorithm shows a clear progression from global formation (with random initialization) to local details in a single optimization process!

Jungeum Kim and Xiao Wang (2022), AOAS

The Bayes classifier in fact can be the most robust classifier. Therefore, adversarial training for robust classification with deep neural networks should still aim to learn the Bayes classifier-!

AWARDS

2021 Virgil Anderson and Gloria Fischer Graduate Fellowship, Purdue

Teaching/Mentoring

Portfolio Project Mentor, AI4ALL Ignite Project, 2024

Graduate Teacher Certificate, by Center for Instructional Excellence, 2019 Purdue University 

Teaching evaluation examples : Example 1, Example 2, Example 3 (student comments in Q34)

Teaching observation (from an instructor): Evaluation sheet

Please download the winter break planning and reflection form, here.

SERVICE

DEI

– Portfolio Project Mentor, AI4ALL Ignite Project, 2024

– Diversity and Inclusion Committee, Department of Statistics, Purdue University, 2022

contact

Jungeum.Kim@chicagobooth.edu

5807 South Woodlawn Avenue, Chicago, IL 60637