Jungeum Kim

Jungeum Kim

Principal Postdoctoral Researcher

The University of Chicago Booth School of Business

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 recently moved my website to https://jungeumkim.com (Please visit).

Interests
  • Artificial Intelligence for Bayesian Statistics
  • Robust and Principled Deep Learning for Science
  • High-dimensional data analysis
  • Manifold Learning and Data Visualization
Education

  • PhD in Statistics, 2022

    Purdue University

  • MAc in Statistics, 2017

    Seould National University

  • BSc in Statistics, 2015

    Seould National University

  • BSc in Social Welfare, 2015

    Seould National University

Research

Jungeum Kim, Veronika Rockova (2023). Deep Bayes Factors. https://arxiv.org/abs/2312.05411.

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Jungeum Kim, Veronika Rockova (2023). On Mixing Rates for Bayesian CART. https://arxiv.org/abs/2306.00126.

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Jungeum Kim, Xiao Wang (2023). Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification. The Annals of Applied Statistics, Vol 17 (2).

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Jungeum Kim (2022). Bridging the gap between human and computer vision in machine learning, adversarial and manifold learning for high-dimensional data. Purdue University Graduate School PhD Thesis.

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Jungeum Kim (2017). A Unified Framework of Robust PCA - Use of Robust Unit Approach. Seoul National University Graduate School MAc Thesis.

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