Kyurae Kim

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I am a second year PhD student advised by Professor Jacob R. Gardner at the University of Pennsylvania working on Bayesian machine learning, Bayesian inference, and Bayesian optimization. I acquired my Bachelor in Engineering degree at Sogang University, South Korea, during which I did undergraduate research under Professor Hongseok Kim, Tai-kyong Song, Sungyong Park, and Youngjae Kim. During this time, I also worked at Samsung Medical Center, South Korea, as an undergraduate researcher, at Kangbuk Samsung Hospital, South Korea, as a visiting researcher, and at Hansono, South Korea, as a part-time embedded software engineer. After graduating, I was a research associate at the University of Liverpool under Professor Simon Maskell and Jason F. Ralph. I hold memberships in the ACM, ISBA, and the IEEE (which implies that I’m a good tipper…)

Previously, I used to work on medical imaging, computer systems, high-performance computing, and array signal processing, but I’m also broadly interested in topics such as computational statistics, programming languages, and optimization. Here is a list of papers that I found interesting over my career.

Software

I am active within Julia’s computational statistics community as part of the Turing language team.

productivity tools that I use

(Last updated in 31 August 2023)
I heavily use cross-platform opensource software tools.

  • Emacs (heavily customized) for writing code
    • Magit for accessing Git within Emacs
  • Zotero for managing citations and exporting bibtex
  • Inkscape for drawing vector diagrams (but also Tikz if not in a rush)
  • Veusz for quick publication quality plots (but also PGFplots, Makie.jl for fancier stuff).
  • Evince for viewing and Master PDF for editing, and annotating PDF files.
  • Nomacs for viewing a lot of image files quickly (on Windows, FastStone is hard to beat)
  • Flameshot for taking screenshots

news

Jan 19, 2024 2 papers have been accepted to AISTATS’24: 1 on the convergence BBVI and 1 on the convergence of the stochastic approximation EM algorithm.
Nov 21, 2023 I have been selected as top reviewer at NeurIPS’23.
Oct 21, 2023 2 papers have been accepted to NeurIPS’23: 1 on the convergence BBVI and 1 on local BayesOpt (spotlight).
Apr 24, 2023 1 paper on BBVI has been accepted to ICML’23 for live oral presentation
Sep 14, 2022 1 paper on inclusive KL minimization has been accepted to NeurIPS’22.

selected publications

  1. Stochastic Approximation with Biased MCMC for Expectation-Maximization
    Samuel Gruffaz,  Kyurae Kim, Alain Durmus, and Jacob R Gardner.
    In Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AISTATS) (to be presented) May 2024
  2. Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
    Kyurae Kim, Yi-An Ma, and Jacob R. Gardner.
    In Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AISTATS) (to be presented) May 2024
  3. On the Convergence of Black-Box Variational Inference
    Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, and Jacob R. Gardner.
    In Advances in Neural Information Processing Systems (to be presented) Dec 2023
  4. The Behavior and Convergence of Local Bayesian Optimization
    Kaiwen Wu,  Kyurae Kim, Roman Garnett, and Jacob R. Gardner.
    In Advances in Neural Information Processing Systems (to be presented) Dec 2023
  5. Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
    Kyurae Kim, Kaiwen Wu, Jisu Oh, and Jacob R. Gardner.
    In Proceedings of the International Conference on Machine Learning (ICML) Jul 2023
  6. Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
    Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, and Hongseok Kim.
    In Advances in Neural Information Processing Systems Jul 2022
  7. A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization
    Kyurae Kim, Youngjae Kim, and Sungyong Park.
    IEEE Transactions on Parallel and Distributed Systems Jul 2020