Kyurae Kim
I am a second year PhD student advised by Professor Jacob R. Gardner at the University of Pennsylvania working on Bayesian inference, stochastic optimization, Markov chain Monte Carlo sampling, and Bayesian optimization. I work closely with Professor Yi-An Ma and Alain Oliviero Durmus.
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.
- AdvancedVI.jl: Variational inference algorithms in Julia.
- SliceSampling.jl: Slice sampling MCMC algorithms in Julia.
- MCMCTesting.jl: Correctness tests for MCMC algorithms in Julia.
productivity tools that I use
(Last updated in 14 April 2024)
I heavily use cross-platform opensource software tools.
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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 Foxit Reader 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
Sep 25, 2024 | 1 paper on BayesOpt has been accepted to NeurIPS’24 as spotlight |
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Aug 26, 2024 | I will be in San Francisco from August to November. |
Jul 24, 2024 | I received a best reviewer award from ICML’24. |
May 7, 2024 | I will do an internship with the Prescient Design team at Genentech this Fall. |
May 1, 2024 | 2 papers have been accepted to ICML’24: 1 on the convergence BBVI and 1 on stochastic gradient descent. |
selected publications
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Demystifying SGD with Doubly Stochastic GradientsIn Proceedings of the International Conference on Machine Learning (ICML) (to be presented) Jul 2024
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Provably Scalable Black-Box Variational Inference with Structured Variational FamiliesIn Proceedings of the International Conference on Machine Learning (ICML) (to be presented) Jul 2024
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Stochastic Approximation with Biased MCMC for Expectation-MaximizationIn Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AISTATS) May 2024
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Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?In Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AISTATS) May 2024
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On the Convergence of Black-Box Variational InferenceIn Advances in Neural Information Processing Systems (to be presented) Dec 2023
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The Behavior and Convergence of Local Bayesian OptimizationIn Advances in Neural Information Processing Systems (to be presented) Dec 2023
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Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian InferenceIn Proceedings of the International Conference on Machine Learning (ICML) Jul 2023