Jinsung Yoon
I am a research scientist at Google Cloud AI. I am currently working on diverse machine learning research topics such as generative models, self- and semi-supervised learning, model interpretation, data imputation, and synthetic data generation.
Previously, I worked on machine learning for medicine with Professor Mihaela van der Schaar as a graduate student researcher in UCLA Electrical and Computer Engineering Department. I received my Ph.D. and M.S. in Electrical and Computer Engineering Department at UCLA, and B.S. in Electrical and Computer Engineering at Seoul National University (SNU).
https://scholar.google.com/citations?user=kiFd6A8AAAAJ&hl=en&oi=ao
Authored Publications
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ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Somesh Jha
Transactions on Machine Learning Research (TMLR) (2024)
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs
Somesh Jha
Findings of the Association for Computational Linguistics: EMNLP (2023)
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Chun-Liang Li
Kihyuk Sohn
Transactions on Machine Learning Research (TMLR) (2023)
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
Chun-Liang Li
Kihyuk Sohn
Transactions on Machine Learning Research (TMLR) (2022)
Algorithmic fairness in pandemic forecasting: lessons from COVID-19
Thomas Tsai
Benjamin Jacobson
Nate Yoder
Dario Sava
Meg Mitchell
Garth Graham
npj Digital Medicine (2022)
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan
Joel Shor
Arkady Epshteyn
Ashwin Sura Ravi
Beth Luan
Chun-Liang Li
Daisuke Yoneoka
Dario Sava
Hiroaki Miyata
Hiroki Kayama
Isaac Jones
Joe Mckenna
Johan Euphrosine
Kris Popendorf
Nate Yoder
Shashank Singh
Shuhei Nomura
Thomas Tsai
npj Digital Medicine (2021)
