Dr. Jinsung Yoon

Google Cloud AI, USA

Email: jinsungyoon@google.com

2020 Ph.D., Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA

2016 M.Sc., Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA

2014 B.Sc., Department of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea

Publications (Selected)

1. J. Yoon, C. Davtyan and M. van der Schaar, “Discovery and Clinical Decision Support for Personalized Healthcare,” published in IEEE J. Biomedical and Health Informatics, June. 2015.
2. J. Yoon, W. R. Zame, and M. van der Schaar, "ToPs: Ensemble Learning with Trees of Predictors," published in IEEE Transactions on Signal Processing (TSP), 2018.
3. J. Yoon, W. R. Zame and M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks," IEEE Transactions on Biomedical Engineering, 2018.
4. J. Yoon, W. R. Zame, A. Banerjee, M. Cadeiras, A. Alaa and M. van der Schaar, "Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation," published in PloS One, 2018.
5. C. Tekin, J. Yoon and M. van der Schaar, "Adaptive Ensemble Learning with Confidence Bound,” published in IEEE J. Selected Topics in Signal Processing (JSTSP), Feb. 2016.
6. A. Alaa, J. Yoon, S. Hu and M. van der Schaar, "Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes," published in IEEE Transactions on Biomedical Engineering, 2017.
7. M. K. Ross, J. Yoon, M. van der Schaar, "Discovering Pediatric Asthma Phenotypes Based on Response to Controller Medication Using Machine Learning," published in Annals of American Thoracic Society, 2017.
8. E. Cenko, J. Yoon, R. Bugiardini, "Sex Differences in Outcomes after STEMI: Effect Modification by Treatment Strategy and Age," published in JAMA Internal Medicine, 2018.
9. C. Lee, J. Yoon, M. van der Schaar, "Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks based on Longitudinal Data," published in IEEE Transactions on Biomedical Engineering (TBME), 2019.
10. D. Jarrett, J. Yoon, M. van der Schaar, "Dynamic Prediction in Clinical Survival Analysis using Temporal Convolutional Networks," published in IEEE J. Biomedical and Health Informatics, 2019.
11. J. Yoon, L. N. Drumright, M. van der Schaar, "Anonymization through Data Synthesis using Generative Adversarial Networks (ADS-GAN)," published in IEEE J. Biomedical and Health Informatics, 2020.
12. J. Yoon, A. Alaa, S. Hu and M. van der Schaar, "ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission,” International Conference on Machine Learning (ICML), 2016.
13. J. Yoon, A. M. Alaa, M. Cadeiras, M. van der Schaar, "Personalized Donor-Recipient Matching for Organ Transplantation," Association for the Advancement of Artificial Intelligence (AAAI), 2017.
14. J. Yoon, J. Jordon, and M. van der Schaar, "GANITE: Estimation of individualized Treatment Effects using Generative Adversarial Nets," International Conference on Learning Representation (ICLR), 2018.
15. J. Yoon, W. R. Zame, and M. van der Schaar," Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," International Conference on Learning Representation (ICLR), 2018.
16. J. Yoon, J. Jordon, and M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," International Conference on Machine Learning (ICML), 2018.
17. J. Yoon, J. Jordon, and M. van der Schaar, "RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks," International Conference on Machine Learning (ICML), 2018.
18. C. Lee, W. R. Zame, J. Yoon, M. van der Schaar, "DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks," Association for the Advancement of Artificial Intelligence (AAAI), 2018.
19. J. Yoon, J. Jordon, and M. van der Schaar, "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.
20. J. Yoon, J. Jordon, and M. van der Schaar, "PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees," International Conference on Learning Representations (ICLR), 2019.
21. J. Jordon, J. Yoon, and M. van der Schaar, "KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks," International Conference on Learning Representations (ICLR), 2019. – Selected as oral presentation
22. J. Yoon, J. Jordon, and M. van der Schaar, "ASAC: Active Sensing using Actor-Critic model," Machine Learning for Healthcare (MLHC), 2019.
23. J. Jordon, J. Yoon, and M. van der Schaar, "Differentially Private Bagging: Improved Utility and Cheaper Privacy than Subsample-and-Aggregate," Neural Information Processing Systems (NeurIPS), 2019.
24. J. Yoon, D. Jarrett, and M. van der Schaar, "Time-series Generative Adversarial Networks," Neural Information Processing Systems (NeurIPS), 2019.
25. J. Yoon, S. O. Arik, and T. Pfister, "Data Valuation using Reinforcement Learning," International Conference on Machine Learning (ICML), 2020.
26. S. O. Arik, C. Li, J. Yoon, R. Sinha, A. Epshteyn, L. T. Le, V. Menon, S. Singh, L. Zhang, N. Yoder, M. Nikoltchev, Y. Sonthalia, H. Nakhost, E. Kanal, and T. Pfister, "Interpretable Sequence Learning for COVID-19 Forecasting," Neural Information Processing Systems (NeurIPS), 2020. - Selected as spotlight presentation
27. J. Yoon, Y. Zhang, J. Jordon, and M. van der Schaar, "VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain," Neural Information Processing Systems (NeurIPS), 2020.

Profile Details

https://scholar.google.com/citations?user=kiFd6A8AAAAJ