Yejin Jaylynn Kim
Assistant Professor
School of Biomedical Informatics
University of Texas Health Science Center at Houston
7000 Fannin, Suite 600
Houston, Texas 77030
Yejin.Kim@uth.tmc.edu
Yejin Kim is an Assistant Professor in the School of Biomedical Informatics at the University of Texas Health Science Center at Houston. She received her Ph.D. in Computer Science from POSTECH, South Korea, where she focused on machine learning for healthcare. Her current research interests include counterfactual machine learning and human-centered machine learning for healthcare. She has also been recognized for her outstanding contributions to research and received several awards including HD4A award from Robert Woods Johnson Foundation and being selected as the top 10% most cited PLoS One paper in 2016. Her work has been covered in public media outlets such as CNN, NPR, and ScienceDaily. She has served on various grant review panels and committees, including the UK Research and Innovation, Luxembourg National Science Foundation.
I am looking for motivated Ph.D students and postdocs. Send me an email with your CV!
Research Interests
Novel machine learning model development
- Heterogeneous treatment effect estimation
- Computational phenotyping with longitudinal data
- Sequential decision making models
- Knowledge graph representation learning
Application area
- Alzheimer’s disease
- Stroke
- Drug repurposing
News
- May 2023, I give an invited talk at the Web Conference 2023 Health Day
- March 2023, I give an invited talk at BERD Core at UT Health McGovern Medical School, Data Science Core at SBMI
- March 2023, Honored to serve as a grant review panel for United Kingdom Research and Innovation
- November 2022, Honored to serve as a grant review panel for AI in Healthcare from Fonds de recherche du Québec, Ministry of Innovation, Science & Technology - State of Israel, Bilateral Research Cooperation Program MOST-FRQS
- November 2022, I give a talk at AI in Health Conference, organized by Rice Ken Kenedy Institute. Thanks for the invitation Dr. Akane Sano.
- September 2022, Our team’s vaccine and Alzheimer’s study was invited to testify before U.S. Department of Health & Human Services
- January 2021, Top 10% most cited PLoS One article in 2016 [paper]
- July 2020 Selected as AAIC 2020 highlighted poster
- July 2020, Our team’s vaccine and Alzheimer’s study was covered in major news media NPR, CNN, and ScienceDaily.
- April 2020, Our team ranked 2nd place in the International DREAM Challenge for Drug Repurposing Competition
- Janurary 2020, Honored to serve as a grant review panel for Luxembourg National Science Foundation.
- November 2020, I give a talk at AI in Health Conference, organized by Rice Ken Kenedy Institute.
- September 2019, Thrilled to initiate the SBMI Datathon Series as an organizer
- September 2019, Awarded Robert Woods Johnson Foundation Health Data 4 Action grant as PI
Service
- Reviewer, Nature Communication, JAMA Open Network, Cell Reports, Nature Scientific Reports, J. Biomedical Informatics, J. Am. Medical Informatics, Knowledge and Information System, Briefing in Bioinformatics, Transaction in Intelligent Systems and Technology
- Program Committee Member, AAAI AI for Social Impact, IJCAI, 2018-, AAAI, 2018-,ACL Clinical NLP workshop, BIBM IEEE International Conference on Bioinformatics & Biomedicine,AMIA Knowledge Discovery and Data Mining Working Group,IEEE International Conference on Healthcare Informatics, 2020-,WSDM Health Day, KDD Applied Data Science for Healthcare,IEEE BIBM International Conference on Bioinformatics and Biomedicine,World Wide Web Conference, 2018, AMIA Annual Symposium, 2017-, AMIA Informatics Summit, 2019-, ACM-BCB Conference on Bioinformatics, Computational Biology, and Health Informatics
- Co-organizer, SBMI Machine Learning Hackathon 2019-
Selected publications
Full publication list is available at google scholar
- Yan Ding, Xiaoqian Jiang, and Yejin Kim, Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules, Bioinformatics. 2022 [paper]
- Bukhbinder, Avram S., Ling, Yaobin, Hasan, Omar, Jiang, Xiaoqian, Kim, Yejin, Phelps, Kamal N., Schmandt, Rosemarie E., Amran, Albert, Coburn, Ryan, Ramesh, Srivathsan, Xiao, Qian, Schulz, Paul E, Risk of Alzheimer’s Disease Following Influenza Vaccination: A Claims-Based Cohort Study Using Propensity Score Matching. Journal of Alzheimer’s Disease, 1 Jan. 2022 : 1 – 14. [paper]
- Yejin Kim, Kai Zhang, Sean I. Savitz, Luyao Chen, Paul E. Schulz, and Xiaoqian Jiang. Counterfactual analysis of differential comorbidity risk factors in Alzheimer’s disease and related dementias. PLoS Digital Health. 2022 [paper]
- Kang-Lin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, and Yejin Kim. Drug Repurposing for COVID-19 using Graph Neural Network and Harmonizing Multiple Evidence. Nature Scientific Reports. 2021. [paper]
- Ziyi Li, Xiaoqian Jiang, Yizhou Wang, and Yejin Kim. Applied machine learning in Alzheimer’s disease research: omics, imaging, and clinical data. Emerging Topic in Life Science. 2021. [paper]
- Yejin Kim, Jessika Suescun, Mya Schiess, and Xiaoqian Jiang. Computational medication regimen for Parkinson’s disease using reinforcement learning. Nature Scientific Reports. 2021. [paper]
- Yejin Kim, Xiaoqian Jiang, Samden D Lhatoo, Guo-Qiang Zhang, Shiqiang Tao, Licong Cui, Xiaojin Li, Robert D Jolly 3rd, Luyao Chen, Michael Phan, Cung Ha, Marijane Detranaltes, and Jiajie Zhang. A community effort for automatic detection of postictal generalized EEG suppressioin in epilepsy. BMC Medical Informatics and Decision Making. 2020. [paper]
- Yejin Kim, Shuyu Zheng, Jing Tang, W. Jim Zheng, Zhao Li, and Xiaoqian Jiang. Anticancer drug synergy prediction in understudied tissues using transfer learning. Journal of American Medical Informatics. 2020. [paper]
- Yejin Kim, Samden D Lhatoo, GQ Zhang, Luyao Chen, and Xiaoqian Jiang. Temporal Phenotyping for Transitional Disease Progress: an Application to Epilepsy and Alzheimer’s Disease. Journal of Biomedical Informatics. 2020 [paper]
- Yejin Kim, Xiaoqian Jiang, Luca Giancardo, Danilo Pena, Avram S. Bukhbinder, Albert Y. Amran, and Paul E. Schulz. Multimodal Phenotyping of Alzheimer’s Disease with Longitudinal Magnetic Resonance Imaging and Cognitive Function Data. Scientific Reports, 2020 [paper]
- Yejin Kim, Xiaoqian Jiang, Luyao Chen, Xiaojin Li, and Licong Cui. Discriminative Sleep Patterns of Alzheimer’s Disease via Tensor Factorization. AMIA Annual Symposium. 2019. [paper]
- Yejin Kim, Kwangseob Kim, Chanyoung Park, and Hwanjo Yu. Sequential and Diverse Recommendation with Long Tail. IJCAI 2019. [paper] [slides]
- Yejin Kim, Luca Giancardo, Danilo Pena, and Xiaoqian Jiang. Finding Discriminative Subgroups of Brain Regions using Tensor Factorization. ACM KDD workshop on Applied Data Science for Healthcare. 2019. [paper]
- Jingyun Choi, Yejin Kim, HS Kim, In Young Choi, and Hwanjo Yu, Phenotyping of Korean patients with better-than-expected efficacy of moderate-intensity statins using tensor factorization, PLoS One. 2018. [paper]
- Yejin Kim, Jimeng Sun, Hwanjo Yu, and Xiaoqian Jiang. Federated Tensor Factorization for Computational Phenotyping. KDD 17 [paper] [slides] [video]
- Yejin Kim, Jingyun Choi , Yosep Chong, Xiaoqian Jiang, and Hwanjo Yu. DiagTree: Diagnostic Tree for Differential Diagnosis. CIKM 17 [paper] [slides]
- Yejin Kim, Robert El-Kareh, Jimeng Sun, Hwanjo Yu, and Xiaoqian Jiang. Discriminative and distinct phenotype by constrained tensor factorization. Scientific Reports 2017. [paper] [slides]
- Jingyun Choi, Mi Jung Rho, Yejin Kim, In Hye Yook, Hwanjo Yu, Dai-Jin Kim, and In Young Choi, Smartphone Over-dependence Classification using Tensor Factorization. PLoS One 2017. [paper]
- Jingyun Choi, Yejin Kim, HS Kim, In Young Choi, and Hwanjo Yu, Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins. ACM BCB 17 [paper]
- Yejin Kim, YongHyun Park, JY Lee, In Young Choi, and Hwanjo Yu. Discovery of prostate specic antigen pattern to predict castration resistant prostate cancer of androgen deprivation therapy. BMC Decision Making and Medical Informatics 2016 [paper] [slides]
- YongHyun Park, Yejin Kim, Hwanjo Yu, In Young Choi, Byun SS, Kwak C, Chung BH, Lee HM, Kim CS, and Lee JY. Is lymphovascular invasion a powerful predictor for biochemical recurrence in pT3 N0 prostate cancer? Results from the K-CaP database. Scientific reports. 2016 [paper]
- Yejin Kim, JE Jeong, H Cho , DJ Jung, M Kwak, MJ Rho, Hwanjo Yu, Dai-Jin Kim, In Young Choi. Personality factors predicting smartphone addiction predisposition: behavioral inhibition and activation systems, impulsivity, and self-control. PLoS One. 2016 [paper]
Selected presentation
- “Prediction of Individual Treatment Effects of Rehabiliation for Post-stroke Patients”, World Stroke Congress, 2021.
- “Population-based Drug Repurposing to Treat Thrombosis in COVID-19 Patients”. ISC 2021 ePoster
- “Literature Mining Approach to Extract Drug Effectiveness in Clinical Trials. An Application to Alzheimer’s Disease” AMIA Informatics Summit, 2021
- “Causal Pathway to Analyze Racial Disparities in Alzheimer’s Disease and Related Dementia”, Alzheimer’s Association International Conference (AAIC), 2020 video
- “Influenza Vaccination is associated with a reduced incidence of Alzheimer’s Disease”, AAIC, 2020
- “Temporal Phenotyping for Transitional Disease Progress: an Application to Epilepsy and Alzheimer’s Disease”, WSDM Workshop Healthcare Day 2020, Houston, Texas
- “Discriminative Sleep Patterns of Alzheimer’s Disease via Tensor Factorization”, AMIA 2019, Washington D.C.
- “Optimized Medication Regimen for Parkinson’s Disease”, Rice Data Science Conference October 15, 2019, Houston, Texas
- “Sequential and Diverse Recommendation with Long Tail”, International Joint Conference on Artificial Intelligence August 14, 2019, Macau, China
- “Finding Discriminative Subgroups of Brain Regions using Tensor Factorization”, ACM KDD workshop on Applied Data Science for Healthcare. August 4, 2019, Anchorage, Alaska
- “Discovering Underlying Concepts with Tensor Factorization”, Center for Health Security and Phenotyping, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Jan 18, 2019, Houston, Texas
- “Interpretable and Federated Tensor Factorization for Computational Phenotyping”, Department of Computer Engineering, Hong Kong Baptist University, June 20 2018, Hong Kong
- “Tensor Factorization for Computational Phenotyping”, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Nov 28 2018, Houston, Texas
- “Data Science and Applications”, Department of Management Engineering, Ulsan National Institute of Science and Technology, May 29 2018, Ulsan, Korea
- “Data Science and Applications”, Korea University WISET, Sep 12 2018, Seoul, Korea
- “Tensor Factorization for Computational Phenotyping”, KCC 2017 Spotlight Session for Young Women Scholars Jun 21 2017, Jeju, Korea
- “Tutorial: Matrix/Tensor factorization and its applications”, Department of Medical Informatics, Catholic University of Korea, Nov 20 2017, Seoul, Korea
- “DiagTree: Diagnostic Tree for Differential Diagnosis”, The Korea Society of Medical Informatics, Nov 17 2017, Seoul, Korea
- “DiagTree: Diagnostic Tree for Differential Diagnosis”, International Conference on Information and Management, Nov 12 2017, Singapore
- “Federated Tensor Factorization for Computational Phenotyping (poster)”. The 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 14, 2017, Halifax, Nova Scotia, Canada
- “Discovery of prostate specific antigen pattern to predict castration resistant prostate cancer of androgen deprivation therapy”, ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics in conjunction with CIKM, Oct 23, 2015, Melbourne, Australia