Skip to Content
SBMI logo

Yejin Kim, PhD

Assistant Professor

Yejin Kim, Ph.D joined the UTHealth School of Biomedical Informatics (SBMI) on November 2018 as an assistant professor and a founding member of the Center for Secure Artificial intelligence For hEalthcare (SAFE).

Dr. Kim’s primary research interests are on developments of innovative machine learning models for healthcare problems and its practical applications to solve real-world problems in biomedicine. She is the co-organizer of SBMI Machine Learning Hackathon and has served as program committee of various data science conferences such as AAAI, AMIA, WWW, KDD workshop, ACM-BCB, IEEE-BIBM. She is a member of the American Medical Informatics Association. She has published papers in prestigious computer science conferences (such as KDD, CIKM, and IJCAI) and high-impact journals (such as Scientific reports and PLoS one).

As an enthusiastic data scientist who is interested in biomedical informatics, Dr. Kim is specialized in exploring new challenging problems in medicine, deriving actionable models, and transferring the knowledge to the domain. Dr. Kim has an in-depth background in machine learning with specific training and expertise in matrix/tensor factorization and Markov decision process. She has been developing innovative algorithms for computational phenotyping and sequential decision-making models for healthcare practice. She is working on multidisciplinary projects as collaborating with various domain experts from neurology, psychiatry, pathology, urology, emergency, dentistry.

“I think now we need some data scientists who make data science actionable in medicine. I want to be that person.”


Contact

Yejin.Kim@uth.tmc.edu
Phone: 713-500-3998
Fax: 713-500-3929

Staff Support

Angela M. Wilkes
Phone: 713-500-3765


Education

  • PhD, Computer Science, 2017, Pohang University of Science and Technology
  • BS, Industrial Engineering, 2012, Pohang University of Science and Technology

Areas of Expertise

  • Data Mining
  • Machine Learning
  • Computational Phenotyping