Ross Kleiman

Ross Kleiman
Machine learning applications for healthcare data.

Years at WID

2015 - present


Ross Kleiman grew up in Westfield, N.J. After graduating Rutgers University, he moved to Madison, WI where he worked for Epic for two years prior to starting his Ph.D. at UW Madison in Computer Science. He will be graduating with his Ph.D. in May 2019 and is currently looking for his next position.


  • B.S., Biomedical Engineering,  Rutgers University
  • M.S., Computer Sciences, University of Wisconsin–Madison 
  • Ph.D., Computer Sciences, University of Wisconsin–Madison (x 2019)

Research Description

Ross Kleiman is broadly interested in opportunities to leverage machine learning algorithms with electronic health record (EHR) data to improve medicine. This manifests in several ways including prediction of patient health events, mining EHRs for new medical knowledge, and producing new algorithms that can adhere to the strict privacy standards necessary for medical research. He is particularly interested in large-scale computing methods that can learn from the whole of the EHR. Additionally, he enjoys working on theoretical machine learning and has recently focused on multi-class classification performance measures.


  • CIBM – Computation and Informatics in Biology and Medicine
  • Tau Beta Pi
  • AEMB – BME Honors Society
  • Rutgers SoE Slade Scholar


  • NLM Informatics Training Grant Recipient
  • Summa Cum Laude
  • Deans List
  • Rutgers Academic Excellence Award

Selected Publications

  • Kleiman, R., Kuusisto, F., Ross, I., Peissig, P., Stewart, R., Page, D., Weiss, J. (2019), “Machine Learning Assisted Discovery of Novel Predictive Lab Tests Using Electronic Health Record Data”, To Appear in the Proceedings of the AMIA 2019 Informatics Summit
  • Giacomelli, I., Jha, S., Kleiman, R., Page, D., Kyongwan, Y. Authors listed alphabetically (2019), “Privacy-Preserving Collaborative Prediction using Random Forests”, To Appear in the Proceedings of the AMIA 2019 Informatics Summit
  • Nicholas Sean Escanilla, Lisa Hellerstein, Ross Kleiman, Charles Kuang, James D. Shull, David Page (2018), “Recursive Feature Elimination by Sensitivity Testing”, To Appear in the Proceedings of the 17th IEEE International Conference on Machine Learning and Applications
  • Kleiman, R., LaRose, E., Badger, J., Page, D., Caldwell, M, Clay, J., Peissig, P. (2018), “Using Machine Learning Algorithms to Predict Risk for Development of Calciphylaxis in Patients with Chronic Kidney Disease”, Proceedings of the AMIA 2018 Informatics Summit
  • Wan T, Madabhushi A, Phinikaridou A, et al. Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model. Med Phys. 2014;41(4):042303.