Prof. Uri Obolski

School of Public Health
Department of Environmental Studies
ביה"ס לבריאות הציבור סגל אקדמי בכיר
Prof. Uri Obolski
Office: Sackler School of Medicine, 914


  • Antibiotic resistance
  • Effects of climate on mosquito-borne diseases
  • Mathematical modelling of infectious diseases
  • Machine learning on medical health records
  • Causality in biomedical data


  • B.Sc Mathematics, Tel Aviv University, 2011
  • M.Sc Computational Biology, Tel Aviv University, 2012
  • PhD Computational Biology, Tel Aviv University, 2016

Academic appointments

  • Associate Professor, Porter School of the Environment and Earth Sciences, School of Public Health, Tel Aviv University - 2024
  • Senior Lecturer, Porter School of the Environment and Earth Sciences, School of Public Health, Tel Aviv University - 2019-2024
  • Postdoctoral research fellow, University of Oxford, 2016-2019

Selected publications

Google Scholar profile

  • Stensrud, M. J., Nevo, D., & Obolski, U. (2024). Distinguishing immunologic and behavioral effects of vaccination. Epidemiology, 10-1097.
  • Obolski, U., Swarthout, T. D., Kalizang’oma, A., Mwalukomo, T. S., Chan, J. M., Weight, C. M., ... & Heyderman, R. S. (2023). The metabolic, virulence and antimicrobial resistance profiles of colonising Streptococcus pneumoniae shift after PCV13 introduction in urban Malawi. Nature communications14(1), 7477.
  • Baraz, A., Chowers, M., Nevo, D., & Obolski, U. (2023). The time-varying association between previous antibiotic use and antibiotic resistance. Clinical Microbiology and Infection29(3), 390-e1.
  • Chowers, M., Zehavi, T., Gottesman, B. S., Baraz, A., Nevo, D., & Obolski, U. (2022). Estimating the impact of cefuroxime versus cefazolin and amoxicillin/clavulanate use on future collateral resistance: a retrospective comparison. Journal of Antimicrobial Chemotherapy77(7), 1992-1995.
  • Lourenço, J., Barros, S. C., Zé-Zé, L., Damineli, D. S., Giovanetti, M., Osório, H. C., ... & Obolski, U. (2022). West Nile virus transmission potential in Portugal. Communications biology5(1), 6.
  • Lewin-Epstein, O., Baruch, S., Hadany, L., Stein, G. Y., & Obolski, U. (2021). Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records. Clinical Infectious Diseases72(11), e848-e855.
  • Diamant, M., Baruch, S., Kassem, E., Muhsen, K., Samet, D., Leshno, M., & Obolski, U. (2021). A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse. Nature Communications12(1), 1148.
  • Cherny, S. S., Nevo, D., Baraz, A., Baruch, S., Lewin-Epstein, O., Stein, G. Y., & Obolski, U. (2021). Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling. Journal of Antimicrobial Chemotherapy76(1), 239-248.
  • Lourenço, J., Thompson, R. N., Thézé, J., & Obolski, U. (2020). Characterising West Nile virus epidemiology in Israel using a transmission suitability index. Eurosurveillance25(46), 1900629.
  • Obolski, U., Perez, P. N., Villabona‐Arenas, C. J., Thézé, J., Faria, N. R., & Lourenço, J. (2019). MVSE: An R‐package that estimates a climate‐driven mosquito‐borne viral suitability index. Methods in ecology and evolution10(8), 1357-1370.


  • Best research project in the Clalit Health Services ROMACH initiative - 2023
  • Rector's excellence in teaching award, Faculty of Medicine - 2020-2021
  • Excellence in teaching award , School of Public Health - 2019-2020
  • EMBO fellowship for postdoctoral research - 2016
  • Wolf Prize for excellent PhD Students - 2014

Active grants

  • ISF personal research grant – Understanding the dynamics of antibiotic resistance: towards data-based empiric treatment
  • Safra and Ichilov Clinical Bioinformatics Research Grant – “Identifying effective antibiotic treatments through CRP dynamics”
  • TAU Data Science Center Grant – Mathematical foundations of xAI and their applications to personalized medicine and public health
  • TAU Data Science Center , Safra and Shaarei Zedek collaborative Grant – Identifying Trajectories of the Six-Minutes Walk Test in Heart Failure Prognostication: A Machine Learning Approach
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