Michal Rosen-Zvi

Michal Rosen-Zvi (Hebrew: מיכל רוזן-צבי) is an Israeli academic and researcher in the fields of artificial intelligence and deep learning and their applications to healthcare. She is research director for Healthcare Informatics at IBM Research in Haifa.[1]

Michal Rosen-Zvi
NationalityIsraeli
Alma materHebrew University, University of California, Berkeley, University of California Irvine
Occupation(s)researcher, professor

Education and career
External video
video icon Dr. Michal Rosen-Zvi, Addressing Special Health Needs Via Personalized Systems that Learn from Data, HIT[2]

After completing a PhD in computational physics at Bar-Ilan University, Rosen-Zvi pursued postdoctoral studies in machine learning at UC Berkeley, UC Irvine, and Hebrew University.[3][4]

She has worked for IBM since 2005, and is director of healthcare informatics at IBM Research in Haifa.[3][5] She has specialised in the fields of medical image analysis, machine learning, and cognitive computing, including creation of a structured dataset on governmental responses to the 2020 COVID-19 pandemic based on Wikipedia coverage.[6] She is a member of the Israeli National Council of Digital Health and Innovation.[4]

She has also taught at a number of colleges, including the Coller School of Management at Tel Aviv University[3] and the Faculty of Medicine at Hebrew University.[4]

Research

Rosen-Zvi is known for her work in document classification, where she introduced the author–topic model.[7] This is an extension of latent Dirichlet allocation, a method for understanding the word frequencies of documents by fitting them to a model in which a document is associated with a mixture of topics, each of which is a probability distribution over words. The author-topic model was introduced in a paper by Rosen-Zvi and three other authors in 2004. It adds the identity of document authors and their preferences for topics to the model, and became "one of the earliest attempts at modeling the interests of authors" used in this area of machine learning.[8]

More recent research of Rosen-Zvi has focused on the application of machine learning techniques to problems in healthcare including medical diagnosis, choice of treatment, and causal inference in the observation of medical treatments and their outcomes.

References
  1. "Michal Rosen-Zvi, PhD". ISPOR | International Society For Pharmacoeconomics and Outcomes Research. Retrieved 28 April 2021.
  2. "Dr. Michal Rosen-Zvi, Addressing Special Health Needs Via Personalized Systems that Learn from Data". Holon Institute of Technology. 5 April 2017. Retrieved 6 September 2020.
  3. "Healthcare Technological Innovation Program: Michal Rosen-Zvi". Lahav Executive Education, Coller School of Management, Tel Aviv University. Retrieved 6 September 2020.
  4. "Monthly Colloquium: AI for Healthcare". Data Science Research Center, University of Haifa. 28 October 2019. Retrieved 7 September 2020.
  5. Mitnick, Joshua (20 August 2019). "Stakes High In Moving Charedi Women Into Tech". The Jewish Week. Retrieved 6 September 2020.
  6. Spiro, James (6 September 2020). "Wikipedia is the perfect Covid-19 datasource, says IBM research director for healthcare informatics". CTech. Retrieved 6 September 2020.
  7. Rosen-Zvi, Michal; Griffiths, Thomas; Steyvers, Mark; Smyth, Padhraic (2012). "The Author-Topic Model for Authors and Documents". arXiv:1207.4169 [cs.IR].
  8. Kataria, Saurabh; Mitra, Prasenjit; Caragea, Cornelia; Giles, C. Lee (2011). "Context Sensitive Topic Models for Author Influence in Document Networks". In Walsh, Toby (ed.). IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011. {IJCAI/AAAI}. pp. 2274–2280. doi:10.5591/978-1-57735-516-8/IJCAI11-379.
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