Reza Zadeh

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Reza Zadeh
NationalityCanada, Iran
CitizenshipCanada, Iran
Alma materStanford University (Ph.D.)
Carnegie Mellon University (M.Sc.)
University of Waterloo (B.S.)
Known forMachine Learning
Recommender Systems
Scientific career
FieldsComputer Science
InstitutionsStanford University
ThesisLarge Scale Graph Completion
Doctoral advisorGunnar Carlsson

Reza Zadeh is a Canadian-Iranian Computer Scientist working on Machine Learning. He is adjunct professor at Stanford University and CEO at Matroid.[1][2] He has served on the technical advisory board of Microsoft and Databricks.[3] His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics.[4][5][6]

As part of his research, he created the machine learning algorithm behind Twitter's Who-To-Follow project [7] and subsequently released it to Open Source.[8] During that time he also led research tracking earthquake damage via Machine Learning, gaining wide media attention.[9][10][11]

Reza helped create the MLlib library[12] and Linear Algebra Package[13] in Apache Spark. Through Open Source, Reza's work has been incorporated into industrial and academic cluster computing environments.[14] In addition to research, Reza designed and teaches two PhD-level classes at Stanford: Distributed Algorithms and Optimization (CME 323)[15] and Discrete Mathematics and Algorithms (CME 305).[16]

In Industry, to evaluate new ventures formed at the University of Toronto, Reza serves as a Chief Scientist of Machine Learning[17] at the Rotman School of Management.[18] His awards include a KDD Best Paper Award[19] and the Gene Golub Outstanding Thesis Award. He has no relation to Lotfi Zadeh.


  1. ^ "Institute for Computational and Mathematical Engineering Faculty". Stanford University. Archived from the original on May 14, 2016. Retrieved 14 May 2016.CS1 maint: Unfit url (link)
  2. ^ Martin, Scott (2017-03-27). "A Life's Ambition, Matroid Launches". Wall Street Journal. ISSN 0099-9660. Retrieved 2017-04-14.
  3. ^ "University of Toronto - Creative Destruction Lab". University of Toronto - Creative Destruction Lab. Retrieved 2016-06-15.
  4. ^ Beyer, David (3 May 2015). "On the evolution of machine learning". O'Reilly Media.
  5. ^ Simonite, Tom. "AI Supercomputer Built by Tapping Data Warehouses for Their Idle Computing Power". MIT Technology Review.
  6. ^ Beyer, David (February 2016). The Future of Machine Intelligence: Perspectives from Leading Practitioners (PDF). O'Reilly Media.
  7. ^ Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh Zadeh WTF:The who-to-follow system at Twitter, Proceedings of the 22nd international conference on World Wide Web
  8. ^ Harris, Derrick. "Gigaom | Twitter open sourced a recommendation algorithm for massive datasets".
  9. ^ Shu, Catherine. "Tweets Can Guide Emergency Responders Almost Immediately After An Earthquake". TechCrunch. Retrieved 2016-06-15.
  10. ^ Wagner, Kurt. "Can Studying Tweets Lead to Faster Earthquake Recovery?". Mashable. Retrieved 2016-06-15.
  11. ^ "Stanford turns to Twitter to track earthquakes". Engadget. Retrieved 2016-06-15.
  12. ^ Meng, Xiangrui; Bradley, Joseph; Yavuz, Burak; Sparks, Evan; Venkataraman, Shivaram; Liu, Davies; Freeman, Jeremy; Tsai, D. B.; Zadeh, Reza (2015-05-26). "MLlib: Machine Learning in Apache Spark". arXiv:1505.06807. Bibcode:2015arXiv150506807M.
  13. ^ Organisers, KDD 2015. "Matrix Computations and Optimization in Apache Spark". Retrieved 2016-06-15.
  14. ^ "Machine Learning using Big Data: How Apache Spark Can Help | Biomedical Computation Review". Retrieved 2016-06-22.
  15. ^ "DAO: Distributed Algorithms and Optimization". Retrieved 2016-06-15.
  16. ^ "CME 305: Discrete Mathematics and Algorithms". Retrieved 2016-06-15.
  17. ^ "Pre-seed start-up program | Creative destruction Lab (CDL) | Toronto". Pre-seed start-up program | Creative destruction Lab (CDL) | Toronto. Retrieved 2016-06-15.
  18. ^ jackclarkSF, Jack Clark. "Google Sprints Ahead in AI Building Blocks, Leaving Rivals Wary". Retrieved 2016-07-30.
  19. ^ "SIGKDD Awards : 2016 SIGKDD Best Paper Award Winners". Retrieved 2016-07-29.

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