Imperial College London

ProfessorMichaelBronstein

Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition
 
 
 
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Contact

 

m.bronstein Website

 
 
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Location

 

569Huxley BuildingSouth Kensington Campus

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Summary

 

Summary

Michael Bronstein joined the Department of Computing as Professor in 2018. He has served as a professor at USI Lugano, Switzerland since 2010 and held visiting positions at Stanford, Harvard, MIT, TUM, and Tel Aviv University. Michael received his PhD with distinction from the Technion (Israel Institute of Technology) in 2007. His main expertise is in theoretical and computational geometric methods for data analysis, and his research encompasses a broad spectrum of applications ranging from machine learning, computer vision, and pattern recognition to geometry processing, computer graphics, and imaging. Michael has authored over 150 papers, the book Numerical geometry of non-rigid shapes (Springer 2008), and holds over 30 granted patents. He was awarded five ERC grants, two Google Faculty Research awards, two Amazon ML Research awards, Facebook Computational Social Science award, Dalle Molle prize, Royal Society Wolfson Merit award, and Royal Academy of Engineering Silver Medal. He is a PI and ML Lead in Project CETI, a TED Audacious Prize winning collaboration aimed at understanding the communication of sperm whales. During 2017-2018 he was a fellow at the Radcliffe Institute for Advanced Study at Harvard University and since 2017, he is a Rudolf Diesel fellow at TU Munich. He was invited as a Young Scientist to the World Economic Forum, an honour bestowed on forty world’s leading scientists under the age of forty. Michael is a Member of Academia Europaea, Fellow of IEEE, IAPR, and BCS, alumnus of the Technion Excellence Program and the Academy of Achievement, and ACM Distinguished Speaker. In addition to academic work, Michael's industrial experience includes technological leadership in multiple startup companies, including Novafora, Videocites, and Invision (acquired by Intel in 2012), and Fabula AI (acquired by Twitter in 2019). Following the acquisition of Fabula, he joined Twitter as Head of Graph Learning Research. He previously served as Principal Engineer at Intel Perceptual Computing (2012-2019) and was one of the key developers of the Intel RealSense 3D camera technology. 

Education

  • Ph.D. (with distinction) in Computer Science, Technion 2007
  • M.Sc. (summa cum laude) in Computer Science, Technion 2005
  • B.Sc. (summa cum laude) in Electrical Engineering, Technion 2002

academic Positions

  • Radcliffe fellow, Institute for Advanced Study, Harvard University (2017-2018)
  • Research affiliate, CSAIL, MIT (2017-2018)
  • Visiting scholar, SEAS, Harvard University (2017-2018)
  • Rudolf Diesel industrial fellow, Institute for Advanced Study, TUM (2017-)
  • Visiting professor, Tel Aviv University (2015-2017)
  • Professor, USI Lugano (2010-)
  • Visiting lecturer, Stanford University (2008-2009)

industrial positions

  • Scientific advisor, Relation Therapeutics (2020-)
  • Head of Graph Learning Research, Twitter (2019-)
  • Co-founder, Chief Scientist, Fabula AI (2018-2019)
  • Co-founder, Technical advisor, Videocites (2014-)
  • Principal Engineer, Intel (2012-2019)
  • Principal technologist, Invision (2009-2012)
  • Co-founder, VP Technology, Novafora (2004-2009)

Publications

Journals

Dong X, Thanou D, Toni L, et al., 2020, Graph Signal Processing for Machine Learning: A Review and New Perspectives, Ieee Signal Processing Magazine, Vol:37, ISSN:1053-5888, Pages:117-127

Gainza P, Sverrisson F, Monti F, et al., 2020, Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning, Nature Methods, Vol:17, ISSN:1548-7091, Pages:184-+

Bronstein MV, Pennycook G, Buonomano L, et al., 2020, Belief in fake news, responsiveness to cognitive conflict, and analytic reasoning engagement, Thinking and Reasoning, ISSN:1354-6783

Conference

Anelli VW, Delic A, Sottocornola G, et al., 2020, RecSys 2020 ChallengeWorkshop: Engagement Prediction on Twitter's Home Timeline, Pages:623-627

More Publications