Imperial College London


Faculty of EngineeringDepartment of Computing

Visiting Professor



m.bronstein Website




569Huxley BuildingSouth Kensington Campus





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 machine learning and data science, and his research encompasses a broad spectrum of applications ranging from computer vision and pattern recognition to geometry processing, computer graphics, and biomedicine. Michael has authored over 200 papers, a book, and holds over 35 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, ELLIS, 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, 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. He is also an angel investor and supporter of multiple early-stage startups. 


  • 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)



Taylor-King JP, Bronstein M, Roblin D, 2024, The Future of Machine Learning Within Target Identification: Causality, Reversibility, and Druggability, Clinical Pharmacology and Therapeutics, ISSN:0009-9236

Khakzad H, Igashov I, Schneuing A, et al., 2023, A new age in protein design empowered by deep learning., Cell Syst, Vol:14, Pages:925-939

Bertin P, Rector-Brooks J, Sharma D, et al., 2023, RECOVER identifies synergistic drug combinations in vitro through sequential model optimization., Cell Rep Methods, Vol:3

Veselkov K, Southern J, Gonzalez Pigorini G, et al., 2023, Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient, Scientific Reports, Vol:13, ISSN:2045-2322, Pages:1-9

Zaripova K, Cosmo L, Kazi A, et al., 2023, Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications., Med Image Anal, Vol:88

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