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| Zoubin Ghahramani | |
|---|---|
| Name | Zoubin Ghahramani |
| Birth date | 1970s |
| Nationality | British–Iranian |
| Fields | Machine learning, Statistics, Artificial intelligence |
| Institutions | University of Cambridge, University of Toronto, University College London, Google, Uber, Microsoft Research |
| Alma mater | University of Cambridge, University of London |
| Doctoral advisor | Geoffrey Hinton |
| Known for | Bayesian machine learning, Gaussian processes, probabilistic modelling |
Zoubin Ghahramani
Zoubin Ghahramani is a British–Iranian researcher in machine learning, statistics, and artificial intelligence known for work in Bayesian methods, probabilistic modelling, and scalable inference. He has held academic positions at University of Cambridge, University College London, and University of Toronto, and industry roles at Google DeepMind, Uber Technologies, Microsoft Research, and Autonomy Corporation. His career intersects research, entrepreneurship, and leadership across institutions such as Alan Turing Institute, Royal Society, and conferences including NeurIPS, ICML, and AISTATS.
Ghahramani was born to a family with ties to Iran and raised in the United Kingdom where he pursued studies at University of Cambridge and University of London. He completed undergraduate and graduate work under supervision connected to Geoffrey Hinton and engaged with research communities at Cambridge University Computer Laboratory, Gatsby Computational Neuroscience Unit, and Royal Society University Research Fellowships. During his formation he interacted with scholars from Oxford University, Imperial College London, Princeton University, Massachusetts Institute of Technology, and Stanford University.
Ghahramani held faculty positions at University of Cambridge, University of Toronto, and University College London, collaborating with groups from Carnegie Mellon University, ETH Zurich, École Polytechnique Fédérale de Lausanne, and Max Planck Institute for Intelligent Systems. He served roles at the Gatsby Computational Neuroscience Unit, co-organized workshops at NeurIPS, taught summer schools associated with Royal Society and Simons Institute for the Theory of Computing, and supervised doctoral students who later joined labs at DeepMind, OpenAI, Facebook AI Research, Microsoft Research, and Amazon Web Services.
Ghahramani's work advanced Bayesian approaches to Gaussian processes, variational inference, and probabilistic graphical models, influencing projects at Google Brain, DeepMind, and IBM Research. He contributed to methodologies used by teams at Apple, Netflix, Uber Technologies, and Airbnb for recommendation and prediction systems, and influenced algorithmic frameworks adopted by TensorFlow, PyTorch, and Scikit-learn. His research intersects with theoretical advances from David MacKay, Christopher Bishop, Yoshua Bengio, Yann LeCun, and Judea Pearl, and practical systems developed at Amazon, Microsoft, and Facebook. Topics include approximate Bayesian computation used alongside techniques from Markov chain Monte Carlo, Expectation-Maximization algorithm, Variational Bayes, and connections to work by Radford Neal and Neil Lawrence.
In industry, Ghahramani held leadership and advisory roles at Google DeepMind, Uber AI Labs, Microsoft Research, and startups connected to Silicon Valley and Cambridge, UK ecosystems. He contributed to product research impacting Waymo, Tesla, and platforms like YouTube and Google Ads, and advised ventures funded by Sequoia Capital, Accel Partners, and Index Ventures. He co-founded or advised startups alongside entrepreneurs from Autonomy Corporation, DeepMind Technologies, Graphcore, and engaged with incubators such as Y Combinator and Cambridge Enterprise.
Ghahramani’s honors include recognition from bodies such as the Royal Society, Royal Academy of Engineering, and invited lectures at Harnack House, Baylor College of Medicine, and plenaries at NeurIPS, ICML, and AISTATS. He has been listed among influential researchers in rankings by Google Scholar, awarded fellowships related to EPSRC, and acknowledged in community awards featuring organizations like AAAI, IEEE, and ACM. His students and collaborators have received prizes from Royal Society, Simons Foundation, and DARPA programs.
Key publications by Ghahramani include influential papers on Gaussian processes coauthored with figures such as Carl Edward Rasmussen, works on variational methods with connections to Thomas Minka, and tutorial surveys cited alongside texts by Christopher Bishop and David MacKay. His research has been cited in applications across genomics projects affiliated with Wellcome Trust and European Molecular Biology Laboratory, neuroscience collaborations at Max Planck Society and Cold Spring Harbor Laboratory, and industry deployments at Amazon Web Services and Google Cloud Platform. He has contributed to edited volumes and proceedings organized by MIT Press, Springer, and IEEE Press, and his influence persists through citations in journals like Nature, Science, Journal of Machine Learning Research, and conference proceedings for NeurIPS and ICML.
Category:British computer scientists Category:Machine learning researchers Category:Bayesian statisticians