# Zoubin Ghahramani

Zoubin Ghahramani is a British-Iranian machine learning researcher, professor at Cambridge, and former chief scientist at Uber, known for Bayesian and probabilistic approaches to AI.

Zoubin Ghahramani (Persian: زوبین قهرمانی; born 8 February 1970) is a British-Iranian researcher in [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) and [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence). He is Vice President of Research at [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind) and Professor of Information Engineering at the [University of Cambridge](https://www.wikiprompt.org/wiki/oxford-university), where he has been a Fellow of St John's College since 2009. His work has focused on Bayesian methods, probabilistic modeling, and scalable learning algorithms, with applications ranging from [neural networks](https://www.wikiprompt.org/wiki/neural-network) to computational neuroscience.

Ghahramani has held academic positions at University College London and Carnegie Mellon University, and has served as Chief Scientist at Uber and head of Google Brain. He has published over 300 papers with more than 100,000 citations, and has been recognized with fellowships and awards including election to the Royal Society and the Royal Society Milner Award.

## Education

Ghahramani was educated at the American School of Madrid in Spain and the University of Pennsylvania, where he earned a dual degree in Cognitive Science and Computer Science in 1990. He then pursued graduate studies at the [Massachusetts Institute of Technology](https://www.wikiprompt.org/wiki/mit-csail), obtaining a Ph.D. in Cognitive Neuroscience from the Department of Brain and Cognitive Sciences. His doctoral research was supervised by [Michael I. Jordan](https://www.wikiprompt.org/wiki/michael-jordan) and Tomaso Poggio, both prominent figures in machine learning and computational neuroscience.

## Early career and postdoctoral work

After completing his Ph.D., Ghahramani moved to the [University of Toronto](https://www.wikiprompt.org/wiki/university-of-toronto) in 1995 as a Postdoctoral Fellow in the Artificial Intelligence Lab, working with [Geoffrey Hinton](https://www.wikiprompt.org/wiki/geoffrey-hinton), a pioneer in deep learning. This period was formative for his later contributions to probabilistic machine learning. From 1998 to 2005, he was a faculty member at the Gatsby Computational Neuroscience Unit at University College London, where he developed variational methods for approximate Bayesian inference and explored graphical models.

## Academic appointments

Ghahramani held a joint appointment as Associate Research Professor in the Machine Learning Department at [Carnegie Mellon University](https://www.wikiprompt.org/wiki/carnegie-mellon-university) from 2003 to 2012. During this time, he continued to advance Bayesian nonparametric models and semi-supervised learning. In 2009, he became a Fellow of St John's College, Cambridge, and later took up a professorship in Information Engineering at the University of Cambridge. He also served as a founding Cambridge Liaison Director of the Alan Turing Institute and as founding Deputy Director of the Leverhulme Centre for the Future of Intelligence, reflecting his interest in the societal implications of AI.

## Contributions to machine learning

Ghahramani's research has significantly influenced several areas of machine learning. He is known for fundamental contributions to probabilistic modeling, including variational inference methods that make Bayesian learning scalable. He pioneered semi-supervised learning and active learning algorithms, which reduce the need for labeled data. His development of sparse Gaussian processes and infinite latent feature models, such as the Indian buffet process, has been highly influential in nonparametric Bayesian statistics. His work also spans computational neuroscience, bioinformatics, and information retrieval, providing mathematical foundations for handling uncertainty and decision-making.

His research has been widely cited, with over 100,000 citations and an h-index of 132, placing him among the most influential researchers in the field. He has contributed to policy reports, including the Royal Society's Machine Learning Report in 2017 and the UK's Future of Compute Review in 2023, which addressed the computational infrastructure needed for AI research.

## Industrial leadership

In 2014, Ghahramani co-founded Geometric Intelligence with Gary Marcus, Doug Bemis, and Ken Stanley, a startup focused on AI research. The company was acquired by Uber in 2016, after which Ghahramani transferred to Uber's AI Labs. He later became Vice President of AI and Chief Scientist at Uber, a role he held until 2020. During his tenure, he oversaw the application of machine learning to ride-hailing, routing, and safety.

In 2020, Ghahramani joined [Google Brain](https://www.wikiprompt.org/wiki/google-brain) as Senior Research Director. He was promoted to Vice President of Research in 2021 and became head of Google Brain, leading the team until its merger with DeepMind in April 2023 to form [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind). He has continued as Vice President of Research at the combined organization, contributing to foundational AI research.

## Awards and honors

Ghahramani was elected Fellow of the Royal Society (FRS) in 2015. His certificate of election highlights his leadership in machine learning, particularly his contributions to probabilistic modeling, Bayesian nonparametric approaches, and variational inference algorithms. It also notes his pioneering work in semi-supervised learning, active learning, and sparse Gaussian processes. In 2021, he received the Royal Society Milner Award in recognition of his fundamental contributions to probabilistic machine learning.

## Legacy and ongoing work

Ghahramani's influence extends beyond his own research through mentorship and collaboration. He has supervised numerous students and postdocs who have become leaders in academia and industry. His advocacy for rigorous probabilistic reasoning has shaped the development of modern AI, including the integration of uncertainty quantification into deep learning systems. As of 2025, he continues to lead research at Google DeepMind, focusing on scalable Bayesian methods and the future of compute infrastructure.

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Last updated: 2026-10-07T16:37:59.393619+00:00