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Zoubin Ghahramani
Professor Zoubin Ghahramani FRS.jpg
Zoubin Ghahramani in 2015, portrait from the Royal Society
Born (1970-02-08) 8 February 1970 (age 55)
Alma mater
Known for
  • Graphical models
  • Variational Bayes
  • Computational neuroscience
Awards FRS (2015)
Scientific career
Fields
Institutions
Thesis Computation and Psychophysics of Sensorimotor Integration (1995)
Doctoral advisor
  • Michael I. Jordan
  • Tomaso Poggio

Zoubin Ghahramani, born on February 8, 1970, is a smart British-Iranian researcher. He is a professor at the University of Cambridge, where he teaches about Information Engineering. He also works at University College London and the Alan Turing Institute. Since 2009, he has been a Fellow at St John's College, Cambridge.

Before that, he was a research professor at Carnegie Mellon University from 2003 to 2012. From 2016 to 2020, he was the main scientist at Uber, a company known for ride-sharing. In 2020, he joined Google Brain, a special team at Google that works on artificial intelligence. He is also a leader at the Leverhulme Centre for the Future of Intelligence.

Education and Early Career

Zoubin Ghahramani went to school in Spain at the American School of Madrid. He then studied at the University of Pennsylvania. There, he earned two degrees in 1990: one in Cognitive Science and one in Computer Science.

He later earned his Ph.D. from the Massachusetts Institute of Technology (MIT). His studies focused on how the brain works and how we think. After finishing his Ph.D. in 1995, he became a researcher at the University of Toronto. He worked in the Artificial Intelligence Lab, learning from other smart scientists. From 1998 to 2005, he was a faculty member at University College London.

Research in Machine Learning

Zoubin Ghahramani is a leader in the field of Machine Learning. This is a type of artificial intelligence that helps computers learn from data. He has made big contributions to how computers can learn and make predictions.

Understanding Data with Bayesian Methods

One of his main areas is called Bayesian statistics. This is a way to use math to understand information, especially when there's some uncertainty. He helped develop "variational methods," which are smart ways for computers to guess answers when they have a lot of data. He also works with "graphical models," which are like maps that show how different pieces of information are connected.

Learning from Complex Information

His research also includes "computational neuroscience," which uses computers to understand how our brains work. He looks at how computers can learn without being told every single detail. This is called "nonparametric Bayesian modelling." He has also worked on finding information, understanding biology with computers, and using math to make smart decisions.

He has written over 200 papers, which are like scientific reports. These papers have been used by other scientists over 80,000 times, showing how important his work is.

Building Companies and Leading Teams

In 2014, Zoubin Ghahramani helped start a company called Geometric Intelligence. This company focused on artificial intelligence. In 2016, Uber bought his company. After the purchase, he joined Uber's AI Labs. He later became the Chief Scientist at Uber, leading their science efforts.

In 2020, he moved to Google Brain. This is a special team at Google that works on advanced AI research. He became a Senior Director there in 2021, continuing his work on cutting-edge technology.

Awards and Recognition

Zoubin Ghahramani is highly respected in the science world. In 2015, he was chosen as a Fellow of the Royal Society (FRS). This is a very special honor for top scientists in the United Kingdom.

His award recognized his leadership in machine learning. He was praised for creating algorithms, which are like step-by-step instructions for computers, that can learn from data. He also helped develop methods for computers to learn even when they don't have all the information. In 2021, he received the Royal Society Milner Award for his important work in machine learning.

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