Zoubin Ghahramani is Professor of Information Engineering at the
University of Cambridge, Co-Director of Uber AI Labs, and the
Cambridge Director of the Alan Turing Institute, the UK's national
institute for Data Science. He is also the Deputy Academic Director of
the Leverhulme Centre for the Future of Intelligence. He has worked
and studied at the University of Pennsylvania, MIT, the University of
Toronto, the Gatsby Unit at UCL, and CMU. His research spans
Neuroscience, AI, machine learning and Statistics. In 2015 he was
elected a Fellow of the Royal Society.
Zoubin Ghahramani FRS is Professor of Information Engineering at the
University of Cambridge, where he leads the Machine Learning Group
consisting of about 30 researchers, and Co-Director of Uber AI
Labs. He is also currently the Cambridge Liaison Director of the Alan
Turing Institute, the UK's national institute for Data Science,
Deputy Academic Director of the Leverhulme Centre for the Future
of Intelligence, and a Fellow of St John's College Cambridge.
He studied computer science and cognitive science at the
University of Pennsylvania, obtained his PhD from MIT in 1995, and was
a postdoctoral fellow at the University of Toronto. His academic
career includes concurrent appointments as one of the founding members
of the Gatsby Computational Neuroscience Unit in London, and as a
faculty member of CMU's Machine Learning Department for over 10 years.
His current research interests include statistical machine learning,
Bayesian nonparametrics, scalable inference, probabilistic
programming, and building an automatic statistician.
He has published over 250 papers, receiving
over 34,000 citations (an h-index of 78). His work has been funded by
grants and donations from EPSRC, DARPA, Microsoft, Google, Infosys,
Facebook, Amazon, FX Concepts, NTT and a number of other industrial
partners. In 2013, he received a $750,000 Google Award for research
on building the Automatic Statistician. He has served as advisor to
Microsoft Research Cambridge, VocalIQ (acquired by Apple), Cambridge
Capital Management, Echobox, Informetis, Opera Solutions, and several other companies.
He has also served in a
number of leadership roles as programme and general chair of the
leading international conferences in machine learning: AISTATS (2005),
ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a
Fellow of the Royal Society, and in 2016 he was named as one of the
Top Ten Most Influential Scholars in Machine Learning. More information can be
found at http://mlg.eng.cam.ac.uk .
My early childhood was spent in the former Soviet Union and Iran. My family
then moved to Spain where I attended the American School of Madrid for
10 years. I studied at the University of
Pennsylvania where I was given the Dean's Scholar Award and
obtained a BA degree in Cognitive Science and a BSEng degree in
Computer Science and Engineering in 1990. In 1995, I obtained my PhD
in Cognitive Neuroscience from
the Massachusetts Institute of
Technology funded by a Fellowship from the McDonnell-Pew
Foundation. My dissertation was entitled "Computation and Psychophysics
of Sensorimotor Integration" and my PhD advisor was Michael Jordan. I moved
to the University of
Toronto in 1995 where I was an ITRC Postdoctoral Fellow in the
Artificial Intelligence Lab of the Department of Computer Science,
working with Geoffrey Hinton. From 1998 to 2005, I was faculty at
the Gatsby Computational
Neuroscience Unit, University
I am currently Professor of Information Engineering, at the University of Cambridge, where I lead
the activities in the Machine
Learning Group. From 2002 to 2012 I was concurrently also an
Associate Research Professor in the Machine Learning Department at the
Computer Science, Carnegie Mellon
University. I have also been Adjuct Faculty at the Gatsby Unit, University
College London and at POSTECH, South Korea.
My current research interests include Bayesian
approaches to machine learning, artificial intelligence,
statistics, and data science. Statistics provides the mathematical foundations for
handling uncertainty, making decisions, and designing learning
systems. I have recently worked on Gaussian processes, non-parametric
Bayesian methods, clustering, approximate inference algorithms,
graphical models, Monte Carlo methods, semi-supervised
programming, and building an Automatic Statistician.
You can find my publications either on our group publications page, or
Scholar, or for a more complete list, on my CV.