Bio
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
College London.
I am currently Professor of Information Engineering, at the University of Cambridge, where I lead
the activities in the Machine
Learning Group and coordinate Cognitive Systems
Engineering. I also have
an appointment as Associate Research Professor in the School of
Computer Science at Carnegie Mellon
University, and I am 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, information retrieval, bioinformatics, and computational
motor control. 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, and semi-supervised
learning.
Editorial Board Memberships:
Selected Publications:
(a sampling of
papers on various topics, look here for
more recent papers)
- Ghahramani, Z., Griffiths, T.L., Sollich, P. (2007)
Bayesian nonparametric latent feature
models (with discussion and rejoinder).
Bayesian Statistics 8. Oxford University Press.
- Beal, M.J., Falciani, F., Ghahramani, Z., Rangel, C., and Wild, D. L. (2005)
A Bayesian approach
to reconstructing genetic regulatory networks with hidden
factors.
Bioinformatics 21(3):349-356.
- Zhu, X., Ghahramani, Z. and Lafferty, J.(2003)
Semi-Supervised Learning Using Gaussian Fields
and Harmonic Functions
The Twentieth
International Conference on Machine Learning
(ICML-2003). pp 912-919[gzipped ps
| pdf]
- Jordan, M.I, Ghahramani, Z., Jaakkola, T.S., and Saul, L.K. (1999)
An introduction to variational methods for graphical models
Machine Learning 37:183-233.
- Roweis, S. and Ghahramani, Z. (1999)
A Unifying Review of Linear Gaussian Models
Neural Computation 11(2):305--345 [abstract]
- Ghahramani, Z. and Wolpert, D.M. (1997)
Modular Decomposition in Visuomotor Learning.
Nature 386:392-395.
- Ghahramani, Z. and Jordan, M.I. (1997)
Factorial Hidden Markov Models.
Machine Learning 29: 245-273.
- Hinton, G.E. and Ghahramani, Z. (1997)
Generative Models for Discovering Sparse Distributed Representations
Philosophical Transactions of the Royal Society B, 352:1177-1190.
- Ghahramani, Z., Wolpert, D.M. and Jordan, M.I. (1996)
Generalization to Local Remappings of the Visuomotor Coordinate
Transformation
Journal of Neuroscience 16:7085-7096.
- Wolpert, D.M., Ghahramani, Z. and Jordan, M.I. (1995)
An Internal Model for Sensorimotor Integration
Science 269: 1880-1882.