Professor of Information Engineering
Academic Division: Information Engineering
Research group: Computational and Biological Learning
I have broad interests in probabilistic modelling, machine learning, Bayesian statistics, and Big Data. My work focuses on advancing the general mathematical and algorithmic foundations of these fields, although I have also worked on applications of Bayesian machine learning to computational biology and bioinformatics, econometrics and quantitative finance, recommender systems, network modelling, and scientific data analysis.
Manufacturing, design and materials
Interested in many areas of application of machine learning, and have collaborations with many companies, including Microsoft, Google, Facebook, Schlumberger, Infosys, NTT, and quantitative finance companies.
Complex, resilient and intelligent systems
Interested in modeling, inference, representation and decision making under uncertainty, a central theme in URR.
- Fellow of St. John’s College
- Member of the Cambridge Neuroscience Initiative
Zoubin Ghahramani 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 focuses on nonparametric Bayesian modelling and statistical machine learning. He has also worked on applications to bioinformatics, econometrics, and a variety of large-scale data modelling problems. He has over 200 publications in fields such as computer science, statistics, engineering, and neuroscience. He has served on the editorial boards of JMLR, JAIR, Annals of Statistics, Machine Learning, Bayesian Analysis, and IEEE Transactions on Pattern Analysis and Machine Intelligence (as Associate Editor in Chief). He also served on the Board of the International Machine Learning Society, and as Program Chair of the NIPS, ICML and AISTATS conferences.