On Tuesday 16 January, Professor Michael Jordan delivered a distinguished lecture at UCL on the topic of “Computational Thinking, Inferential Thinking and Data Science.” The lecture was attended by over 200 students and staff of the UCL Department of Computer Science, and the wider UCL Engineering community. 

In his opening address, Professor John Shawe-Taylor, Head of the Department of Computer Science, thanked Professor Jordan for his visit: “Michael is a world-renowned researcher, bridging the fields of computational, statistical, cognitive and biological sciences. It is a great honour to host him today.”

During the lecture, Professor Jordan discussed a rapid growth in the size and scope of datasets in science and technology over the last few decades, which has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. He argued that classical perspectives from these fields are not adequate to address emerging problems in Data Science; in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results can be invoked.

This void is made evident by the lack of a role for computational concepts such as "runtime" in core statistical theory and the lack of a role for statistical concepts such as "risk" in core computational theory. Professor Jordan presented several research vignettes, which aim to bridge computation and statistics, including the problem of inference under privacy and communication constraints, and the surprising role of symplectic geometry.

Michael Jordan is Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science(EECS) and Department of Statistics at the University of California, Berkeley.

His research interests have focused, in recent years, on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics.

Professor Jordan is a member of the American National Academy of Sciences, the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science.

He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015, for contributions to the Theoretical Foundations of Human Cognition, and the ACM/AAAI Allen Newell Award in 2009, for contributions to Computer Science. 

He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.

Read Professor Michael Jordan’s research profile here.