7/8/2018, Michael Jordan (Berkeley)
11-12 conferência no auditório do CT2
12-13 almoço
13-14 pequenas reuniões com alunos e docentes

On Computational Thinking, Inferential Thinking and Data Science
Michael I. Jordan
University of California, Berkeley

The rapid growth in the size and scope of datasets in science and technology has
created a need for novel foundational perspectives on data analysis that blend
the inferential and computational sciences. That classical perspectives from these
fields are not adequate to address emerging problems in Data Science is apparent
from their sharply divergent nature at an elementary level—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. On a formal
level, the gap 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. I present several research
vignettes aimed at bridging computation and statistics, discussing the problem of
inference under privacy and communication constraints, the problem of the control
of error rates in multiple decision-making, and the notion of the “optimal way
to optimize”.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the
Department of Electrical Engineering and Computer Science and the
Department of Statistics at the University of California, Berkeley.
His research interests bridge the computational, statistical, cognitive
and biological sciences. Prof. Jordan is a member of the National Academy
of Sciences and a member of the National Academy of Engineering.
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 and
the ACM/AAAI Allen Newell Award in 2009.