The current boom in data science, in reality an umbrella term for diverse but loosely connected activity, has been brought about by rapid advances in techniques for collection, storage and dissemination of data, along with increased computational abilities to process it. It is on the scale of the industrial revolution except that it is now the abstract symbols rather than energy and material that is being generated, stored and distributed. This is affecting the older sciences as well where pure model based approaches are being combined with purely data driven ones, to get the best of both the worlds.
The major action, however, is on a different front -- that of fast processing tools for the enormous data that is being generated, sometimes at a high speed. This is the analytic and algorithmic side of data science on which this workshop is focused. This in itself is a multi-verse, ranging from high end pure mathematics feeding it and being fed back in by it, to pure heuristics, often based on natural or social sciences, that work very well but for reasons unknown or ill understood, with a great deal of grey area in between. These are the `engines' behind the new revolution. And unlike the classical applied mathematics that the traditional engineering and sciences mostly grew upon, such as differential and partial differential equations, transform techniques, and so on, these find succor and sustenance from optimization, linear algebra, and probability and statistics.
This workshop aims at opening a window to some of the leading themes in this sphere and expose the participants to both their underpinnings and to the new directions they are headed for, with a focus on probabilistic and optimization techniques.
As part of the program, Michael I. Jordan, Pehong Chen Distinguished Professor, UC Berkeley will give Infosys-ICTS Turing Lectures.
The meeting is partly supported by and
ICTS is committed to building an environment that is inclusive, non-discriminatory and welcoming of diverse individuals. We especially encourage the participation of women and other under-represented groups.