Dr. Gautam Shroff is a Vice President and Chief Scientist in TCS and heads TCS Research.

Prior to joining TCS in 1998, Dr. Shroff had been on the faculty of the California Institute
of Technology, Pasadena, USA (1990 - 91) and thereafter of the Department of Computer
Science and Engineering at Indian Institute of Technology, Delhi, India (1991 - 1997). He
has also held visiting positions at NASA Ames Research Center in Mountain View, CA,
and at Argonne National Labs in Chicago. He completed his B.Tech degree in Electrical
Engineering from IIT Kanpur in 1985, and Ph.D in Computer Science from Rensselaer
Polytechnic Institute Troy, NY in 1990.

In 1994 he was conferred the ‘Young Scientist Award from the Indian Department of
Atomic Energy. Dr. Shroff has published over 60 research papers in the areas of
computational mathematics, parallel computation, distributed systems, software
architecture, software engineering, big data, information fusion, virtual reality as well as
artificial intelligence including machine learning, deep learning, Bayesian inference and
natural language processing. He has written two books “Enterprise Cloud Computing”
published by Cambridge University Press, UK, in October 2010, and “The Intelligent
Web”, published by Oxford University Press, UK, in 2013 (paperback ed. 2015).
In addition to corporate research and writing, in 2012 Dr. Shroff offered a massive open
online course, or MOOC, titled “Web Intelligence and Big Data” in his capacity as an
adjunct professor at IIT Delhi and IIIT Delhi.

Dr. Shroff is an active member of ACM and ACM-India, is a member of the ACM India
Council, was the founding chair of the ACM-India SIG on Knowledge Discovery from
Data (IKDD), which is also the India chapter of ACM SIGKDD; and was a member of the
AI Task Force constituted in 2017 by the Ministry of Commerce & Industry in the Govt.
of India.

AG 1, AG 2, AG 3, AG 4, AG 5, RG1, SWS, MMCI   Info

MPI Audience

English

45 Minutes

Saarbrücken

In this talk I’ll first give an overview of TCS Research and how traditional enterprises are
applying AI techniques not only for automation of tasks but also amplification of human
expertise. In particular I shall then focus on our experience implementing different
categories of conversational systems in TCS, some at scale, using deep learning and
knowledge graphs: (i) A virtual assistant that answers FAQs via a hierarchical sequence-to-
sequence deep network that handles both co-references and abstract anaphora (ii) A
conversational help-desk using interactive slot-filling with a sequence-to-sequence deep
network (iii) Knowledge synthesis where users both ask and provide information, and where
the system answers natural language queries on a knowledge graph as well proactively asks
questions from users to expand its knowledge when needed (iv) Interacting with data in
natural language by first migrating data to a knowledge graph and then translating natural
language queries to paths this graph. Finally, (v) the creation of knowledge schemas from
scratch via natural language interaction, so as to enable the creation of systems such as (iii)
and (iv) via dialog. Last but not least I’ll conclude by speculating on the future of IT
systems that might include not only a conversational interface but also semantic knowledge
representation as well as the integration of predictive models with data models.

Kurt Mehlhorn

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