Name:Smita Ghaisas

Bio:

Smita Ghaisas is an independent researcher and consultant on automating disambiguation of complex businee-critical texts such as contracts, regulations, and software requirements. She serves on the IP Framework Working Group of NASSCOM Deeptech, India. Formerly, Smita was a Chief Scientist and Distinguished Engineer at Tata Research, Development and Design Center, Tata Consultancy Services Ltd (TCSL) where she led Data Driven Requirements Engineering (RE) Research. She additionally led the technical task force for an enterprise-wide initiative in AI powered governance of contractual obligations at TCS. For this initiative, she defined a technical strategic solution and roadmap and deployed it in coordination with the CIO’s office, Delivery Excellence Group, and Corporate Legal at TCS. Their solution for identifying, classifying, and analyzing contractual obligations has recently won the prestigious Blitz award at the TCS Global summit. She is a Senior Member of IEEE.

She has published her research in international journals and conferences of high repute and serves on program committees of some of the flagship conferences in Software Engineering. She was a co-chair for the Industry Innovation track of RE 18 Banff, Canada (https://www.re18.org/organization/organizingCommittee.html), keynote speaker for SST@ICSE19 Montreal Canada (https://sst2019.chalmers.se/program/keynote/), and editor for IEEE Software Special Issue on Software Engineering in Society (https://www.computer.org/csdl/magazine/so/2020/03/09068302/1j30YOQomCQ) . She serves on the editorial board for the prestigious IEEE Empirical Software Engineering (EMSE) journal. ( Springer https://lnkd.in/gvfreCr)

Her current interests include creating scalable AI solutions that solve non-trivial practical problems in large enterprises. This involves identifying and addressing multiple interesting Data Science challenges.

Country:India

Affiliation:Independent Researcher and Consultant

Research interests:Smita’s current research interests include disambiguation of complex and critical texts such as regulations and contracts by employing deep learning techniques coupled with learning theory-based experiments in crowd sourcing of interpretation tasks.