Courses, supervision and curriculum
Teaching sits at the intersection the research points to: management students who need to use analytical tools with judgement, and technology students who need to understand the decisions their models feed. Courses are built around applied work — a capstone, a corpus, a dataset the students have to defend conclusions from.
Courses taught
Data Mining with Capstone Project
End-to-end analytical practice, from problem framing through to a defended capstone.
NLP, Text Mining & Semantic Analysis
Language technology for business text — extraction, classification and meaning.
Prompt Engineering
Working effectively with generative models, and knowing where they should not be trusted.
Computational Thinking for Business
Decomposition, abstraction and algorithmic reasoning for managers rather than programmers.
Tech Tools for Modern Business Leaders
The working toolkit — what to use, when, and what each tool quietly assumes.
Applied Programming Tools for B2B Business
Programming as an instrument for business problems, taught to non-specialists.
Doctoral guidance
- Ph.D. supervision in Natural Language Processing — CHRIST (Deemed to be University)
- Research mentorship for postgraduate and doctoral scholars in research design and publication
- Supervision of B.Tech and M.Tech projects — Periyar Maniammai University
- Dissertation supervision at postgraduate level — Presidency University
Curriculum & academic leadership
- Designed and launched UG and PG courses in AI, analytics and emerging technologies
- Advanced curriculum development aligned to industry requirements
- Accreditation and quality assurance — NAAC, NBA, IQAC
- Academic administration, including Assistant Controller of Examinations
- Organised international conferences and academic events
How the courses are built
Applied before abstract
Students meet a real dataset or corpus early, so the theory arrives as an answer to a problem they have already run into.
Judgement, not just tooling
Knowing what a model assumes, and where it should not be relied on, is treated as part of the skill rather than a caveat at the end.
Research-connected
Course material is fed by current publication work, and strong students are routed into research and co-authorship.