Everything you need to introduce me

Dr. S. Sangeetha is Associate Professor, Artificial Intelligence & Data Science at PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India. An artificial intelligence and privacy researcher with eighteen years of teaching, she has published 30 papers on differential privacy, privacy-preserving machine learning and applied AI.

Dr. S. Sangeetha is Associate Professor, Artificial Intelligence & Data Science at PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India. Her doctorate, completed at PSG College of Technology in 2022, asked how a recommender system can learn what someone likes without learning who they are, and her 30 papers run from association-rule hiding through differential privacy, federated learning for healthcare, and location privacy on distributed ledgers. She is a recognised research supervisor with Anna University, Chennai, and Faculty Coordinator of the ACM Student Chapter at PSG iTech. She has taught for eighteen years across four institutions, and her applied work is done with undergraduate teams who are credited as authors.

Dr. S. Sangeetha is Associate Professor, Artificial Intelligence & Data Science at PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India, where she works on privacy in machine learning systems.

She began teaching in 2008 at PSG Polytechnic College and has taught for eighteen years across four institutions, taking a Master of Engineering in 2015 and a doctorate in Information and Communication Engineering in 2022, both from PSG College of Technology, while carrying a full teaching load. Her thesis addressed a problem that sits awkwardly at the centre of modern recommendation: a recommendation is a statement about one person, so the noise that protects that person is the noise that degrades the recommendation. Her work measures that trade-off rather than assuming it away.

Her 30 publications span five threads — differential privacy and private recommenders, federated and differentially private methods for healthcare data, location privacy and distributed ledgers, applied computer vision and language work with student teams, and the data-mining foundations the field was built on. The survey of big-data privacy she wrote for a Springer handbook remains her most cited work. Her papers have been cited 110 times, for an h-index of 5.

She is a recognised research supervisor with Anna University, Chennai, recognition number 4040073, and Faculty Coordinator of the ACM Student Chapter at PSG iTech. She speaks regularly to student audiences on digital footprints and privacy, most recently to eight hundred students in January 2026, and co-leads an ACM COMPUTE 2026 working group on how Indian computing students handle refutation problems. Her applied research is conducted with undergraduate teams, who appear as authors on the resulting papers; one such project, on real-time infection detection in oyster mushroom cultivation, won a best paper award and released its dataset openly.