Rate My Professor Anagha Kulkarni

Anagha Kulkarni is a Professor of Computer Science and Associate Chair in the Department of Computer Science at San Francisco State University, where she has served since 2013. She received her PhD in Language Technologies from Carnegie Mellon University, MS in Computer Science from the University of Minnesota, Duluth, and Bachelor’s in Computer Engineering from Government College of Engineering, Pune. Over a decade as a computer science educator, Dr. Kulkarni’s research focuses on natural language processing, information retrieval, machine learning, and artificial intelligence, applied to multidisciplinary challenges in public health, women's health, STEM student retention, and medicine. She currently leads several programs promoting diversity and success in computing, including AI-STAARS, BPC-A Faculty Learning Community, Promoting Inclusivity in Computing, Genentech-PINC, gSTAR, and gSTAR Pro.

Dr. Kulkarni’s scholarly work is supported by grants from the National Science Foundation, National Institutes of Health, Genentech Inc., Diffbot Inc., Genentech Foundation, and Smith-Kettlewell Eye Research Institute. Notable funding includes a $661,250 renewal award from Genentech Inc. to develop and expand a professional certificate in Data Science and Machine Learning for biotechnology professionals, serving cohorts like the first group of 24 Genentech employees graduating in 2024. Additionally, she received a Smith-Kettlewell Eye Research Institute grant for an interdisciplinary project designing computational systems to study and translate findings on Cerebral Visual Impairment, a brain-based visual impairment, into clinical practice for pediatric ophthalmology, in collaboration with Dr. Arvind Chandna. Her publications feature key works such as “Socially Responsible Computing in an Introductory Course” (SIGCSE 2024), “Validated Image Caption Rating Dataset” (NeurIPS 2023 Datasets and Benchmarks Track), “Quantitative Measures of Online Health Information (QMOHI): Broadening the impact through improved usability, applicability, and effectiveness” (IEEE ICHI 2023), “Improved Image Caption Rating – Datasets, Game, and Model” (CHI 2023), “Contraception information on the websites of student health centers in the United States” (Contraception 2022), and “Quantifying the Quality of Web-Based Health Information on Student Health Center Websites Using a Software Tool” (JMIR Formative Research 2022). These contributions span health informatics, computing education, and accessible technology development.