Dr. Ritwik Banerjee
is a computer science researcher and educator, specializing in computational linguistics and its applications to privacy, law, discourse, argumentation, semantics, and pragmatics.
Dr. Ritwik Banerjee is a Research Assistant Professor of Computer Science at Stony Brook University (SUNY), where he has been advancing research and teaching since 2016. He is also affiliated with the university's AI Innovation Institute.
Banerjee is a pioneering expert in natural language processing (NLP) and AI-driven healthcare, with a focus on transforming healthcare through AI applications that personalize patient care. His research explores how machine learning and computational linguistics can analyze clinical data, tailor treatments, improve disease predictions like chronic kidney disease, and minimize risks of adverse drug events. By bridging NLP and AI with patient-centered care, Banerjee is making healthcare more efficient, personalized, and intelligent.
Banerjee has made significant contributions beyond AI-driven healthcare, impacting fields such as misinformation detection, forensic linguistics, and computational argumentation. He is the recipient of several prestigious honors, including two early-concept EAGER grants from the U.S. National Science Foundation, recognizing his innovative research in medical privacy and semantic shifts in information.
Banerjee is passionately dedicated to education in computer science and mathematics. He actively mentors undergraduate, M.S., and Ph.D. students in computer science, data science, and applied mathematics. He also leads the Social & Computational Intelligence Research (SCIRE) group within Stony Brook's Department of Computer Science, fostering the next generation of innovators in AI and NLP.
News & Events
- [JAN 2024] Pilot Grant Award from Stony Brook School of Medicine for Banerjee's research on the use of Natural Language Processing to predict acute kidney injury in critically ill patients.
- [SEP 2023] Grant Award from the Secure and Trustworthy Cyberspace (SaTC) program of the U.S. National Science Foundation (NSF) for research on investigating live medical data against privacy laws.
- [DEC 2022] Invited Talk at the National Institute of Technology (Jaipur, India). "Natural Language Representation: What’s next for embeddings?" @ IEEE CIS Summer School on Research Trends in Artificial Intelligence and Machine Learning for Engineering Challenges.
- [NOV 2022] Invited Talk at the Brookhaven National Laboratory. "Deception in medical information across natural language genres" @ AI & ML Seminar. ↦ slides and video
- [OCT 2022] Research Feature by the Institute for AI-Driven Discovery and Innovation: "Twitter Twisting: Research Behind COVID-19 Misinformation Shared Online". ↦ news article
- [SEP 2022] Research Showcase of Banerjee's work on misleading citations in social media posts. ↦ showcase
- [MAR 2022] Research Feature by the Institute for AI-Driven Discovery and Innovation: "Medical Misinformation in Our Everyday Lives". ↦ news article
- [DEC 2021] Panel on Pandemic 2023: An Information Technology Retrospective. @ 7th IEEE International Conference on Collaboration and Internet Computing (CIC).
- [NOV 2021] Invited Talk at the Department of Computer Science Colloquium, Colorado State University.
- [AUG 2019] Pilot Grant Award from Stony Brook School of Medicine Targeted Research Opportunity Program Award to improve follow-up of incidental findings in radiology reports.
- [JUN 2019] Invited Paper at Conference and Labs of the Evaluation Forum (CLEF) 2019 in recognition of being the "Best of the Labs" in identification of check-worthy claims in media. ↦ article
- [MAY 2018] Grant Award from the Secure and Trustworthy Cyberspace (SaTC) program of the U.S. National Science Foundation (NSF) for research on tracking semantic change in medical information.
- [MAR 2018] Invited Talk at Stony Brook's Digital Humanities Series. "Social identities in text".
- [NOV 2015] Best Paper Award at the IEEE International Conference on Healthcare Informatics (ICHI) for "Patient-centered Identification, Attribution, and Ranking of Adverse Drug Events". ↦ news feature | research article