Rohini K. Srihari

Dr. Rohini K. Srihari is a full Professor and Associate Chair in the Department of Computer Science and Engineering at the University at Buffalo, The State University of New York, and also serves as an Adjunct Professor in the Department of Linguistics within the School of Engineering and Applied Sciences. Her expertise lies at the intersection of natural language processing, artificial intelligence, information retrieval, and AI for social good.

Education:

• PhD in Computer Science, University at Buffalo, 1992
• B.Math, University of Waterloo, Canada

Research Interests:

Dr. Srihari’s research spans a wide range of topics including conversational AI systems (especially empathetic and socially-aware chatbots), disinformation detection and attribution, multilingual text mining for low-resource languages, and predictive analytics for social unrest using heterogeneous data sources. She also explores multimedia information retrieval and AI-driven early warning systems for social and economic disruption.

Recent Research Trends:

Her recent publications reflect a strong focus on conversational AI and AI for social good, with applications in mental health support, misinformation mitigation, and empathetic dialogue systems. She has led interdisciplinary teams in competitions like the Alexa Prize Socialbot Grand Challenge, where her team won the bronze medal in 2021.

Scientific Awards & Recognition:

• Bronze Prize, Alexa Prize Socialbot Grand Challenge 4 (2021)
• NSF Grant: Purposeful Conversational Agents based on Hierarchical Knowledge Graphs
• Holds two US patents in multilingual text mining and face recognition

Advising & Funding:

Dr. Srihari has supervised numerous PhD and Master's students across departments and has led research funded by NSF, DARPA, IARPA, and the US Government. She has also served as Chief Data Scientist at PeaceTech Lab, leading AI initiatives for conflict prevention.

Research Labs & Teams:

She directs the Conversational AI for Social Good research group, which focuses on building trustworthy, empathetic, and socially responsible AI systems. The group integrates deep learning, knowledge graphs, and symbolic AI to address real-world societal challenges.