Interdisciplinary Intelligence: AI, Data, and Social Sciences in Dialogue
Abstract
The current Generative AI (GAI) revolution is built on a simple premise: bigger is better. Tech giants are fiercely chasing the "scaling law," betting that massive data and immense compute will unlock Artificial General Intelligence (AGI). This talk challenges that assumption, arguing that scaling alone is insufficient to achieve AGI. Grounded in insights from social media mining and data science, we propose Interdisciplinary Intelligence, a collaborative framework essential for the next frontier of AI. This framework not only drives technical innovation but also tackles the urgent socio-technical challenges of the LLM era, including bias, reliability, equity, evaluation, and the integrity of AI-assisted research. This talk serves as a call to action for computer scientists, data scientists, social scientists, ethicists, and policymakers to come together and co-create the future of responsible AI.
Short Bio
Dr. Huan Liu is a Regents Professor and Ira A. Fulton Professor of Computer Science at Arizona State University. He is the recipient of the ACM SIGKDD 2022 Innovation Award for his outstanding contributions to the foundation, principles, and applications of social media mining and feature selection for data Mining. He co-authored the textbook, Social Media Mining: An Introduction, by Cambridge University Press. He is Editor-in-Chief of ACM TIST, and Field Chief Editor of Frontiers in Big Data. He is a Fellow of AAAI, AAAS, ACM, and IEEE.Title to be added
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Short Bio
Nadia Berthouze ....Mind the Gap: Grounding Foundation Models with Local Context for Real-World Applications
Abstract
Foundation models are remarkably capable but fundamentally lack local context, which are the domain-specific, community-specific, and structurally rich knowledge required by real-world applications. In this talk, I will discuss our work on bridging this gap through knowledge graphs, retrieval-grounded architectures, and domain-aware reasoning. I will show how these approaches enable deployed tools for wildlife conservation research, navigation of Colombia's Truth Commission archives, and personalized nutritional reasoning; the very settings where generic model knowledge falls short and local context is everything. I will close with reflections on what it takes to move from methods to tools that practitioners actually use.
Short Bio
Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering and the Lucy Family Director for Data & AI Academic Strategy, leading the Data, AI, and Computing Initiative at the University of Notre Dame. His research is focused on artificial intelligence, data science, and network science, and is motivated by the question of how technology can advance the common good through convergence. He is a Fellow of: the Institute of Electrical and Electronics Engineers (IEEE); the Association of Computing Machinery (ACM); the American Association for the Advancement of Science (AAAS); and the Association for the Advancement of Artificial Intelligence (AAAI). He is the recipient of multiple awards, including the National Academy of Engineers New Faculty Fellowship, IEEE CIS Outstanding Early Career Award, Rodney F. Ganey Community Impact Award, IBM Big Data & Analytics Faculty Award, IBM Watson Faculty Award, and the 1st Source Bank Technology Commercialization Award. He is a serial entrepreneur, having (co-) founded multiple start-ups.
