We are standing on the brink of an extraordinary transformation. Artificial intelligence is not just a reshaping technology, it also reshapes possibility. Yet even as this technological renaissance accelerates, our society faces many deep and urgent challenges: education, healthcare, poverty, food security, and natural disasters. Can we turn these challenges into catalysts for a new era of innovation? In this talk, we will explore inspiring real-world examples of AI innovation and discover how AI offers us unprecedented opportunities to reimagine the very fabric of research in higher education. I will introduce a bold, unified framework built on three pillars: first, leveraging AI to amplify learning outcomes and remove barriers to quality education for all; second, embedding AI literacy into every discipline to cultivate a generation of agile, future-ready innovators; and third, advancing cutting-edge AI research driven by real-world societal needs, continually expanding the horizons of what education can achieve. This presentation is a call to action for educators, technologists, and students to dream bigger, move faster, and transform education into a launchpad for limitless human potential. The future isn’t something we passively await, but something we should actively create, starting today.
Dr. Jinjun Xiong is Empire Innovation Professor with the Department of Computer Science and Engineering at University at Buffalo (UB). He also serves as the Scientific Director for the $20 M National AI Institute for Exceptional Education, the AI lead for the $10 M IES Center for Early Literacy and Responsible AI, and Director for the SUNY-UB Institute for Artificial Intelligence and Data Science. Prior to that, he was a Senior Researcher and Program Director for AI and Hybrid Clouds Systems at the IBM Thomas J. Watson Research Center. He was the former co-founder and co-director for the IBM-Illinois Center for Cognitive Computing Systems Research (C3SR), the success of which in 5 years has led to the 10-year $200M expansion of the center to the IBM-Illinois Discovery Accelerator Institute. His research interests are on across-stack AI systems research, including AI applications, algorithms, tooling and computer architectures. Many of his research results have been adopted in IBM’s products and tools. He published more than 200 peer-reviewed papers in top AI conferences and systems conferences. His publication won 9 Best Paper Awards and 10 Nominations for Best Paper Awards. He also won top awards from various international competitions, including the championship award for the IEEE GraphChallenge on accelerating sparse neural networks in 2020, and the First Place Awards for the 2019 DAC Systems Design Contest on designing an object detection neural network for edge FPGA and GPU devices, respectively.