IC Colloquium: Learning from the Interconnected World with Graphs
By: Jiaxuan You - Stanford University
IC Faculty candidate
Abstract
The fact that our world is fundamentally interconnected presents unique challenges for modern data-driven research. In this talk, I will present my research on investigating the interconnected world through the lens of graphs. Specifically, I will present my research on accelerating graph AI research by systematically investigating the design space and task space for graph deep learning. The research has democratized graph AI for domain experts and helped them with scientific discoveries. Next, I will demonstrate my pioneering research in deep graph generative models which can generate novel realistic graph structures toward desirable objectives. This line of work has broad applications in molecule design and drug discovery. Lastly, I will show that graphs can further power AI problems in general domains. I will specifically cover my research in representing neural networks as relational graphs, which advances the design and understanding of deep neural networks and connects to network science and neuroscience. Overall, the talk will outline the promising path toward bridging interdisciplinary research and extending the frontiers of AI with graphs.
Bio
Jiaxuan You received his CS Ph.D. from Stanford University, advised by Prof. Jure Leskovec. His research investigates scientific and industrial problems through the lens of graphs and develops graph AI methods to solve these problems. He has published 12 first-author papers in NeurIPS, ICML, ICLR, AAAI, KDD, and WWW, many of which are widely recognized. He has created or co-led multiple open-source software with over 20,000 combined GitHub stars. Jiaxuan has received multiple prestigious awards, including a JPMorgan Chase Ph.D. Fellowship, AAAI Best Student Paper Award, World Bank Best Big Data Solution, and Outstanding TA Award from Stanford CS. He was the lead organizer of NeurIPS New Frontiers in Graph Learning Workshop, a co-organizer of the Stanford Graph Learning Workshop, and a program committee member at numerous top-tier AI conferences and journals. His Ph.D. research further leads to a startup Kumo AI which demonstrates significant real-world impact.
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IC Faculty candidate
Abstract
The fact that our world is fundamentally interconnected presents unique challenges for modern data-driven research. In this talk, I will present my research on investigating the interconnected world through the lens of graphs. Specifically, I will present my research on accelerating graph AI research by systematically investigating the design space and task space for graph deep learning. The research has democratized graph AI for domain experts and helped them with scientific discoveries. Next, I will demonstrate my pioneering research in deep graph generative models which can generate novel realistic graph structures toward desirable objectives. This line of work has broad applications in molecule design and drug discovery. Lastly, I will show that graphs can further power AI problems in general domains. I will specifically cover my research in representing neural networks as relational graphs, which advances the design and understanding of deep neural networks and connects to network science and neuroscience. Overall, the talk will outline the promising path toward bridging interdisciplinary research and extending the frontiers of AI with graphs.
Bio
Jiaxuan You received his CS Ph.D. from Stanford University, advised by Prof. Jure Leskovec. His research investigates scientific and industrial problems through the lens of graphs and develops graph AI methods to solve these problems. He has published 12 first-author papers in NeurIPS, ICML, ICLR, AAAI, KDD, and WWW, many of which are widely recognized. He has created or co-led multiple open-source software with over 20,000 combined GitHub stars. Jiaxuan has received multiple prestigious awards, including a JPMorgan Chase Ph.D. Fellowship, AAAI Best Student Paper Award, World Bank Best Big Data Solution, and Outstanding TA Award from Stanford CS. He was the lead organizer of NeurIPS New Frontiers in Graph Learning Workshop, a co-organizer of the Stanford Graph Learning Workshop, and a program committee member at numerous top-tier AI conferences and journals. His Ph.D. research further leads to a startup Kumo AI which demonstrates significant real-world impact.
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Practical information
- General public
- Free
Contact
- Host: Bob West