报告人：尤佳轩
Incoming Assistant Professor
UIUC
主持人：张牧涵 助理教授
北京大学人工智能研究院
时 间：2023/7/24 11:00 - 12:30
地 址：北京大学镜春园79号甲多功能教室
Tencent Meeting：763 234 976
报告题目：Learning from the Interconnected World with Graphs
报告摘要:
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 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. Next, I will 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. Lastly, I will briefly discuss exciting future directions related to graphs and LLMs. Overall, the talk will outline the promising path toward bridging interdisciplinary research and extending the frontiers of AI with graphs.
报告人简介:
Jiaxuan You will join UIUC as a tenure-track Assistant Professor at UIUC CS in 2024. Jiaxuan received his CS Ph.D. from Stanford University and B.Eng from Tsinghua University. 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 WAIC Bright Stars Award. He is the lead organizer of NeurIPS New Frontiers in Graph Learning Workshop 2022 & 2023, a co-organizer of the Stanford Graph Learning Workshop. His Ph.D. research further leads to a startup Kumo AI which demonstrates significant real-world impact. Jiaxuan is actively looking for multiple self-motivated Ph.D. students to work on high-impact problems, starting in 2024 Fall (application starts in 2023 Fall).
Homepage: https://cs.stanford.edu/~jiaxuan.
