Yinshuang Xu is currently pursuing her fifth year of PhD studies in Computer and Information Science at the University of Pennsylvania, where she is advised by Prof. Kostas Daniilidis. She previously graduated from Shanghai Jiao Tong University with a bachelor’s degree in Engineering Mechanics and later received her master’s in Robotics from the University of Pennsylvania. Her research interests include equivariance and geometric deep learning, with a focus on their use in computer vision and machine learning.

Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998. He is an IEEE Fellow. He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens, 1986, and his PhD in Computer Science from the University of Karlsruhe, 1992, under the supervision of Hans-Hellmut Nagel.   He received the Best Conference Paper Award at ICRA 2017. He co-chaired ECCV 2010 and 3DPVT 2006. His most cited works have been on event-based vision, equivariant learning, 3D human pose, and hand-eye calibration

Robin Walters is an assistant professor in the Khoury College of Computer Sciences at Northeastern University, where he leads the Geometric Learning Lab. Robin’s research seeks to develop a fundamental understanding of the role symmetry plays in deep learning and to exploit this to improve the generalization and data efficiency of deep learning methods. This includes designing equivariant neural networks, symmetry discovery methods, and creating a theory of symmetry for model parameters.  He has applied these methods to improve models in domains with complex dynamics including climate science, transportation, and robotics

Dian Wang is a Ph.D. candidate at the Khoury College of Computer Sciences, Northeastern University, where he is co-advised by Prof. Robert Platt and Prof. Robin Walters. His research lies at the intersection of Machine Learning and Robotics, with a particular focus on Geometric Deep Learning and its applications in Robot Learning. Recently, Dian has focused on enhancing robotic manipulation through the use of equivariant methods to boost learning efficiency and performance. Dian has contributed to leading conferences and journals, including ICLR, NeurIPS, CoRL, ICRA, RSS, IJRR, AR, ISRR, and AAMAS. Dian was awarded the JPMorgan Ph.D. Fellowship in 2023 and the Khoury Research Fellowship in 2019.

I am an Assistant Professor of Applied Mathematics and of Computer Science at Harvard, where I lead the Geometric Machine Learning Group. My research focuses on utilizing geometric structure in data for the design of efficient Machine Learning and Optimization methods with provable guarantees. In 2021-2022, I was a Hooke Research Fellow at the Mathematical Institute in Oxford and a Nicolas Kurti Junior Research Fellow at Brasenose College. In Fall 2021, I was a Research Fellow at the Simons Institute in Berkeley, where I participated in the program Geometric Methods for Optimization and Sampling. Previously, I received my PhD from Princeton University (2021) under the supervision of Charles Fefferman, held visiting positions at MIT and the Max Planck Institute for Mathematics in the Sciences and interned in the research labs of Facebook, Google and Microsoft. My research is supported by the National Science Foundation, the Sloan Foundation and the Harvard Data Science Initiative.