Ximing Lu is a research staff of Large Language Model (LLM) Research at NVIDIA and a Ph.D. candidate at the University of Washington, advised by Professor Yejin Choi. She previously earned her B.S. degree in Computer Science at University of Washington. Her research interest centers around data synthesis, model architecture, science of LLMs, commonsense reasoning, knowledge acquisition, and multimodality. She is a co-recipient of the Best Paper Award at NAACL 2022 and the Outstanding Paper Award at EMNLP 2023.

Shuran received her Ph.D. in Computer Science at Princeton University, BEng. at HKUST. Her research interests lie at the intersection of machine learning, computer vision, and robotics. Song’s research has been recognized through several awards, including the Best Paper Awards at RSS’22 and T-RO’20, Best System Paper Awards at CoRL’21, RSS’19, and finalists at RSS, ICRA, CVPR, and IROS. To learn more about Shuran’s work, please visit: https://shurans.github.io/

Julius Berner is a research scientist in NVIDIA’s Fundamental Generative AI Research (GenAIR) Group. He did his postdoc at Caltech and received his PhD from the University of Vienna in 2023. His research focuses on (probabilistic) machine learning with applications in the natural sciences, including generative modeling, sampling, and neural solvers for partial differential equations and inverse problems. More information can be found on his personal website.