I am a Ph.D candidate in ICME at Stanford University where I am part of the Medical AI and ComputeR Vision Lab (MARVL), where I am fortunate to be advised by Prof. Serena Yeung. Previously, I received a BS in Mathematics from Caltech. My current research interests include geometric deep learning, unsupervised learning, representation learning, and applications to biology and science. You can reach me at jeffgu[at]stanford.edu!

Publications

• Generalizable Neural Fields as Partially Observed Neural Processes (ICCV 2023) [Project page][Paper][Code] Jeffrey Gu, Kuan-Chieh Wang, Serena Yeung

• Hyperbolic Deep Learning in Computer Vision: A Survey (In submission) [Paper][Tutorial][Sample code] Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressler, Jeffrey Gu, Serena Yeung

• NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action (CVPR 2023 Highlight) [Project page][Paper][Code] Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, Joao Pedro Araujo, Jeffrey Gu, C Karen Liu, Serena Yeung

• Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations (NeurIPS 2021) [Paper][Code] Joy Hsu*, Jeffrey Gu*, Gong-Her Wu, Wah Chiu, Serena Yeung

• Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning (UAI 2021) [Paper][Code] Jeffrey Gu, Serena Yeung

• Learning Hyperbolic Representations for Unsupervised 3D Segmentation (NeurIPS Differential Geometry meets Deep Learning (DiffGeo4DL) Workshop 2020) [Paper]
  Joy Hsu*, Jeffrey Gu*, Serena Yeung