Alan Yuille

About

Alan Yuille is a Bloomberg Distinguished Professor of Cognitive Science and Computer Science at Johns Hopkins University, holding joint primary appointments in these departments. His research interests include computational models of vision, mathematical models of cognition, medical image analysis, artificial intelligence, and neural networks. Dr. Yuille's work spans several disciplines, including computer vision, vision science, and neuroscience. He directs the research group on Computational Cognition, Vision, and Learning (CCVL) and is affiliated with the Center for Brains, Minds and Machines, as well as the NSF Expedition in Computing, Visual Cortex on Silicon. He received a BA degree in mathematics from the University of Cambridge in 1976 and completed his PhD in theoretical physics at Cambridge in 1981 under the supervision of Prof. S.W. Hawking. His career includes positions as a research scientist at MIT's Artificial Intelligence Laboratory and Harvard University’s Division of Applied Sciences, as well as roles as an assistant and associate professor at Harvard until 1996. He was a senior research scientist at the Smith-Kettlewell Eye Research Institute from 1996 to 2002 and served as a full professor at UCLA with joint appointments in computer science, psychiatry, and psychology. Dr. Yuille joined Johns Hopkins University in January 2016 as a Bloomberg Distinguished Professor, where he continues his research and teaching.

Research topics

• Computer Science
• Artificial Intelligence
• Computer vision
• Mathematics
• Machine Learning
• Engineering
• Theoretical computer science
• Mathematical optimization
• Algorithm
• Programming language

Selected publications

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

  arXiv (Cornell University) · 2021 · 3814 citations

  Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard and achieved tremendous success. However, due to the intrinsic locality of convolution operations, U-Net generally demonstrates limitations in explicitly modeling long-range dependency. Transformers, designed for sequence-to-s…

  DOI

• DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2021 · 1019 citations

  Senior authorCorresponding

  Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and g…

  DOI

• Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation

  Lecture notes in computer science · 2020 · 665 citations

  DOI

• Learning From Synthetic Animals

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 119 citations

  Senior authorCorresponding

  Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use synthetic images and ground truth generated from CAD animal models to address this challenge. To bridge the domain gap between real and synthetic images, we propose a novel consistency-constrained semi-supervised learning method (CC-SSL). Our method leverages both spatial and temporal consistencies, to bootstrap weak…

  DOI

• PatchAttack: A Black-Box Texture-Based Attack with Reinforcement Learning

  Lecture notes in computer science · 2020 · 93 citations

  Senior authorCorresponding
  DOI

Recent grants

• Collaborative Research: Visual Cortex on Silicon

  NSF · $750k · 2013–2017

• NIH Grant R01EY02224703

  NIH · $344k · 2015

• NIH Grant R01EY022247

  NIH · $1.2M · 2016

Frequent coauthors

• Adam Kortylewski

  University of Freiburg

  103 shared

• Elliot K. Fishman

  Johns Hopkins University

  100 shared

• Yuyin Zhou

  University of California, Santa Cruz

  87 shared

• Wei Shen

  83 shared

• Cihang Xie

  81 shared

• Lingxi Xie

  81 shared

• Siyuan Qiao

  53 shared

• Weichao Qiu

  Huizhou University

  53 shared

Labs

• Computational Cognition, Vision, and Learning Group (Yuille)PI

Education

• Ph.D., Applied Mathematics and Theoretical Physics

  University of Cambridge

  1986

• Post Doc Fellow (N.A.T.O.), Theoretical Physics

  The University of Texas at Austin

  1981

• Post Doc Fellow (N.A.T.O.), theoretical Physics

  University of California Santa Barbara

  1981

• Distinction Part 3, Mathematics Tripos

  University of Cambridge

  1977

• B.A., Mathematics

  University of Cambridge

  1976

Similar researchers at Johns Hopkins University

• Adam Charlesh-126
• Rama Chellappah-124
• Michael I. Millerh-106