2026 Summer Intern Project at the CCVL Lab

Lab Page

https://ccvl.jhu.edu/

Advisor Profile

Professor Alan Yuille is a Bloomberg Distinguished Professor in the Department of Computer Science and the Department of Cognitive Science at Johns Hopkins. He published many influential papers in computer vision, cognitive science, etc. He has won the ICCV Marr Award and is an IEEE Fellow.

Overall Information

We are seeking several summer research interns for 2026. If interested, please email Professor Alan Yuille (ayuille1@jhu.edu) with your resume attached. Interns will collaborate with Professor Alan Yuille and other graduate students at CCVL. The internship starts in May, and the duration is flexible (between 6 months to 1 year). Exceptional interns from previous years have been published as the first authors at top conferences in computer vision or medical image processing, such as CVPR, ICLR, and MICCAI. Priority will be given to exceptional interns for Ph.D. applications.

Research Directions

Our lab’s research lies in computer vision and machine learning. The detailed research groups include:

World model
Medical image analysis
Transformers architecture
Vision, language, and action

Requirements

The applicants are expected to fulfill one of the following group’s requirements. Besides, we would really appreciate it if you could specify which group you’re interested in when submitting your applications. We strongly enough you to read the related papers of our group and learn some preliminary knowledge by checking our publication list: https://ccvl.jhu.edu/publication/

The requirements for different groups are as follows,

World model:

Basic skills in using Python, PyTorch, transformers, and other machine-learning libraries;
Understanding recent 3D vision or generative models techniques. At least one of the following topics

3D from images (e.g., pose, shape, and 3D reconstruction)
Video generative models (e.g., Wan, etc.)
Other generative models-related topics (e.g., VAE, DDPM, DDIM, etc.)

Publications or submissions in related conferences and journals, e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, TPAMI, IJCV.

Medical image analysis:

Proficiency in computer vision and image analysis concepts;
Proficiency in Python programming to use prevalent frameworks (such as nnU-Net and MONAI);
Prior experience with the analysis of radiological image datasets for AI applications is preferred;
Relevent publication/submission in conferences/journals (such as MICCAI, TMI, and MedIA) is preferred.

Transformers architecture:

Basic skills in using Python, PyTorch, transformers, and other machine-learning libraries;
Basic mathematics foundations in related areas, e.g., statistical learning and optimization;
Knowledge of the basic concepts of the Transformers architectures;
Publications or submissions in related conferences and journals, e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, TPAMI, IJCV.

Vision, language, and action:

Proficiency in using Python, PyTorch, transformers, and other machine-learning libraries;
Basic knowledge in common deep learning methods in language modeling and multimodal learning (E.g. CLIP, SigLIP, Transformer);
Hands-on experience with the vision-language model or large language model (e.g., LLaMA, LLaVA, Qwen, etc.)
Understanding the concepts of robotics and embodied AI. Hands on experience on real/simulated robots, familiar with forward&inverse kinematics
Publications or submissions in related conferences or journals, e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, TPAMI, IJCV.