Computer Vision Research

Computer Vision Research

Researchers working in the field of computer vision develop algorithms which automatically analyze visual data to extract useful knowledge. MBZUAI researchers work across multiple sub-areas of computer vision, including but not limited to facial and object recognition, object detection, counting and segmentation, image and video captioning, bio-metric security, medical imaging, image colorization and enhancement, object tracking, action recognition and video understanding.

Computer vision has important applications in augmented and virtual reality, autonomous cars, service robots, bio-metrics and forensics, remote sensing and smart cities.

The university offers Ph.D. and master's degrees in computer vision with advanced courses and outcomes.

Chair's message

The Computer Vision (CV) Department is comprised of expert faculty and researchers that have been leading their field for decades. Though MBZUAI is new, it has amassed a truly talented team of faculty, researchers, and world-class students that mark MBZUAI as a rising star in CV innovation and research. It is my great pleasure and honor to be part of such a dynamic institution, undertaking such momentous research.

Computer vision algorithms and technologies are rapidly impacting all aspects of our society from security to governance. It will be my goal to balance education with research opportunities. We will interact with industrial partners to identify problems and create solutions.

department-chair

Ian Reid

Department Chair of Computer Vision, and Professor of Computer Vision

Computer vision algorithms and technologies are rapidly impacting all aspects of our society from security to governance.

The mission of the CV Department is to develop and maintain high quality graduate programs in CV and conduct research that yields tangible projects and patents that we can introduce to the market. CV will continue to be at the forefront of security, surveillance, and autonomous vehicle technologies, and we will lead that wave of innovation. You will find the faculty are eager to interact with the next generation of tech leaders and foster synergy.

Ian Reid

READ BIO

Faculty members

faculty_member

Ian Reid

Department Chair of Computer Vision, and Professor of Computer Vision

Read Bio
faculty_member

Fahad Khan

Deputy Department Chair of Computer Vision, and Professor of Computer Vision

Read Bio
faculty_member

Hao Li

Associate Professor of Computer Vision, and Director of MBZUAI Metaverse Center

Read Bio
faculty_member

Ivan Laptev

Professor of Computer Vision

Read Bio
faculty_member

Hosni Ghedira

Professor of Practice of Computer Vision

Read Bio
faculty_member

Salman Khan

Associate Professor of Computer Vision

Read Bio
faculty_member

Karthik Nandakumar

Associate Professor of Computer Vision

Read Bio
faculty_member

Mohammad Yaqub

Associate Professor of Computer Vision

Read Bio
faculty_member

Rao Muhammad Anwer

Assistant Professor of Computer Vision

Read Bio
faculty_member

Hisham Cholakkal

Assistant Professor of Computer Vision

Read Bio
faculty_member

Muhammad Haris Khan

Assistant Professor of Computer Vision

Read Bio
faculty_member

Xiaojun Chang

Visiting Professor of Computer Vision

Read Bio
faculty_member

Xiaodan Liang

Visiting Associate Professor of Computer Vision

Read Bio
faculty_member

Shahrukh Hashmi

Adjunct Professor of Computer Vision

Read Bio
faculty_member

Min Xu

Affiliated Associate Professor of Computer Vision

Read Bio

Projects

Research centers

Center for Integrative Artificial Intelligence (CIAI)

Related

news-image
Thursday, October 24, 2024

New machine-learning approach to inform cancer prognoses presented at MICCAI

  1. cancer,
  2. computer vision,
  3. healthcare,
  4. machine learning,
  5. MICCAI,
  6. medical,
Read More
news-image
Tuesday, October 22, 2024

Adapting foundation models for medical image segmentation: a new approach presented at MICCAI

  1. segmentation,
  2. medical,
  3. MICCAI,
  4. foundation models,
  5. dataset,
  6. image analysis,
Read More
news-image
Thursday, October 17, 2024

Machine-learning-driven predictions for antimicrobial resistance could play a role in addressing looming global health crisis

  1. medical,
  2. ML,
  3. dataset,
  4. health,
  5. computer vision,
  6. research,
  7. machine learning,
Read More