About Me

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I am an Applied Scientist at Amazon in Seattle, where I work on developing foundation models and multi-modal LLMs for the world’s largest fulfillment network. My research broadly lies at the intersection of computer vision, generative AI, and responsible AI, with a particular emphasis on foundation models, self-supervised learning, medical imaging, and fairness.

Previously, I completed my PhD in Computer Science at New York University as a Global PhD Fellow. My doctoral work was recognized with a Best Paper Award at ML4H as well as a nomination for the Pearl Brownstein Doctoral Research Award. During my PhD, I collaborated with the Harvard Ophthalmology AI Lab on a range of research problems including vision-language foundation models and medical diffusion models. I also had the opportunity to gain valuable industry and academic experience through internships and summer schools at Amazon, Cambridge, and Oxford.

My research has been published in several prestigious venues including CVPR, ECCV, Science Advances, MICCAI, and MIDL. Prior to my PhD, I earned a B.S. in Electrical Engineering from NYU Abu Dhabi, graduating summa cum laude.

Research Interests

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• Foundation Models
• Multi-modal LLMs
• Diffusion Models

• Computer Vision
• Medical Imaging
• Responsible AI

Education

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New York University
PhD, Computer Science, 2024
Awards: Global PhD Fellowship, Best PhD Thesis Nomination, Best Paper Award

New York University Abu Dhabi
B.S, Electrical Engineering, 2019 (GPA: 4.0/4.0)
Study Abroad: New York, London, Sydney, Thailand
Awards: Summa cum laude, Full undergraduate scholarship, Silver medalist (iGEM)

Select Publications

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• FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation
  Y Luo^*, MO Khan^*, C Wen^* et al. In Science Advances.

• FairCLIP: Harnessing Fairness in Vision-Language Learning
  Y Luo^*, M Shi^*, MO Khan^* et al. In CVPR.

• MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation
  MO Khan, J Liang, CK Wang et al. In NeurIPS (UniReps).

• Implicit Neural Representations for Medical Imaging Segmentation
  MO Khan, and Y Fang. In MICCAI.

• How Fair are Medical Imaging Foundation Models?
  MO Khan, MM Afzal, S Mirza, and Y Fang. In Machine Learning for Health (ML4H).

• What Is the Best Way to Fine-Tune Self-supervised Medical Imaging Models?
  MO Khan, and Y Fang. In Medical Image Understanding and Analysis (MIUA).

• FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
  Y Tian^*, C Wen^*, M Shi^*, MM Afzal, H Huang, MO Khan et al. In ECCV.

• A Comprehensive Benchmark of Supervised and Self-supervised Pre-training on Multi-view Chest X-ray Classification
  MM Afzal^*, MO Khan^*, and Y Fang. In Medical Imaging with Deep Learning (MIDL).

Gallery

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