Minhaj Alam, Ph.D.

Dr. Minhaj Alam, Ph.D.

Assistant Professor
Electrical & Computer Engineering
704-687-8408
EPIC 2168

Education and Training:

  • Postdoctoral Fellowship, Stanford University, Stanford, CA, 2022
  • Ph.D. University of Illinois at Chicago, Chicago, IL, 2020
  • M.Sc. Islamic University of Technology, Gazipur, Dhaka, Bangladesh, 2015
  • B.Sc. Islamic University of Technology, Gazipur, Dhaka, Bangladesh, 2013

Research Areas:

  • AI in healthcare and medicine
  • Computer aided diagnostics
  • Imaging biomarkers
  • Precision medicine and federated learning

Selected Publications:

  1. Sina Gholami, FE Jannat, Atalie C. Thompson, Sally Ong, Jennifer I. Lim, Theodore Leng, Hamed Tabkhi, and Minhaj Alam*, Distributed Training of Foundation Models for Ophthalmic Diagnosis Nature Communications Engineering (2025) 4(1), 6
  2. Tania Haghighi, Sina Gholami, Jared Todd Sokol, Aayush Biswas, Jennifer I. Lim, Theodore Leng, Atalie C. Thompson, Hamed Tabkhi, and Minhaj Alam*. “Compact vision language models enable efficient and interpretable optical coherence tomography through layer-specific multimodal learning.” Nature Communications Medicine (2026).
  3. Ahammed Sakir Nabil, Sina Gholami, Theodore Leng, Jennifer I. Lim, and Minhaj Alam*. “Federated Learning for Multi-Disease Ophthalmic Diagnostics using Optical Coherence Tomography Angiography (OCTA).” Ophthalmology Science (2026).
  4. Sina Sholami, Abdulmoneam Ali, Tania Haghighi, Ahmed Arafa, and Minhaj Alam*, “FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3340-3348. 2026. (CVPR FedVision workshop) https://doi.org/10.48550/arXiv.2604.20825 (2026). (Nominated for best paper, oral presentation).
  5.  Tania Haghighi, Sina Gholami, Hamed Tabkhi, and Minhaj Alam*, “Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6640-6649. 2026 (CVPR CV4Clinical workshop) https://doi.org/10.48550/arXiv.2604.22139 (2026).

 

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