• Multi-modal learning for imaging and non-imaging medical data.
• Realism assessment in medical imaging.

Ongoing Projects:

• Deep learning models for automated segmentation of the small bowel and body composition on MR enterography studies.

• Teaching Assistant in Deep Learning (Winter Semester 2024).

Hashmi, A. U. R., Almakky, I., Qazi, M. A., Sanjeev, S., Papineni, V. R., Mahapatra, D., & Yaqub, M., "XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model," arXiv preprint arXiv:2403.09240, 2024.

Maani, F., Hashmi, A. U. R., Aljuboory, M., Saeed, N., Sobirov, I., & Yaqub, M., "Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks," *arXiv preprint arXiv:2403.09262*, 2024.

Qazi, M. A., Hashmi, A. U. R., Sanjeev, S., Almakky, I., Saeed, N., & Yaqub, M., "Continual Learning in Medical Imaging from Theory to Practice: A Survey and Practical Analysis," *arXiv preprint arXiv:2405.13482*, 2024.

Hashmi, A. U. R., Mahapatra, D., & Yaqub, M., "Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models," *arXiv preprint arXiv:2403.18996*, 2024.

Sanjeev, S., Zhaksylyk, N., Almakky, I., Hashmi, A. U. R., Qazi, M. A., & Yaqub, M., "FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis," in *Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent.*, Springer, 2024, pp. 131–141.