About Me

I am a second-year Master’s student in Computer Science at Columbia University, expected to graduate in May 2025. Currently, I am a Graduate Research Assistant in the AI4VS Lab at Columbia University, working under the guidance of Dr. Kaveri Thakoor. My research focuses on developing anatomically guided cross-attention mechanisms and hybrid deep learning models for 3D medical imaging, with applications in glaucoma and AMD detection. Previously, I conducted research at MIT Lincoln Laboratory, focusing on biomedical imaging and weakly supervised learning, and at Dr. Zhaozheng Yin’s Lab at Stony Brook University, where I developed computer vision solutions for nuclear reactor safety. My early research experience was with Koo Lab at Cold Spring Harbor Laboratory, exploring genomic deep learning and interpretability.

In my spare time, I enjoy running, watching college football and Jeopardy!, playing squash, and gardening.

My Interests

My research interests lie at the intersection of artificial intelligence, computer vision, and medical imaging. Specifically, I focus on designing interpretable deep learning models for 3D medical data, leveraging weakly supervised and self-supervised learning approaches. My work aims to bridge the gap between clinicians and machine learning models, ensuring reliable and clinically relevant outputs. Additionally, I have a strong interest in diffusion models, topology preservation, and leveraging domain-specific data to address real-world challenges in healthcare.

Publications

Journal and Conference Papers

• Kenia, R., Amin, F., Roop, B., Brattain, L., Eastwood, B., Fay, M., Gerfen, C., Glaser, J., Gjesteby, L.** (In Review).** Topology Preserving Deep Supervision for 3D Axon Centerline Segmentation Using Partially Annotated Data.
  
• Kaushal, S., Kenia, R., Aima, S., & Thakoor, K. A. (2024, November). Medical-Expert Eye Movement-Augmented Vision Transformers for Glaucoma Diagnosis. Presented at the 2024 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Retrieved from OpenReview
• Lau, W. T., Tian, Y., Kenia, R., Aima, S., & Thakoor, K. A. (2024, June). Using Expert Gaze for Self-Supervised and Supervised Contrastive Learning of Glaucoma from OCT Data. In Proceedings of the Fifth Conference on Health, Inference, and Learning (pp. 427–445). Retrieved from PMLR
• Kenia, R., Mendil, J., Jasim, A., Al-Dahhan, M., & Yin, Z. (2024, January). Robust TRISO-Fueled Pebble Identification by Digit Recognition. Presented at the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 8142-8150. doi:10.1109/WACV57701.2024.00797