AI Engineer | Medical Imaging | Deep Learning Enthusiast

About Me

I'm an AI Engineer with over 4+ years of experience in developing advanced deep learning models tailored for medical imaging and its analysis. My work has contributed to FDA-cleared solutions and enhancing clinical outcomes. I specialize in creating efficient deep-learning pipelines while maintaining compliance with FDA & HIPAA regulations.

• Deep Learning: PyTorch, TensorFlow, MONAI
• Medical Imaging: DICOM, NIfTI, 3D Slicer
• Cloud & DevOps: AWS (S3, EC2, ECR), Docker
• Data Science: Python, SQL, Pandas, Scikit-Learn
• Algorithms: Segmentation, Classification, Clustering

• Developed a multi-label segmentation algorithm for Breast DCE-MRI that achieved FDA 510k clearance.
• Improved clinical performance by 15% and reduced catastrophic failures by 95%.
• Automated the training and evaluation pipeline using Docker and AWS EC2/ECS.
• Implemented SAM-like Data-flywheel to scale the data generation. View Project

• Working on nn-UNet-esque customizable segmentation pipeline to develop and test off-the-shelf segmentation models with minimal intervention.
• Create end-to-end pipeline that delivers a dockerized version of the best model along with a model-card. View Project

• Created a pipeline using DenseNet variants for detecting COVID-19 and assessing severity from CXR images.
• Enhanced model robustness against adversarial examples and data noise. View Project

• Investigated different ensembling techniques for deep-learning based robot grasping algorithms View Project

• Seeing beyond cancer: multi-institutional validation of object localization and 3D semantic segmentation using deep learning for breast MRI Publication arvix

• Performance of an AI-powered Visualization Software Platform for Precision Surgery in Breast Cancer Patients Link

• A 3D Visualization Method for Breast Cancer Surgeons and Patients Link

I'm always open to collaborating on innovative projects in AI and healthcare. Feel free to reach out via LinkedIn