About Me
I'm an AI and Computer Vision Engineer passionate about using artificial intelligence to solve real-world
problems and make a tangible impact. My work focuses on bridging the gap between advanced research and
practical application. I've developed deep learning models that improve critical healthcare procedures,
like heart valve replacements, and engineered large-scale system integrations with an IBM Golden
Partner.
My experience isn't just about coding; it's about driving innovation through collaboration. I've led AI
research projects, successfully deployed models, and enjoy sharing my knowledge by teaching others about
complex topics such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs),
Natural Language Processing (NLP), and the exciting fields of Retrieval Augmented Generation (RAGs) and
AI Agents. I love tackling complex challenges and making AI more understandable.
I am always learning and currently expanding my skills. If you're looking for a dedicated professional
with strong technical skills in AI, Machine Learning (ML), Deep Learning (DL), and a drive to build
solutions that matter, I'd love to connect.
Projects
(Click on a project title to view it on
GitHub where available)
AI-based Website for Automated Pre-Operative Planning in Aortic Valve Replacement using Deep
Learning
- Led a team of 5 members to develop an AI-powered web system for optimizing
valve sizing in heart surgeries.
- Used CTA imaging data to construct 3D anatomical models,
perform aortic valve segmentation, and detect anatomical landmarks
automatically.
- Achieved 90% accuracy, outperforming manual expert estimations (81%).
Tools Used: Python, Deep Learning, MongoDB, Model Deployment, Fine-Tuning.
- Built and compared two CNN architectures to classify images into 38
plant disease categories. One model was designed from scratch, while the second
used Transfer Learning via VGG-16.
- Fine-tuning improved accuracy by 15% over baseline model performance.
Tools Used: Python, TensorFlow, Keras, OpenCV, VGG-16, Image Classification, Data
Augmentation, Jupyter Notebook.
- Trained a real-time Instance Segmentation model on the IDD
dataset using Detectron2, capable of detecting vehicles,
pedestrians, traffic signs, and road markings.
- Achieved high accuracy in complex road environments, suitable for autonomous
driving applications.
Tools Used: Detectron2, Python, PyTorch, Deep Learning, IDD Dataset.