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Biography
I am a researcher and developer focused on building intelligent systems that bridge the gap between machine learning theory and real-world impact. My work spans medical imaging, computer vision, and biomedical signal processing, with a strong emphasis on scalable deep learning infrastructure and reproducible research.
I currently lead the development of EchoForge, a modular open-source library designed to accelerate research and deployment of AI models for echocardiographic view classification, segmentation, and phase detection. EchoForge supports reproducibility across benchmarked datasets and integrates seamlessly with clinical and educational workflows.
I also contributed to the development of PulseNote, a real-time ECG annotation and classification platform for Brugada Syndrome, alongside earlier projects in traumatic brain injury (TBI) outcome prediction and 3D echocardiographic video segmentation. My Master’s research achieved a Dice score of 86.7% on the HMC-QU dataset using a customized 3D U-Net architecture.
With proficiency in Python, TensorFlow, Firebase, OpenCV, and Google Cloud Platform, I specialize in designing full-cycle AI pipelines—from data acquisition and preprocessing to real-time inference and deployment. I also maintain the Thrive Research Centre website.
I’m passionate about advancing AI in healthcare and committed to creating intelligent, interpretable, and clinically meaningful solutions.
Qualifications
- MSc in Artificial Intelligence, University of West London, UK
- BTech in Computer Engineering, Ajeenkya D Y Patil University, India
Research Projects
Research Interests
- Computer Vision for Healthcare and Industrial Applications
- Internet of Things (IoT) and Edge AI Systems
- Biomedical Signal and Image Interpretation
- Reinforcement Learning for Game Environments
- Cloud-Based Annotation and Diagnostic Platforms
- Reproducible and Modular Deep Learning Libraries