Projects
Built a real-time ECG heartbeat classification system using CNNs trained on the MIT-BIH dataset. The model automatically extracts key temporal ECG features and was optimized with Optuna, with experiments tracked via MLFlow for robust performance. Deployed as a scalable REST API on AWS EC2, the system accepts CSV inputs and delivers fast, accurate predictions, achieving 95% accuracy in classifying arrhythmias. This end-to-end solution enables cardiologists and researchers to analyze their own data in real-world healthcare settings, improving clinical diagnosis through efficient, scalable arrhythmia detection. Code
Built an end-to-end transformer-based pipeline to classify ~20K blood cells from scRNA-seq data, identifying ~10 immune subtypes using scGPT embeddings, clustering, and marker gene validation. Preprocessing included HVG selection and canonical immune marker validation. Results highlight the potential of large-scale generative models for high-resolution immune profiling. Code
This project introduces a cutting-edge approach to anomaly detection in urban surveillance systems using Two-Stream Inflated 3D (I3D) Convolutional Networks. By capturing both spatial and temporal features more effectively than traditional methods, our model significantly improves detection precision. Leveraging a weakly supervised Multiple Instance Learning (MIL) framework, we treat surveillance videos as collections of ranked clips, enabling efficient anomaly identification with minimal manual labeling. Optimized for real-world deployment, this scalable and high-performing solution sets new standards in public safety technology through intelligent, context-aware video analysis. Code.
