Srijani Das

Built an ML classifier for stars, galaxies, and quasars using SDSS photometric data. Designed a custom three-stage hybrid model (PRISM) combining a color graph encoder, mutual information attention, and calibrated LightGBM.

Deployed an interactive dashboard with real-time predictions and model comparison across Random Forest, XGBoost, and PRISM.

Developed a full-stack recruitment consulting portal for a startup, with an admin panel for job and candidate management. Built RESTful API backend using Node.js with SQLite database for CRUD operations on job postings.

Implemented secure authentication and a responsive frontend using HTML, CSS, and JavaScript.

Built a local Python application that transcribes audio files and generates AI-powered summaries at multiple detail levels (short, medium, detailed). Implemented privacy-focused architecture with all processing performed locally using Whisper for speech-to-text and BART for summarization.

Developed user-friendly Streamlit interface with downloadable transcript and summary export functionality. Supports multiple audio formats (MP3, WAV, M4A, FLAC, OGG) with all computation on user's device to ensure privacy.

Conducted research on automated plant disease detection using deep learning under faculty supervision. Implemented and compared CNN, Vision Transformer (ViT), and hybrid ViT+CNN architectures for multiclass classification on the PlantVillage dataset, achieving ~99% accuracy.

Applied Grad-CAM visualizations to interpret model predictions and enhance explainability. Research was extended into a published paper in Frontiers in Artificial Intelligence (March 2026).