Index

Interpretable Vision Transformers with Attention Maps for Phonological Precision Assessment from MRI

Reinforcement Learning for Centralized Fault Coordination in Power Systems

In this project, we develop a hybrid reinforcement learning framework for adaptive protection in power grids with high DER penetration. A centralized model is first trained using system-wide current, voltage, and impedance data to coordinate both primary and backup relays, followed by decentralized fine-tuning using only local measurements to ensure autonomous operation in case of communication loss. The approach aims to improve relay coordination, robustness, and decision-making by exploring different recurrent network architectures such as RNN and LSTM.

Transformer-Based Forecasting Model for Fault Detection in Power System Protection

This project explores transformer-based forecasting models for intelligent fault detection in electrical power systems. The approach reframes fault detection as a prediction problem, where the model learns the normal temporal dynamics of high-frequency voltage and current waveforms and identifies deviations as anomalies. By doing so, it avoids the heavy reliance on labeled data that limits many existing machine learning approaches in protection systems.

The study evaluates several transformer architectures on a large set of physics-based simulations that represent realistic grid conditions, fault types, and operating scenarios. The results demonstrate that prediction-based attention models can achieve high detection accuracy and robustness, even under scarce data and varying grid configurations. This work provides a promising foundation for more adaptive, data-efficient, and resilient protection schemes in future power networks.

Report Generation in pathology using WSIs

This project focuses on developing methods for processing large-scale digital pathology datasets and extracting meaningful features from whole slide images to support automated report generation. Emphasis is placed on efficient handling of gigapixel image data and preparing it for use in vision-language models for clinical applications.