Publications

Proposed a novel framework for generating CFs using finetuned large language models (LLMs), with a focus on structured sensor-derived datasets in health and physiological monitoring

A hybrid attention framework designed for accurate and efficient blood glucose forecasting using multimodal data using feature decomposition and knowledge distillation

An innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson’s Disease (PD) patients, using a single triaxial accelerometer

This paper is enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity.

A comprehensive survey introducing the first cross-domain framework and taxonomy for agentic LLM systems in biology, spanning genomics to clinical imaging and highlighting key evaluation challenges.

A comprehensive review on AI-powered wearable biosensors, highlighting how machine learning and edge AI enable real-time health monitoring and personalized care. The paper discusses key innovations like digital twins and LLMs, along with challenges in privacy, scalability, and clinical integration..

Proposed a novel framework for generating CFs using large language models (LLMs), with a focus on structured sensor-derived datasets in health and physiological monitoring

Developed a method for diagnosis and severity assessment of PD using a model based on Gramian Angular Fields in combination with deep Convolutional Neural Networks (CNNs)

A privacy-preserving system that leverages Gramian Angular Field (GAF) transformations, Federated Learning, and wearable sensor data to detect Freezing of Gait (FoG) in individuals with Parkinson’s Disease

Proposed a wrapper based framework for a National Clinical Data Warehouse (NCDW) designed to address the unique challenges faced by healthcare systems in developing countries

Introduced MetaBoost, a novel hybrid framework that integrates weighted averaging and iterative weight tuning to optimize synthetic data generation and improve model robustness

By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FUSE-MET optimizes label merging, reducing label complexity and improving classification accuracy.

An innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson’s Disease (PD) patients, using a single triaxial accelerometer

Our patient-independent model achieved an overall accuracy of 78% in detecting FoG events using both medication ‘On’ and ‘Off’ state data.

Lung cancer and bladder cancer can be causally linked, so distinguishing between lung and bladder cancer tissues is critical for accurate diagnosis. The goal of this study was to determine the best method for classifying these tissues based on gene expression analysis data.