Publications

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

We introduce MetaBoost, a novel hybrid framework that integrates SMOTE, ADASYN, and CTGAN, optimizing synthetic data generation through weighted averaging and iterative weight tuning to enhance the model’s performance (achieving up to 1.87% accuracy improvement over individual balancing techniques).

AIMI is a knowledge-guided adherence forecasting system that leverages smartphone sensors and previous medication history to estimate the likelihood of forgetting to take a prescribed medication.

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.

We propose “Artificial Intelligence (AI) for Modeling and Explaining Neonatal Health” (AIMEN), a deep learning framework that not only predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum risk factors but also provides the model’s reasoning behind the predictions made.

This research aims to develop a lifestyle intervention system, called MoveSense, that forecasts a patient’s activity behavior to allow for early and personalized interventions in real-world clinical environments.

We propose an automated risk prediction system that can make recommendations to clinicians in real-time with machine learning classifiers that predict the risk of developing neurological impairment.

ActSafe utilizes a context-free grammar based approach for extracting and mapping MTCs from patient education materials.

We focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance.

We propose an algorithm that can reconstruct the missing input data for unavailable sensors.