Research Projects

Research Projects

Boiling Crisis (CHF) Detection Project

Problem & Motivation

Solution: 

My dissertation proposes a 4-phases solution to address this:

Smart Driving Project for Early Alzheimer's Detection (NIA Funded)

We present the Smart Driving System, a pioneering framework leveraging multi-modal sensing (MMS) to evaluate driving behavior for the early detection of Alzheimer's disease and related dementias (AD/ADRD). This project is a collaborative effort between several ASU labs from the School of Electrical, Computer, and Energy Engineering, the School of Engineering for Matter, Transport, and Energy, and the School of Computing and Augmented Intelligence at Arizona State University (ASU). It also involves key contributions from the ASU-Mayo Clinic Medical Devices and Methods Laboratory, the Center for Bioelectronics and Biosensors at ASU's Biodesign Institute, and the Barrow Neurological Institute's Healthy Brain Clinic in Phoenix, Arizona. Additionally, TF Health Corporation, operating as Breezing Co., plays a significant role in this partnership.

Key Contributions:

Smart Building (NSF PIRE Project)

The PIRE project aims to tackle the challenges of operating District Heating and Power (DHP) systems in a way that is efficient, human-centered, and socially justifiable to combat climate change with a focus on energy equity and resilience. This project aims at integrating AI techniques with an understanding of human needs and behaviors to enable an efficient, human-centered, resilient, and socially justifiable operation of district- and community-scale heat pumps systems that promote and support regional scale adoption of building decarbonization. This PIRE brings together a diverse, yet highly interdisciplinary and synergistic team led by Texas A&M University (TAMU), with Drexel University (DU), Arizona State University (ASU), Temple University (TU), KTH Royal Institute of Technology (KTH) and Blekinge Institute of Technology (BTH) in Sweden, and Aalborg University (AAU) in Denmark as partner institutions (Figure 2). 

Key Objectives:

Identifying Homogeneous Healthy Bio-Cores in Biomedical Multivariate Time Series 

Biomedical datasets, particularly those used for disease detection, often contain inherent heterogeneity, not only within disease cohorts but surprisingly also within supposedly homogeneous healthy cohorts due to inter-subject variability. To address this challenge, our project proposes the concept of a **"Healthy Bio-Core (HBC)"**, a novel framework designed to identify and select a homogeneous subset of healthy multivariate time series (MTS). By leveraging dynamic time warping (DTW), Gaussian distribution modeling, and ROCKET classification accuracy, the HBC method systematically extracts representative healthy instances, thereby enhancing the discriminative power of subsequent classification models. Our empirical analysis demonstrates that this targeted selection of homogeneous healthy subjects significantly improves classification performance across multiple biomedical datasets, emphasizing the potential of the HBC approach for improved precision in biomedical diagnostics and predictive analytics.