Tarek Hamid

I am an Electrical and Computer Engineering PhD student at UVA, advised by Dr. Amanda Watson. Before starting my PhD, I held various roles at Sotera Digital Health, JPMorgan Chase, the Department of Defense, and Johnson & Johnson. I received my Master’s degree from Johns Hopkins University and my Bachelor’s degree from The College of New Jersey.

My research focuses on the detection of various physiological biomarkers using off-the-shelf wearable devices, including glucose, blood pressure, skin-tone, macronutrients, and lactate. My primary interests are in digital health, wearable devices, medical cyber-physical systems, and applied machine learning.

selected publications

UbiComp 2023

SpectraVue - An Interactive Web Application Enabling Rapid Data Visualization and Analysis for Wearable Spectroscopy Research

Tarek Hamid, Insup Lee, and Amanda Watson

In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, Cancun, Quintana Roo, Mexico, 2023

Abs PDF Video

Spectroscopic analysis of physiological phenomena has remained an important yet underutilized application in wearable technology today. Lumos has recently been introduced as an open-source wearable device capable of on-body spectroscopic research across the visible spectrum, enabling scientists and researchers to study the optical properties of various clinical biomarkers in real-time. However, a key limitation in the data output of this device is the lengthy process required to visualize and plot the spectral responses of observed mediums. In this paper, we present SpectraVue, an interactive web application that allows for visualization of Lumos spectral data. Utilizing a user-friendly interface, SpectraVue enables researchers to quickly generate three-dimensional plots from Lumos data stored in csv or text files, providing a comprehensive view of the spectral response of the medium under investigation. Additionally, SpectraVue offers features such as comparison of spectral data with a clinical biomarker, various data export options, and interactive plotting, further enhancing the user experience and researcher efficiency. The output graphs can be used to provide a standardization of spectral responses across a wide range of mediums, including characterization of these responses in clinical biomarkers such as glucose and alcohol. SpectraVue aims to facilitate these investigations by streamlining the data processing and visualization workflow, thereby accelerating clinical diagnostic research.