Project

The Mars Data Explorer aims to transcend traditional data presentation methods by integrating this geolocated, multi-faceted dataset atop an interactive map that traces the rover’s journey across the Martian landscape. Our platform significantly reduces the time from acquisition of raw data to its interactive visualization, enabling immediate insights and fostering a dynamic environment for scientific discovery. By leveraging cutting-edge data analytics techniques and sophisticated visualization tools, our project offers a comprehensive and intuitive exploration of the chemical and mineralogical composition of Mars’ surface. The interactive map not only contextualizes the spatial distribution of analyzed samples but also enables users to engage with the data in an immersive way. Users can select and zoom into specific sampling and analysis locations along Perseverance’s path, access detailed analyses from SHERLOC and PIXL, and visualize comparisons across datasets and samples for real-time data exploration Our approach emphasizes the combination of analytical rigor with dynamic visualizations for deeper data exploration and hypothesis testing in a visualization-enabled, group contribution environment. This process accelerates discovery, fosters cross-disciplinary collaborations, and serves as an example for ongoing and future astrobiology and planetary science missions. “Mars 2020 on the Campfire: An Interactive Data Visualization and Analysis Platform” project highlights the transformative power of data visualization and analytics and how the addition of these tools can change the dynamics of interdisciplinary collaboration in science.

Eat4Genes is a prototype diet recommendation web app for patients, healthcare providers, and researchers that aids in the selection of a healthy diet to help treat and prevent numerous health conditions. Our approach is focused on the strategic use of diet to regulate key risk gene expression, which we call dietary rational gene targeting (DRGT).

BlockIot is an interoperable, and decentralized way to connect medical IoT devices and health records systems systems.

This study examines how social determinants associated with COVID-19 mortality change over time. Using US county-level data from July 5 and December 28, 2020, the effect of 19 high-risk factors on COVID-19 mortality rate was quantified at each time point with negative binomial mixed models. Then, these high-risk factors were used as controls in two association studies between 40 social determinants and COVID-19 mortality rates using data from the same time points. The results indicate that counties with certain ethnic minorities and age groups, immigrants, prevalence of diseases like pediatric asthma and diabetes and cardiovascular disease, socioeconomic inequalities, and higher social association are associated with increased COVID-19 mortality rates.

HEALS Resources for Personal Health Knowledge Graph Construction

HEALS Semantic Technology Resources for Clinical Decision Support Recommendations

This is a research project to develop a trustworthy, accountable, data sharing eco-system for biomedical research that utilizes knowledge representation and blockchain technologies to address the challenge of the costly and time-consuming effort needed to bring a biomedical innovation from the bench (basic research) to bedside (clinical level).