I will discuss our research (currently ongoing) into the impact of GenAI on legal education and the profession.

Making use of exploratory graph analysis (EGA) framework, we will summarize the changes over time in three key indicators: science identity, research self-efficacy, and academic self-concept.

We will discuss why this is a fundamental question in Data Science and how we connected the concept to educational experience of the next generation of Data Scientists.

In this work, we demonstrate OrgaTuring, an end-to-end vision AI approach that can locate, quantify, and classify human colon organoids with an accuracy of 90%.

We investigate the value of information through a randomized controlled trial on market design in the market for sanitation services in Burkina Faso.

LINflow is designed to automate LIN assignments to genomes using their data, i.e., nucleotide content, by considering one or more organism comparison measurements.

This is the story of the author's first introduction into technology for research in 1961 and then across the decades as she learns to compile data first in Pro-Cite (now defunct) and presently Endnote, with misadventures along the way.

This abstract describes a birds-of-a-feather session which focused on the development of accelerated pathways to good jobs in data science and artificial intelligence at community colleges.

Pearls in vision health research with special data science implications, as part of ADSA 2023 "Learning from data in complex and heterogeneous biological systems" session

Poster presentation for

The Center for TAIMing AI is a pilot center at the University of North Carolina at Charlotte

This study analyzes the effectiveness of Neural-ODEs in pharmacokinetics (PK) modeling by benchmarking ODEs against other state-of-the-art approaches for time-series modeling. The study also evaluates ODEs in noisy and missing data simulations to mimic real-world settings.

The Data Science by Design collective has self-published two anthologies of creative, data-inspired, and data-informed art and written work. The goals of these anthologies are to broaden the idea of what data is and what “doing” data science means.

Despite the expanding educational and employment opportunities in developed countries, many young adults aged 16-24 are neither enrolled in school nor employed—opportunity youth—in the U.S. This study explores some of the health implications of not addressing this disengagement.

Clinical trials generate a wealth of data. To maximize the utility of this data, it is critical that data be made FAIR (Findable, Accessible, Interoperable and Reusable) and that we have to tools necessary to properly analyze this data. 

Presented at the Data Science for Social Good panel

Information about the keynote speakers at the 2023 ADSA Annual Meeting

Introduction to the ADSA Annual Meeting along with a downloadable program book.