LMI and UVA's Data Entrepreneurship Challenge: Embracing Innovation and Collaboration for the Social Good

LMI and the University of Virginia School of Data Science (UVA SDS) have a long partnership: collaborating on research, sponsored capstone projects, internships, and faculty engagements; speaking jointly at student-run organization events; teaming on government contracts; and, of course, actively hiring new graduates.

This year, we achieved a new milestone with the launch of our inaugural Data Entrepreneurship Challenge (DEC).

At its core, DEC acknowledges if users can’t access, trust, or understand AI solutions, no one will use it. This is especially true when solving societal challenges that involve influencing large populations and carry significant consequences if those solutions are not adopted. Competing teams have two months to develop solutions, culminating in “Shark Tank” like pitches to a panel of judges who score on innovation, feasibility, impact, and AI adoption.

Well aligned with LMI’s focus on health and communities, the theme for DEC 2023 focused on actionable health equity solutions coupled with adoption strategies focused on explainability and building trust. Successful health equity solutions can lead to profound impacts for health outcomes, societal stability, economic conditions, and the healthcare system itself.

While data challenges are common, we approached things a little differently with our UVA partner, resulting in engaging 14 registered teams involving nearly 40 students across 3 UVA Schools (data science, engineering, commerce). Driving this engagement and motivation for a learning experience that moves from data to end users, we:

• Incentivized diverse perspectives by messaging from all levels at UVA (University, School, student clubs) to engage the student body from many angles. We also had DEC embedded into courses (e.g., lab alternative) and involved faculty mentors.
• Made the topic real and relevant with government partners. We brought in senior leadership from the Office of Health Equity and the Office of Healthcare Innovation and Learning at the Department of Veterans Affairs. They presented the intersection of AI and health equity in context of the Veteran community.
• Capitalized on entrepreneurial excitement and expertise by having senior leadership from one of LMI’s investment firms participate as judges to give feedback and provide ongoing dedicated coaching to the winners as a unique incentive.
• Connected outcomes to user communities. DC Action, a nonprofit supporting underserved youth in D.C., committed to field testing the winning solution and providing validation and feedback to the winning team.

LMI and UVA SDS congratulate the top finishing teams!

Winning Teams

First place: Health Data Wizards 

Project title: Community Factors that Influence Life Expectancy

Team members: Sarrah Abdulali, Jayeesh Chennupati, Rishi Raghavan, and Peneeta Wojcik 

• The team developed a web-based tool trained on data from the U.S. census and the Supplemental Nutrition Assistance Program data system. The model predicts localized life-expectancy risk factors by zip code, producing recommendations for communities to resolve risks and evidence for policy makers for decision support.

Second place: BRIO 

Project title: Preemptive Screening of Diabetes Onset Risk

Team members:  Sai Chundi, Nithin Avala, Jonah Abraham, and Jay Lalwani 

• The team developed a model for predicting the onset of diabetes in low socioeconomic communities, targeting early awareness to empower informed healthcare decisions and automated recommendations for action.

Third place: Roomies 

Project title: Diagnosing Scoliosis in Children Through AI Detection

Team members: Ethan Hirai and Patrick Ho

• The team used X-ray imagery to predict scoliosis in pediatric cases at underserved hospitals, providing rapid diagnostic expertise where this resource might be limited or otherwise not available.

LMI is looking forward to continuing DEC next year, presenting new challenges to tomorrow’s data entrepreneurs.

The winning team, an interdisciplinary mix of biomedical engineering, economics, computer science, and medicine, said it best: “It was empowering to apply ourselves outside a classroom setting towards a meaningful, real-world problem of life expectancy and community initiatives to address it. In learning more about our topic, we discovered how powerful a health, business, and engineering oriented approach can be.”
 

“It was empowering to apply ourselves outside a classroom setting towards a meaningful, real-world problem of life expectancy and community initiatives to address it. In learning more about our topic, we discovered how powerful a health, business, and engineering oriented approach can be.”
 

Health Data Wizards