This summer, four students will travel 7,000 miles from Kigali, Rwanda to Yale School of Medicine as part of a new international exchange program organized by the department of biomedical informatics and data science.
Biomedical Informatics and Data Science International Exchange for Students (BIDSIES) is a new academic exchange designed to foster innovation and collaboration across the globe, and mentor and support early career data scientists from under-represented communities. The program, directed by Annie Hartley, MD, PhD, MPH, assistant professor of biomedical informatics and data science, will support exchange student visits for the next three years. Each student will spend 80% of their time working on research with a faculty advisor at Yale School of Medicine, and 20% on a curriculum overseen by Hartley.
The BIDSIES curriculum will equip students with "data science diplomacy", providing training in scientific communication, leadership, and career strategies for academia, NGOs and industry. The program also builds a suite of open source pedagogical software that will help medical students critically evaluate artificial intelligence (AI) in clinical practice. The software will be integrated into an upcoming course at Yale School of Medicine, training students to detect and mitigate bias within a framework of AI ethics and regulations.
"It was only when I started teaching that I began to value the unique critical skillset that one develops when growing up and studying in a low resource setting," said Hartley, who grew up in South Africa and is trained as both a clinician and data scientist. "Through building pedagogical software, I hope the students will reflect on the unique value of their voice in the field of data science."
Four students attending Carnegie Mellon University Africa in Rwanda will form the inaugural cohort. One of these students is Scovia Achan, a masters student from Kampala, Uganda. She is interested in developing AI methods to address healthcare needs in Africa, and recently worked on a project that mapped cholera hotspots in developing countries.
“The project leveraged machine learning to get the best predictor variables and used k-means clustering to classify regions into low, medium and high-risk of getting cholera,” said Achan. At Yale, she hopes to deepen her understanding of data science further. “By learning from the distinguished faculty and engaging with fellow students, I aim to enhance my proficiency in conducting research and analyzing complex data sets.”