A Multimodal and Progressive Data Science Training Program to Strengthen the Addiction Research Workforce

University Of Mississippi, University MS

Investigators

Abstract

The proposed training program aims to address the lack of specialized training in applying data science methods, including artificial intelligence (AI)/machine learning (ML), for addiction research at Mississippi’s major universities. The program is featured by its evidence-based, multimodal approach and extensive interdisciplinary collaboration. The design of a progressive learning pathway facilitates learning access for students from various backgrounds. A series of webinars will help raise campus-wide awareness and interest in the topic, laying the groundwork for the recruitment and participation of undergraduate and graduate students in a summer academy that provides hands-on training and subsequent mentored research experiences using data science methods for addiction research. Students participating in the summer academies and mentored research experiences will be encouraged to develop research projects suitable for academic conferences and/or journal submissions. The program will also establish a dedicated research lab to serve as a central hub for collaborative mentoring, program event announcements, and learning resources distribution. Furthermore, an e-learning platform will be used to broaden the program’s reach, and faculty members will work to integrate training content into course curricula to enhance both its impact and long-term sustainability. Successful implementation of the program will equip students to apply data science methods for addiction research, inspire pursuit of advanced degrees in the field, foster cross-disciplinary and institutional collaboration, and strengthen the field’s capacity to address complex addiction challenges.