Interdisciplinary Graduate Training through Research in Artificial Intelligence and Secure Networked Sensing to Mitigate the Crisis of Alcohol and Drug Abuse

Alcohol and drug abuse are on the rise around the world, causing serious socioeconomic crises and consequences, such as accidents, homicides, suicides, physical and sexual assaults, or other problems such as anxiety and depression. Existing data for the treatment of alcohol and drug abuse are primarily collected through unreliable self-reports. Other methods are direct measurements on bodily fluids, breath, or sweat, which require penetration through the skin or physical maneuvering, and indirect estimates through interviews and observations.

Non-invasive sensing and testing paired with artificial intelligence (AI) techniques offer the promise of vastly improved data collection methods and analysis that in turn can improve treatments. However, the world, and the United States in particular, faces a shortage of trained workforce and innovators in this field. This NSF Research Traineeship (NRT) award will address these needs by preparing master’s and doctoral students to make convergent research contributions in the interdisciplinary fields of artificial intelligence (AI) and secure networked sensing to tackle the growing alcohol and drug crisis, and other social pandemics.

The project will provide a unique and comprehensive training opportunity for up to one hundred twenty graduate students, including twenty funded trainees, by combining the disciplines of AI, communication networks, cybersecurity, sensor technologies, interactions with healthcare and legal partners, and a culture of innovation and translational research that considers human factors, law, regulation, communication, and leadership.

This project examines the global and national alcohol/drug abuse crisis from a broad perspective, including technological, ethical, legal, and socioeconomic challenges and opportunities. It aims at making radical programmatic changes in the way traditional and non-traditional graduate students are trained. This crisis, which costs in work productivity, healthcare, and law enforcement expenses, requires interdisciplinary research to efficiently collect and assemble data, possibly from diverse sources, and analyze it to extract actionable information while being conscious of contextual factors, such as a consumer's health history, potential criminal record, personal background, and individual data security and privacy.

The data aspects of this societal problem pose significant challenges on its collection, e.g., non-invasive sensing, and its secure and privacy-aware data analytics, which are the main research lines of this proposal. The project's students will get practical and multidisciplinary training to prepare them as future skilled professionals in the rapidly growing area of applications of AI and secure networked sensing technologies.

The proposed model combines project-based learning techniques, multidisciplinary programs, ethics, civic engagement, leadership, and professional development and dissemination initiatives. The educational model structure will allow students from seven interdisciplinary doctoral and four master's programs to participate in the activities.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.