National Science Foundation Awards Morgan State University $3 Million Research Traineeship Grant

The National Science Foundation (NSF) has recently bestowed a significant grant of nearly $3 million upon Morgan State University to establish the NSF Research Traineeship (NRT) program known as Artificial Intelligence for Climate Change and Environmental Sustainability (ACCESS). This pioneering initiative is aimed at equipping the next generation of scientists and engineers with the expertise needed to develop artificial intelligence and machine learning (AI/ML) solutions to combat the multifaceted challenges of climate change.

Funded over a span of five years, the ACCESS program will provide intensive training to approximately 50 Ph.D. students, including 25 individuals from diverse scientific backgrounds such as bioenvironmental science, mathematics education, engineering, and computer science. This program will prioritize the inclusion of underrepresented groups in STEM research, enabling them to gain a comprehensive, multidisciplinary understanding of how AI can play a pivotal role in addressing climate change, environmental pollution, and water quality management.

Dr. Samendra Sherchan, Ph.D., Associate Professor of Biology within the School of Computer, Mathematical and Natural Sciences (SCMNS) and Executive Director of the Center of Research Excellence in Wastewater-based Epidemiology and the Center for Climate Change and Health at Morgan State University, serves as the principal investigator (PI) for this transformative endeavor. Notably, since June 2020, Morgan has been the recipient of 84 awards from the National Science Foundation, totaling an impressive $30 million in research grants.

"The NRT ACCESS program advances convergence research by training graduate students at the intersection of climate change, health, and AI," stated Dr. Sherchan. "This grant will allow our students the opportunity to continue expanding ambitious research projects while also developing the necessary skills to pursue a range of STEM careers."

As the world grapples with the undeniable realities of climate change, including rising global temperatures and their negative impacts on society, the ACCESS program is poised to tackle these pressing issues head-on. Leveraging the latest advancements in AI and ML technologies, researchers within the program will explore innovative solutions and novel approaches to mitigate climate change impacts, which have the potential to disrupt various industries and negatively affect disadvantaged communities.

The ACCESS program emphasizes a convergence research approach to climate change and focuses on three major initiatives: AI for water reuse, emerging contaminants and ML/AI prediction, and disease ecology, climate change, and AI. Trainees will collaborate with a diverse team of faculty and postdoctoral instructors, including environmental chemists, environmental scientists, computer scientists, and engineers, to address real-world environmental challenges while engaging in professional development activities.

ACCESS will provide trainees with a rigorous research and learning experience, including advanced experimental courses, mentored research theses, professional leadership and ethics workshops, and a range of internship opportunities.

Contributors to the ACCESS program include Dr. Chunlei Fan, Professor of Biology; Dr. Md Rahman, Associate Professor and Associate Chair of Computer Science; Dr. Dong Hee Kang, Assistant Professor of Civil Engineering; and Dr. Kofi Nyarko, Professor in the Department of Electrical and Computer Engineering and Director of the Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS).

The National Science Foundation is an independent federal agency that provides approximately 25% of federal support to colleges and universities for research with the potential to yield advancements for the American people. The NSF Research Traineeship (NRT) Program aims to encourage the development of transformative models for STEM graduate education training.