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NSFGRADUATE INVOLVEMENT

NRT AI: Artificial intelligence for Changing Climate and Environmental SuStainability (ACCESS)

Active
Institution
Morgan State University, MD
Principal investigator
Samendra Sherchan
Funding
$3.0M
Duration
2023–2029
Open portfolio

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Abstract

Abstract Extreme events such as hurricanes, flooding, drought, heatwaves, wildfires are escalating around the U.S. Machine-learning (ML) based Artificial-Intelligence (AI) approaches have shown great promise in reducing and responding to contaminated aquatic ecosystems and contributing to the sustainability of water resources. However, research in this area is fractionated in various disciplines (computer science, data science, AI, geosciences, biological science, and engineering). This delays the progress towards a better understanding of the impacts of extreme events, contaminated ecosystems, and ML/AI solutions. This National Science Foundation Research Traineeship award to the Morgan State University will provide substantive, hands-on research experience for students who can tackle grand AI challenges using interdisciplinary methods. The traineeship will prepare 50 PhD students including 25 funded trainees from various fields (bioscience, computer science, civil engineering, mathematics, electrical and computer engineering) with technical, dynamic interdisciplinary and professional skills to responsibly solve grand challenges. The traineeship program places a strong emphasis on a convergence research approach to AI/ML, aquatic ecosystems, and sustainability. Major research efforts will focus on three areas: a) AI for water reuse; b) emerging contaminants and ML/AI prediction; and c) disease ecology, weather events, and AI. The program consists of an interdisciplinary team of chemists, biological scientists, computer scientists, engineers to focus on research, educational, and career development activities. Over a five-year period, the career and scientific activities will include: a) mentored research thesis; b) advanced experimental courses; c) a series of professional workshops on leadership, scientific ethic, and science communication skills; d) domestic internship opportunities; e) summer workshops on potential solutions; and f) international research internships. Trainees will work in teams to solve real-world AI challenges, and a seminar with invited distinguished speakers and professional development activities will all help to foster a friendly and collaborative inclusive positive learning atmosphere. The outcome will be a cohort of students with a strong multidisciplinary background and understanding of how AI can provide solutions for water use, aquatic contamination, and water quality management. The goal is to provide a rewarding opportunity for all trainees to conduct novel, hands-on citizen science research at Morgan State under the guidance of diverse faculty and postdocs. A second major goal is to increase the participation of students and stimulate their interest to pursue future careers in the STEM fields. 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.

Research team

  • Samendra SherchanPI
  • Chunlei FanCo-PI
  • Kofi NyarkoCo-PI
  • Md M RahmanCo-PI
  • Donghee KangCo-PI

LOADING MOONBASE SCORE

GRADUATE INVOLVEMENTDirectorate for STEM EducationNSF Research Traineeship (NRT)AI Education/Workforce DevelopSCIENCE, MATH, ENG & TECH EDUCATIONHIST BLACK COLLEGES AND UNIVextreme eventsgrand challengestraineeshipresearch traineeshipaquaticmorgannrtstem graduate

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