REU Site: BIGDatA - Big Data Analytics for Cyber-Physical Systems

New Mexico State University, Las Cruces NM

Investigators

Abstract

NON-TECHNICAL SUMMARY This Research Experience for Undergraduate (REU) site will support NSF's mission to promote progress of science by introducing big data analytics in Cyber-physical Systems (CPS) to undergraduate students, helping advance the state of art, and preparing them for the future scientific workforce. The site's objectives include (i) motivating and solidifying students' interest in Computer Science (CS) in general, and big data analytics in particular; (ii) providing students with problem solving skills for conducting research in big data analytics and for presenting scientific findings verbally and in writing; (iii) equipping students with working knowledge of applying data management and data analytics techniques, by involving students in research projects related to different aspects of CPS; (iv) enhancing self-confidence and self-efficacy of participants, by creating a sense of belonging to a diverse community; and (v) broadening participation in computing, and contributing to diversify the computing workforce. The research outcome will advance the state of the art of big data analytics and push the research agenda of utilizing big data techniques for CPS. The REU site will reach undergraduate students beyond the New Mexico State University (NMSU) campus and train workforce for big data analytics in CPS. Special emphasis will be placed on recruiting underrepresented students nationwide. The participating students will be mentored by the site researchers to disseminate their research projects' findings via professional conferences and through the REU site website. TECHNICAL SUMMARY: The goal of this REU site initiative is to inspire and prepare undergraduate students to pursue careers in STEM with a focus on big data analytics for Cyber-physical Systems (CPS). PI's propose research projects to explore big data analytics for CPS in three intertwined layers: (L1) systems and architecture, (L2) models and algorithms, and (L3) visualization, spanning across four CPS application areas including smart grids, wireless sensor networks, smart homes for elderly and disabled, and disaster response. The planned student activities include: (i) team-based research activities on focused research projects, (ii) creation of cohorts, which will engage in training workshops, to develop research skills and to prepare for graduate schools, (iii) field trips to companies, and local and national labs to broaden students' research horizon, (iv) workshop and conference participation to present research findings, and (v) mentoring by faculty members and interaction with other student researchers. The proposed research outcome has the potential to advance the operation, protection, and utility of CPS. The research activities in the projects will equip student participants with skills and knowledge related to both big data analytics and CPS. The training workshops will help students develop research skills and their career paths. The field trips will broaden participants' understanding about data analytics research for CPS.