TitleAssistant Vice President for Research Cyberinfrastructure
Office BuildingOld Horticulture
Mailing AddressOld Hort Bldg 206B
IT Research Computing-Box 2201
Brookings, SD 57007
BiographyOver fifteen years of leadership experience that includes enterprise network infrastructure, server systems, research cyberinfrastructure (computing, data flow, and storage) with a demonstrated performance of providing creative solutions within public higher education. Skilled in grant proposal writing/project coordination, personnel, and operations management.
1. “Cyberinfrastructure Facilitation Skills Training via the Virtual Residency Program | Practice and Experience in Advanced Research Computing.” Acm.org, 2020, dl.acm.org/doi/10.1145/3311790.3396629.
2. “Great Plains CyberTeam: A Regional Mentor Approach to Cyberinfrastructure Workforce Development and Advancement.” Aptaracorp.com, 2020, camps.aptaracorp.com/ACM_PMS/PMS/ACM/PEARC20/67/993df133-9f93-11ea-97aa-16dda94fa160/OUT/pearc20-67.html. Accessed 12 Jan. 2021.
3. Brandt, Kevin L. “Predictive Modeling of Sugarbeet Quality Using Vegetative Index, Statistical, and Artificial Neural Network Methods.” Open PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange, 2016, openprairie.sdstate.edu/etd/624/. Accessed 12 Jan. 2021.
Education1) South Dakota State University, Ph. D, Agricultural, Biosystems, and Mechanical Engineering Dissertation Research: Modeling Algal Bloom Risk in Fresh Water Lakes Using Artificial Intelligence Deep Learning Networks
Status: Projected completion date: May 2024.
Dissertation Abstract: South Dakota has a wide diversity of freshwater lakes used for many purposes, including water consumption and recreational activities. Many of these lakes are threatened in the summer months with harmful blue-green algal bloom conditions that are adversely affecting use. This impairment increases oxygen depletion within the water body which is needed to sustain aquatic organisms and is also responsible for the further proliferation of harmful algal bloom conditions (HABs) that make a lake toxic for recreational use. Economic impacts can be devastating due to the loss of revenue resulting from the lack of lake use as a drinking water source and for recreational purposes. Early algal bloom prediction could provide critical information for water resources managers, state and federal agencies to aid in early detection for planning, potential pollution mitigation activities, and for enacting state and federal water quality standards associated with point and nonpoint sources of pollution.
This study aims to determine the factors contributing to the growth of hazardous algal blooms in freshwater lakes, including human activities, environmental conditions, and characteristics of the lake and surrounding land. To achieve this, the study will use historical spectral data and critical factors that increase the growth of HABs to develop deep neural network prediction models. These models will attempt to predict/forecast algal bloom risk zones in several freshwater lakes in eastern South Dakota. The accuracy of the models will be evaluated by comparing predicted chlorophyll-a measurements to actual measurements collected from the lakes through statistical analysis.
2) South Dakota State University, MS- Ag and BioSystems Engineering
Research Thesis: Modeling of Sugarbeet Quality Using Vegetative Index, Statistical, and Artificial Neural Networks
2009, Activities and Societies: IEEE, ASABE
Scope: Five years of Landsat 5 and 7 multispectral data from over 1000 sugar beet fields.
• Tested different vegetative index approaches and investigated the potential statistical link between remote sensing canopy and sucrose concentration.
• Developed artificial neural network and conventional multiple linear regression models, and applied them to new data sets for whole-field sucrose
concentration prediction analysis.
• Examined the correlation of the artificial neural network and conventional multiple linear regression sucrose model prediction results with measured sucrose
concentration, assessing model performance over multiple site-years.
• Compared the effectiveness of multiple linear regression models to artificial neural network models for sucrose predictions.
3) South Dakota State University
BS Agricultural and BioSystems Engineering
2001 Activities and Societies: IEEE, ASABE, Alpha Epsilon, Order of the Engineer
Academic InterestsHigh Performance Computing
Remote sensing, image processing and machine vision
Methods: GIS, Systems Modeling & Integration, Spatial Analysis, Big Data
Committee ActivitiesUniversity Strategic Planning Committee
Northern Tier Network Consortium (NTNC) State Representative, Steering, and Program Committee Member.
Great Plains Network (GPN): Cyberinfrastructure Program, University Representative.
XSEDE Campus Champion Coordinator, Region 3 Lead (ND, SD, Minn, Wisc, Iowa, Illinois).
SDSU IT Committee
GrantsActive Funded Initiatives:
MRI: Acquisition of a Heterogeneous High-Performance Computing Cluster Driven by Computational and Data-Intensive Multidisciplinary Research (Role: Project PI)
Award Number:2216311; Principal Investigator: Kevin Brandt; Co-Principal Investigator: Anne Fennell, Larry Leigh, Joy Scaria, Timothy Hansen; Organization:South Dakota State University;NSF Organization:OAC Start Date:10/01/2022; Award Amount:$978,708.00;
RCN:CIP: A Connect.CI-based Community-Wide Mentorship Network (CCMnet) for the Advancement of Science and Engineering Research and Education (Role: Project PI)
Award Number:2227656; Principal Investigator:Kevin Brandt; Co-Principal Investigator:Laura Christopherson, Marisa Brazil, Torey Battelle, Vikram Gazula; Organization:South Dakota State University;NSF Organization:OAC Start Date:01/01/2023; Award Amount:$999,797.00;
CC* Team: Great Plains Regional CyberTeam (Role: Local PI, CyberTeam Project Lead)
Award Number:1925681; Principal Investigator:Grant Scott; Co-Principal Investigator:Douglas Jennewein, Daniel Andresen, Derek Weitzel, Carrie Brown, David Swanson, James Deaton, Bradley Spitzbart, George Louthan, Henry Neeman, Kevin Brandt; Organization:University of Missouri-Columbia;NSF Organization:OAC Start Date:07/01/2019; Award Amount:$1,399,479.00;
CC* Regional: SD-REDI: The South Dakota Research and Education Data Interchange (Role: Local PI)
Award Number:2201822; Principal Investigator:Liza Clark; Co-Principal Investigator:Alyssa Kiesow, Debbi Bumpous, Kevin Brandt, Ryan Johnson; Organization:South Dakota Board of Regents;NSF Organization:OAC Start Date:07/01/2022; Award Amount:$998,750.00;
Professional MembershipsActivities and Societies: ASABE, IEEE, ACM, Alpha Epsilon, Order of the Engineer
Creative Activities“Cyberinfrastructure Facilitation Skills Training via the Virtual Residency Program | Practice and Experience in Advanced Research Computing.” Acm.org, 2020, dl.acm.org/doi/10.1145/3311790.3396629.
“Great Plains CyberTeam: A Regional Mentor Approach to Cyberinfrastructure Workforce Development and Advancement.” Aptaracorp.com, 2020, camps.aptaracorp.com/ACM_PMS/PMS/ACM/PEARC20/67/993df133-9f93-11ea-97aa-16dda94fa160/OUT/pearc20-67.html. Accessed 12 Jan. 2021.
Area(s) of ResearchMachine Learning, Remote Sensing, Precision Agriculture
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Predictive Modeling of Sugarbeet Quality Using Vegetative Index, Statistical, a…
Open PRAIRIE - Public Research Access Institutional Repository and Information Exchange