Kevin Brandt

Assistant Vice President for Research Cyberinfrastructure

Email 605-688-5265

Biography

Twenty years of technology leadership experience that includes enterprise network infrastructure, server systems and research cyberinfrastructure (computing, dataflow and storage) with a demonstrated performance in providing creative solutions within public higher education. Skilled in grant proposal writing/project coordination and multi-state and multi-institutional collaboration and personnel and operations management.

Education

Ph.D. in agricultural and biosystems engineering | South Dakota State University (In Progress)
- Dissertation Research: Modeling Algal Bloom Risk in Freshwater Lakes Using Artificial Intelligence Deep Learning Networks
- Dissertation Abstract: South Dakota has a wide variety of freshwater lakes used for many purposes, including water consumption and recreational activities. Many of these lakes are threatened in the summer months by harmful blue-green algal blooms that adversely affect use. This impairment increases oxygen depletion in the water body, which is essential for sustaining aquatic organisms. Also, it contributes to the further proliferation of harmful algal blooms (HABs), making the lake toxic for recreational use. Economic impacts can be devastating due to the loss of revenue from reduced use of the lake for drinking water and recreation. Early prediction of algal blooms could provide important information to water managers and government agencies, helping them detect problems early, plan ahead, reduce pollution and enforce water quality rules.
- This study aims to identify the factors contributing to the growth of hazardous algal blooms in freshwater lakes, including human activities, environmental conditions, and lake and surrounding land characteristics. To do this, the study will analyze historical data and key factors that drive the growth of harmful algal blooms 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. We will evaluate the models' accuracy by comparing predicted chlorophyll a measurements with actual measurements collected from the lakes using statistical analysis.
M.S. in agricultural and biosystems engineering | South Dakota State University
- Research Thesis: Modeling of Sugar Beet 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.
  - I examined the correlation between the prediction results of the artificial neural network's predictions and those of conventional multiple linear regression sucrose models. I compared the models' performance across multiple site-years.
  - The effectiveness of multiple linear regression models and artificial neural network models for sucrose predictions was compared.
B.S. in agricultural and biosystems engineering | South Dakota State University

Academic and Professional Experience

Academic Interests

High performance computing
Remote sensing, image processing and machine vision
Methods: GIS, systems modeling and integration, spatial analysis, big data

Committees and Professional Memberships

Committees:

University Strategic Planning Committee
Northern Tier Network Consortium (NTNC) State Representative, Steering and Program Committee Member
Great Plains Network (GPN): Cyberinfrastructure Program, University Representative
Access Campus Champion Coordinator, Region 3 Lead (North Dakota, South Dakota, Minnesota, Wisconsin, Iowa, Illinois)
SDSU IT Committee

Professional Memberships:

ASABE
IEEE
ACM
Alpha Epsilon
Order of the Engineer

Research and Scholarly Work

Areas of Research

Machine learning
Remote sensing
Precision agriculture

Grants

Active Funded Initiatives:

RCN:CIP: A Connect.CI-based Community-Wide Mentorship Network (CCMNet) for the Advancement of Science and Engineering Research and Education. (Role: Project PI)
1. Award Number: 2227656; Principal Investigator: Kevin Brandt; Co-principal investigator: Laura Christopherson, Marisa Brazil, Torey Battelle, Vikram Gazula.
2. Organization: South Dakota State University; NSF Organization: OAC Start Date: 1/01/2023; Award Amount: $999,797.
CC* Team: Great Plains Regional CyberTeam (Role: Local PI, CyberTeam Project Lead)
1. 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.
  1. Organization: University of Missouri-Columbia; NSF Organization: OAC Start Date: 7/01/2019; Award Amount: $1,399,479.00
CC* Regional: SD-REDI: The South Dakota Research and Education Data Interchange (Role: Local PI)
1. Award Number: 2201822; Principal Investigator: Liza Clark; Co-Principal Investigator: Alyssa Kiesow, Debbi Bumpous, Kevin Brandt, Ryan Johnson.
2. Organization: South Dakota Board of Regents; NSF Organization: OAC Start Date: 7/01/2022; Award Amount: $998,750.

Publications

Products

“ | Practice and Experience in Advanced Research Computing.” Acm.org, 2020, DOI: 10.1145/3311790.3396629
“.” Aptaracorp.com, 2020. Accessed Jan. 12, 2021
Brandt, Kevin L. “.” Open PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange, 2016. Accessed Jan. 12, 2021

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