POWER-UP REU for Undergraduate Research

POWER-UP REU at South Dakota State University provides undergraduates with immersive research experiences in energy and power systems across engineering, computer science, mathematics and statistics. The program emphasizes students from a broad range of educational and life experiences, including those attending small rural colleges, community colleges and tribal colleges, as well as individuals with nontraditional pathways, such as military service, through mentored, high-impact research aligned with the NSF's mission.

Program Dates and Application
  • Dates: May 26, 2026-July 31, 2026
  • Application Deadline: March 1, 2026, or until the position is filled
  • Email: sdsu.PowerUpREU@sdstate.edu
Application Submit Application

Program Benefits
  • Stipend: $7,000
  • Housing: $100 per week for a double room
  • Food: $130 per week
  • Travel support for conference publication
Eligibility
  • U.S. citizen or permanent resident
  • Enrolled undergraduate (associate or bachelor's)
  • Majoring in engineering, math, computer science or physics
  • Minimum GPA: 3.0
Faculty Mentors
  • Lead PI: Jeffrey Doom, Mechanical Engineering
  • Co-PI: Jung-Han Kimn, Mathematics
  • Mentors: Won, Puthawala, Letcher, Qi, Ghabchi and Larson

Research Projects

  1. Net-Zero Emissions in Scramjets: Hydrogen combustion and flameholder design for sustainable aerospace systems.
  2. ML for Climate-Energy Forecasting: Statistical models linking weather trends with microgrid energy usage.
  3. Vegetation Management in Storm Events: Finite element and LiDAR modeling of power poles and trees under wind loads.
  4. Perfect Spacings and Contrastive Learning: Pattern recognition and feature extraction in power systems using mathematical theory.
  5. Lunar Energy Infrastructure: Robotic deployment and maintenance of power systems on the moon.
  6. Physics-Constrained Graph Neural Networks: Hybrid AI-physics models for cyber-physical and energy systems resilience.
  7. Renewables Impact on Baseload Units: Analyzing emissions and operational cycling of thermal plants in high-RES environments.