SURE Program

Engineering students have an opportunity to work closely with faculty and industry professionals on research projects during the academic year and/or summer. These projects help prepare students for graduate school and the workforce in engineering fields. Also, participating in research projects prepares students in critical thinking, teamwork, and hands-on experience in applying theoretical knowledge gained in the classroom to solve practical engineering problems. Students will be exposed to professional development, technical and academic seminars. SURE links undergraduate students with faculty and industry mentors, and introduces them to advanced research tools and databases at the frontier of engineering.

The SURE program is designed for engineering students that are in the level of Sophomore, Junior, and Senior year with a minimum cumulative GPA of 3.0/4.0. Students with GPAs lower than 3.0 are encouraged to apply and will be considered for conditional acceptance into the program. They must be enrolled during the semester, and available to work on projects for at least one semester. The selection criteria are based on academic records, a statement of purpose for the research project, and recommendations by the faculty member who will supervise the project.

Students participating in the program are required to

• setup research schedule and weekly meeting with the faculty advisor;
• submit an abstract, research plan, project schedule, milestones, and bi-weekly progress report to your faculty advisor and Department Chair;
• make a 10-15 minutes presentation of your research project and submit a PowerPoint presentation to Department Chair for the approval of the project;
• submit a poster presentation, at the end of the project, to UTPB’s student research day and/or other venues of publication.

First Annual Research Expo Award-Winning Participants

1. Density Functional Theory and Non-Equilibrium Green Function-based First-Principles Study on the Effect of Substitutions in Z-scheme Photocatalytic Materials: The case of TiO2 by Nolan Hines

Undergraduate Students

1. Analysis and Prediction of Produced Water Data using Machine Learning Algorithms by Raiel Amesquita

2. Open Loop Buck Converter and Microgrid HIL Real-Time Simulation by Jaqueline Obreque and Kandus Box

3. Anthromorphic Testbed Hand To Test Orthotic Hand Devices by Melany Azocar