Pack Research Experience Program

The Pack Research Experience Program (PREP) is a research award that directly benefits historically underrepresented and first-generation students with an academic standing of freshman or sophomore. Get paid to work on a research project or creative activity under the guidance of a faculty mentor!

Benefits of participating:

  • Get paid while learning about research.
  • Apply what you learn in the classroom to real issues.
  • Develop skills and knowledge that can help you be a better student.
  • Build a supportive community around you that includes faculty, staff and fellow students.
  • Contribute to a team of researchers that are developing new knowledge.
  • Build experience to apply for other undergraduate research opportunities, the McNair Scholars Program or even graduate school.

Mentors and projects for spring 2024

Applications for these projects are due on November 20, 2023. Visit the PREP student information page for details.

Christopher Barile

Christopher Barile

Dynamic Windows based on reversible metal electrodeposition

Laura Blume

Laura Blume

The violence against public figures project

Shannon Burleson

Shannon Burleson

Investigating barriers and facilitators to the Indigenous population's access to secondary education

Gina Delgado

Gina Delgado

What is student success? Investigating and understanding undergraduate student success through self-efficacy and sense of belonging

Bob Ives

Bob Ives

Academic Integrity in Higher Education

Brad Johnson

Brad Johnson

Local government policy and the construction of public organizations

Dan Jones

Dan Jones

Do we hide who we really are during interviews? Self-presentation and personality

James Leonhardt

James Leonhardt

Psycholinguistics in marketing: Shaping consumer behavior

Lesley Morris

Lesley Morris

Historical Ecology in Rangelands (HEIR Lab)

Jenny Ouyang

Jenny Ouyang

Effects of artificial light at night on organismal function

Elnaz Esmaeilzadeh Seylabi

Elnaz Esmaeilzadeh Seylabi

Developing machine learning models for analyzing engineering systems