Individual Fellow Initiatives

The development of increasingly powerful computational resources has made computational competencies new core forms of literacy that should be formed as part of basic education across all STEM fields.

Biology continues to be seen as a field for science-interested, but math-disinterested students. However, due to the surge in data use in all biology careers, and the focus in research on sequence data, there is a strong need for data skill development in biology degree programs. Math anxiety poses a specific challenge for biology education. A suggestion to overcome this, is an early introduction to data skills in the two-course sequence of Introductory Biology.
 

Traditional science degree programs concentrate primarily on content and are not known for preparing their graduates with other skills needed for scientific careers.

The Peer Mentor Leadership Project (PMLP) empowers undergraduate students to explore and develop their leadership styles. Participants design and implement mentoring and leadership projects that align with their goals and values, while assessing the impact of their work. The program emphasizes introspection, reflection, and professional growth, helping students strengthen their resumes and leadership skills.

For some time now the humanities have been “in crisis,” but the crisis is becoming acute: majors in nearly all humanities fields have been sharply declining, enrollments are down, hiring of tenure-track faculty is down, and, at some colleges and universities across the United States, whole departments are being eliminated. Here at the University of Texas, majors that are growing seem to be ones that promise a literal return on investment (invest money in a degree and get that money back, in the form of a well-paying job upon graduation) or at least suggest an obvious and practical use.

The goal of this project is to train the next generation of environmental engineers in computing and statistical techniques to solve big data problems. Current undergraduate students in the Department of Civil, Architectural and Environmental Engineering have little to no exposure to computational and statistical methods for data analysis (e.g., big data collected from sensor networks). I proposed to integrate computational techniques in several courses throughout the Environmental Engineering Degree.