Computer Science

Faculty Mentor: Dr. Bernie Daigle, Jr.

Faculty Mentor's Department: Biological Sciences


Project Title: Identifying Prognostic Biomarkers for Posttraumatic Stress Disorder

Project Description: Posttraumatic stress disorder (PTSD) is the fifth most common psychiatric disorder, with an occurrence rate of approximately 8% in the United States. Left untreated, PTSD can be life-threatening, as it is often linked to substance abuse and severe depression. Thus, there is a pressing need to identify reliable molecular and physiological biomarkers of PTSD for the accurate diagnosis, prognosis, and treatment of the disorder. The Department of Defense-funded Systems Biology of PTSD Consortium has collected blood samples and demographic/clinical data from over 200 male combat veterans with and without PTSD for the purposes of identifying these biomarkers. Recently, a subset of these veterans has been reassessed at an additional time point 1-2 years after the initial assay. The goal of this project is to use data from both the original and follow-up time points to identify candidate biomarkers for PTSD prognosis—i.e., whether or not a currently affected individual will eventually recover, and whether or not a currently unaffected individual will eventually develop PTSD. Statistical and machine learning tools will be applied to clinical and molecular data to identify candidate biomarkers predictive of a change in PTSD status or severity over time. Knowledge of these markers will contribute to an improved understanding of the biological mechanisms underlying PTSD progression and recovery.

Requirements for Student Applicants: Through the U.S. Army Research Office Undergraduate Research Apprenticeship Program (URAP), the Daigle Lab has funding for one student researcher to work on the above project in summer 2018. Candidates should currently be enrolled in their second or third year at the University of Memphis in a degree program within the Departments of Biological Sciences, Computer Science, or Biomedical Engineering. Desired qualifications include GPA >3.5 and some prior computer programming experience. Applicants must provide a current CV, at least one letter of recommendation, and a one page personal statement describing academic preparation, prior research experience, and future career goals. Applications must be submitted online by February 28, 2018 through the following webpage:

Starting Date and Duration: The position will begin on a mutually agreed upon date in June 2018 and continue for up to 10 weeks.

Method of Compensation: The selected student will receive $15/hour for up to 300 total hours of summer research.

Methods of Compensation: Federal Work Study or Volunteer

Federal Work Study eligible students should submit application materials directly to by Friday, January 26, 2018. Other Interested students should submit materials directly to the faculty mentor.


Faculty Mentor: Lan Wang

Department: Computer Science,

Telephone Number and/or E-mail Address: 901-678-2727,

Project Description: Students will work on developing Named Data Networking (, a future internet architecture that is much more secure and efficient than the current internet.

Requirements for Student Applicants: Open only to undergraduate students with a GPA of 3.5 and above in the Computer Science Department. The student must have obtained an A from both COMP 1900 and COMP 2150, and the student must be able to work for at least one year.

Application or Interview Process: Students must submit a resume, unofficial transcript and 2 letters of reference from computer science faculty members (letters must be directly emailed to Professor Wang from the other faculty members).

Hours per week: The student is expected to work up to 20 hours per week during semesters and up to 40 hours per week during the summer. 

Starting Date: Immediately

Method of Compensation: Volunteer or Federal Work Study

Federal Work Study eligible students should submit application materials directly to by Friday, January 26, 2018. Other Interested students should submit materials directly to the faculty mentor.