University of Texas at Austin

Computational Medicine Portfolio

Effective beginning Fall 2022

What is a Portfolio Program?

Graduate portfolio programs provide opportunities for enrolled graduate students to obtain transcriptable credentials in cross-disciplinary academic areas of inquiry while they are completing the requirements for a master’s or doctoral degree in a particular discipline. Portfolio programs promote cross-disciplinary scholarship and study by bringing together faculty and students from a variety of disciplines whose interests transcend boundaries of traditional academic disciplines. The Office of Graduate Studies provides an overview of the university’s general portfolio program requirements.

Computational Medicine Portfolio Overview

Computational Medicine is an emerging discipline that uses physics-based and data-driven advanced mathematical approaches to model complex systems across a spectrum of scales, from the molecular to cellular, to the organ to system levels of the human body, and even to the entire health care system. To accurately represent these complex systems, such modeling efforts need to capture the individuality of health and disease for accurate decision making at all levels, ranging from patient to policy. To do so requires substantial computational and mathematical skills, as well as detailed medical knowledge. The models can be theory-driven, knowledge-driven, or data-driven, or typically a novel combination of these.

The Computational Medicine Portfolio Program is intended for graduate students with strong mathematical and physical science backgrounds, but limited knowledge of biology and/or medicine, to pursue a program of study that will prepare them to interact knowledgably and collaborate productively with members of the medical community on interdisciplinary, cutting-edge research. It is important to realize that medicine itself is an enormous field, consisting of numerous subdisciplines, and Computational Medicine has become an important research direction in most of the larger subdisciplines. For these reasons, the Computational Medicine Portfolio will allow for the maximum flexibility to suit students whose interests reside within specific subdisciplines, which are currently Cardiovascular, Oncology and Neurology.


  • Must be a currently enrolled, degree-seeking graduate student at the University of Texas at Austin
  • Must be in good academic standing (minimum cumulative GPA of 3.0)


  • 4 courses (12 credit hours) taken from the list of Recommended Portfolio Courses (or approved alternatives)
  • At least 3 courses (9 hours) must be taken from at least 2 departments other than the student’s major
  • No more than 2 courses may be taken from the same department
  • No more than 1 Independent Study course or Internship course may be used to satisfy portfolio requirements

Application Information

  • Each student must apply and obtain approval from the Computational Medicine Portfolio Committee for their list of courses. Students may contact Michael Sacks ( to discuss course selections.
  • Student must have approval from their Graduate Advisor
  • Application form
  • Application deadlines
    • Aug 1 for Fall semester
    • Dec 1 for Spring semester

Recommended portfolio courses

General Topics
CSE 397.9 Mathematical Physiology
BME 383J.13 or CSE 397.7 Introduction to Mathematical and Physical Biology
BME 385J.6 Analysis of Biological Systems
Medical Imaging
BME 381J.3 or CSE 397.8 or ECE 385J.18 Biomedical Imaging: Signals/Systems
ECE 381V or CSE 397 Computational Magnetic Resonance Imaging
BME 385J or CSE 397.4 Computational Modeling of the Cardiovascular System
BME 384T.2 or M E 385J or CSE 397.5 Cellular, Tissue, and Scaffold Biomechanics
KIN 395.11 Pulmonary Exercise Physiology
KIN 395.16 Cardiovascular Response to Exercise
KIN 395.70 Human Cardiovascular and Autonomic Physiology
KIN 395.62 Aging and Cardiovascular Function and Disease Risk
BME 393J.3 or CSE 397.6 Introduction to Computational Oncology
BIO 394T Tumor Biology
PGS 384L Biochemical and Molecular Toxicology
PGS 388K Molecular Mechanisms/Methods in Nutrition and Cancer
NEU 394P or C S 395T Neural Computation
NEU 380E or PSY 380E Vision Systems
NEU 382T Principles of Neuroscience I
Medical Sciences
KIN 395 Human Anatomy and Dissection

Sample programs

Sample programs for graduate students with a limited background in biology or medicine will be added soon.

Background courses

The following undergraduate courses are recommended for students who wish to strengthen their background, but these courses cannot be counted as part of the Portfolio:

  • BIO 365S Human Systems Physiology
  • BME 365R Quantitative Engineering Physiology I


Professor Michael Sacks, Portfolio Program Chair,

Stephanie Rodriguez, Graduate Program Administrator,