PhD in Biomedical Data Science

The current explosion of biomedical data, including Electronic medical records (EHR), biomedical imaging, and genomics/proteomics/metabolomics, provide an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care. But fully harnessing the power of high-dimensional, heterogeneous data requires a new blend of skills including programming, data management, data analysis, and machine learning. The UW-Madison Department of Biostatistics and Medical Informatics has initiated a new PhD program in Biomedical Data Science, with the first class to be enrolled in Fall, 2018.

Program Curriculum

The program blends the best of statistics and computer sciences, biostatistics and biomedical informatics. It gives students the training they need to make sense of large-scale biomedical data, and to be scientific leaders in the team science that invariably accompanies such data. Unique features of the program include cross-training in computer science and biostatistics, and research rotations mentored by a program faculty member jointly with a scientific collaborator.

Core Topics

Three year-long course sequences (18 credits) will be selected from a set of core topics, including one biostatistics sequence (topics 1-3), and one computer science/informatics sequence (topics 4-7). The third sequence can be selected from any of the listed topics (topics 1-12).

Biostatistics Theory and Methods
Topics 1-3
Computer Science/Informatics
Topics 4-7
Topics 8-12
1. Statistical Theory
Mathematical Statistics
(STAT 609-610)
4. Machine Learning/AI
Intro to Artificial Intelligence
(CS 540)
Machine Learning
(CS 760)
8. Clinical Informatics
Health Systems Engineering
(ISyE 417)
Health Information Systems
(ISyE 617)
2. Biostatistical Methods
     Statistical Methods
(STAT 601-602)
     Regression Theory and Application
(STAT 849-850)
5. Database Systems
Database Management
(CS 564)
Database Management Topics
(CS 764)
9. Clinical Biostatistics
Clinical Trials Statistical Methods
(BMI 641)
Epidemiological Statistical Methods
(BMI 642)
3. Applied Biostatistics
     Data Science Practicum
(STAT 628)
     Data Visualization
(CS 765)


6. Optimization
Linear Program Methods
(CS 525)
Nonlinear Optimization
(CS 726)
10. Statistical Computing
Statistical Computing
(STAT 771)
Professional Skills for Data Science
(STAT 627)
7. Algorithms
Introduction to Algorithms
(CS 577)
Advanced Algorithms and Data Structures
(CS 787)
11. Bioinformatics/Statistical Genomics
Introduction to Bioinformatics
(BMI 576)
AND one of the following two
Advanced Bioinformatics
(CS 776)
Statistical Methods for Molecular Biology
(STAT 877)
12. Biomedical Image Analysis (2 of 3)
Computer Vision
(CS 766)
Comp. Methods for Medical Image Analysis
(BMI 767)
Stat. Methods for Medical Image Analysis
(BMI 768)

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Course requirments include additional credits of electives, which may be taken from the core topics (see above), or other graduate-level courses in biostatistics, computer science, or biomedical sciences. A students' particular choices will be guided by and subject to the approval of their Academi Advisor.

Biology Training and Breadth

Students will generally specialize in some field of biomedical application (e.g., clinical medicine, genomics, or neuroscience). Thus, their training must include coursework in the biological sciences. In addition, students will need to meet the formal breadth requirements set forth by the Graduate School. These objectives will be achieved by selection of a minor(formal external or distributed), with the further requirement that this minor include at least six credits of biology courses (e.g., Genetics 466 or Oncology 703).

Research Ethics Requirement

All students will take a 1-credit Research Ethics course, such as Nursing 802 (Ethics and Responsible Conduct of Research) or Oncology 675 (Appropriate Conduct in Science).

Additional Requirements

In addition, to contribute to the students' breadth of knowledge, to build cohesiveness among the students, to train the students in the critical evaluation of the biostatistical, computational, and scientific literature, and to build their professional skills, all students will participate in two year long seminar style courses:

  • Biodata Science Scholarly Literature (BMI 881-882, 4 credits): including readings, discussion, and presentations on a selected set of primary journal articles from the biostatistics, biomedical informatics, computer science, and biomedical literature.
  • Biodata Science Profession Skills (BMI 883-884, 2 credits): covering such topics as giving scientific presentations, writing research grants, the publication process (writing scientific articles, reviewing such articles, and responding to reviewers), applying for jobs, employment opportunities in academics and industry, and working with scientific collaborators as part of interdisciplinary teams.


Research Rotations

Students will carryout three semester- or summer-long research rotations (one in first year and two in the second year) concering a substantive problem in biomedical data science, advised by a Program Faculty member, in collaboration with an additional UW faculty member from the biological, biomedical, or population health sciences. The aim is for the students to begin to learn the craft of data science research, to expand their understanding of specific biomedical application areas, to gain a deeper exposure to a broad set of problems in biomedical data science, and to ultimately identify an appropriate advisor and to begin to identify a dissertation research topic.


The program will include an Oral Preliminary Exam, ideally taken in the student’s third year, on a topic selected with the approval of the student’s advisor. The examination is given by a committee of at least four faculty members, including at least three Program Faculty; a Program Faculty member must chair the committee. Prior to the exam, the student must prepare a 15–20-page paper outlining the area to be covered. The paper should indicate the aims, scope, and depth of the  student’s proposed dissertation research, as well as the anticipated approach, and should be submitted to the committee at least one week prior to the examination. The examination typically consists of a 20–30 min talk by the student and questions by the committee. The committee may ask questions during or after the talk. The scope of the questions will be determined by the subject matter of the paper but may include any relevant topic. The student’s advisor may not serve as Chair of the exam committee.


In addition, and in accordance with requirements set by the Graduate School at UW- Madison, students must pass a Final Oral Exam (i.e., a Dissertation Defense), following completion of their dissertation research. The primary requirement for the PhD degree is the completion of a significant body of original research and the presentation of this research in a dissertation. The research is carried out under the guidance of a member or members of the Program Faculty. The candidate must defend the dissertation in a Final Oral Exam. The rules for the composition of the Final Oral Exam committee are the same as for the Oral Preliminary Exam, except that, following Graduate School policy, the committee must have at least four members and at least one must be from outside the program.


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Criteria for evaluation will include:

  1. Academic record
  2. GRE scores
  3. Three letters of reference
  4. Personal statement
  5. Evidence of quantitative preparation, including at least two semesters of college calculus (similar to Math 221–222) and either a course in linear algebra (similar to Math 340) or courses in programming and data structures (similar to CS 302 and CS 367)
  6. For international students who are not native English speakers, TOEFL scoresof 93 or above are required

Applications will be accepted through the Graduate School and subject to the minimum requirements for applicants to the Graduate School.

In order to receive full consideration, your application must be completed by December 31.