The current explosion of biomedical data, including Electronic Health Records, biomedical imaging, and genomics/proteomics/metabolomics, provide an opportunity to improve understanding of the mechanisms of disease and ultimately improve 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 program blends the best of statistics, 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 interdisciplinary training and research rotations mentored by program faculty.
Three year-long course sequences (18 credits) will be selected from a set of core topics, including one biostatistics sequence (topics 1-2), and one computer science/informatics sequence (topics 3-6). The third sequence can be selected from any of the listed topics (topics 1-11).
Course requirements 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 Academic 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).
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.
Students will carryout three semester- or summer-long research rotations in the first year (fall-spring-summer terms) concerning 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. Please see the BDS_PhD_Rotation Policy for details.
The PhD training includes an Oral Preliminary Exam, ideally taken in the student’s third year, on a topic selected with the approval of the student’s advisor. A student is expected to have completed nearly all other course requirements by this time. Please see BDS PhD Prelim Exam Guidelines for details.
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.
The highly collaborative faculty in Biostatistics and Medical Informatics offer training to undergraduate, graduate and other students through a variety of flexible mechanisms aimed at attracting the best and brightest students into our innovative fields of quantitative methodology for biomedical investigation, and at training biomedical scientists in the optimal application of our methods to their chosen field of study.
For undergraduates, our summer programs offer the opportunity to gain a foundation in biostatistics or biomedical informatics and to explore research in these areas. Other undergraduates pursue honors work or research rotations with our faculty, either informally or as part of their major.
For students wishing to pursue Master’s or PhD level graduate work in quantitative methodologies in biomedical science, we offer both a MS Degree Program in Biomedical Data Science and a Doctoral Degree Program in Biomedical Data Science.
Other opportunities to train with our faculty are available through the graduate programs in Statistics (Biostatistics Degree Option) and in Computer Sciences, but also through graduate programs in Clinical Investigation, Genetics, and Population Health Sciences.