grad-biomedicaldata-phd

The current explosion of biomedical data provides an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care. However, fully harnessing the power of high-dimensional, heterogeneous data requires a new blend of skills including programming, data management, data analysis, and machine learning.

Blending the best of statistics and computer sciences, biostatistics and biomedical informatics, this program provides 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.

Potential students include both those with bachelor’s degrees in an area of data-science (e.g., computer science, statistics), as well as health professionals and clinicians (e.g., M.D.'s, Pharm.D.'s, R.N.'s). It is expected that admitted candidates will have demonstrated an aptitude for computer science and math, fundamental programming skills, knowledge of data structures and algorithms, and at least two semesters of college calculus. We will however consider candidates who have a wide range of undergraduate backgrounds; providing opportunities to develop necessary skills immediately upon entering the program.

Applying to the Program:

  • A formal online application with required fee through the UW–Madison Graduate School
  • Three letters of recommendation
  • Transcripts from each higher-education institution attended
  • A statement of purpose
  • GRE or MCAT scores
  • Applicants whose native language is not English, or whose undergraduate instruction was not in English, must provide an English proficiency test score (TOEFL, MELAB, or IELTS)
  • Evidence of quantitative preparation, including at least two semesters of college calculus (similar to MATH 221MATH 222) and either a course in linear algebra (similar to MATH 340) or courses in programming and data structures

Application Deadline: December 31

For additional information about admission to the program, see PhD Program in Biomedical Data Science on the department website.

Graduate School Admissions

Graduate admissions is a two-step process between academic degree programs and the Graduate School. Applicants must meet requirements of both the program(s) and the Graduate School. Once you have researched the graduate program(s) you are interested in, apply online.  

Graduate School Resources

Resources to help you afford graduate study might include assistantships, fellowships, traineeships, and financial aid. Further funding information is available from the Graduate School. Be sure to check with your program for individual policies and processes related to funding.

PROGRAM RESOURCES

The program is designed such that almost all students who are accepted to the program will receive guaranteed funding for five years. This funding may take a number of forms including, but not limited to training grants, teaching assistantships, and research assistantships. For more information about funding opportunities, see Graduate Assistantships.

Minimum Graduate School Requirements

Review the Graduate School minimum academic progress and degree requirements, in addition to the program requirements listed below.

Major Requirements

MODE OF INSTRUCTION

Face to Face Evening/Weekend Online Hybrid Accelerated
Yes No No No No

Mode of Instruction Definitions

CURRICULAR REQUIREMENTS

Minimum Credit Requirement 51 credits
Minimum Residence Credit Requirement 32 credits
Minimum Graduate Coursework Requirement Half of degree coursework (26 out of 51 total credits) must be completed in graduate‐level coursework; courses with the Graduate Level Coursework attribute are identified and searchable in the university's Course Guide.
Overall Graduate GPA Requirement 3.00 GPA required.
Other Grade Requirements Ph.D. candidates should maintain a 3.5 GPA in all core curriculum courses and may not have any more than two Incompletes on their record at any one time.
Assessments and Examinations Students must complete an Oral Preliminary Exam, ideally taken in the students’ third year.
Language Requirements No language requirements.
Doctoral Minor/Breadth Requirements All doctoral students are required to complete a minor.

Required Courses

CORE TOPICS

Three year-long course sequences (18 credits) will be selected from a set of core topics.

  1. A Biostatistics Theory and Methods sequence (topics 1–3)
  2. A Computer Science/Informatics sequence (topics 4–7)
  3. A sequence from any of the listed topics from Biostatistics Theory and Methods, Computer Science/Informatics, and the Specializations (topics 1– 12)

sequences in Biostatistics Theory and Methods (Topics 1–3)

Topic 1 : Biostatistics Theory and Methods—Mathematical Statistics AND Introduction to Statistical Inference

STAT 609
STAT 610
Mathematical Statistics I
and Introduction to Statistical Inference
7

Topic 2 : Biostatistical Methods—Statistical Methods Series OR Regression Theory and Application Series

STAT 601
STAT 602
Statistical Methods I
and Statistical Methods II
8
or
STAT 849
STAT 850
Theory and Application of Regression and Analysis of Variance I
and Theory and Application of Regression and Analysis of Variance II
6

Topic 3 : Applied Biostatistics—Data Science AND Data Visualization

STAT 628
COMP SCI 765
Data Science Practicum
and Data Visualization
6

sequences in Computer Science / informatics (topics 4–7)

Topic 4 : Machine Learning / AI—Intro to Artificial Intelligence AND Machine Learning

COMP SCI 540
COMP SCI 760
Introduction to Artificial Intelligence
and Machine Learning
6

Topic 5 : Database Systems—Database Management AND Database Management Topics

COMP SCI 564
COMP SCI 764
Database Management Systems: Design and Implementation
and Topics in Database Management Systems
7

Topic 6 : Optimization—Linear Program Methods AND Nonlinear Optimization

COMP SCI/​I SY E/​MATH/​STAT  525
COMP SCI/​I SY E/​MATH/​STAT  726
Linear Programming Methods
and Nonlinear Optimization I
6

Topic 7 : Algorithms—Introduction to Algorithms AND Advanced Algorithms and Data Structures

COMP SCI 577
COMP SCI 787
Introduction to Algorithms
and Advanced Algorithms
7

sequences in additional Specializations (topics 8-12)

Topic 8 : Clinical Informatics—Health Systems Engineering AND Health Information Systems

I SY E 417
I SY E/​B M I/​L I S  617
Health Systems Engineering
and Health Information Systems
6

Topic 9 : Clinical Biostatistics—Clinical Trials Statistical Methods AND Epidemiological Statistical Methods

B M I/​STAT  641
B M I/​STAT  642
Statistical Methods for Clinical Trials
and Statistical Methods for Epidemiology
6

Topic 10 : Statistical Computing—Statistical Computing AND Professional Skills for Data Science

STAT 771
STAT 627
Statistical Computing
and Professional Skills in Data Science
6

Topic 11 : Bioinformatics / Statistical Genomics—Introduction to Bioinformatics AND Advanced Bioinformatics OR Statistical Methods for Molecular Biology

B M I/​COMP SCI  576
COMP SCI/​B M I  776
Introduction to Bioinformatics
and Advanced Bioinformatics
6
or STAT/​B M I  877 Statistical Methods for Molecular Biology

Topic 12 : Biomedical Image Analysis (2 of 3)—Computer Vision OR Computer Methods for Medical Image Analysis OR Statistical Methods for Medical Image Analysis

COMP SCI 766 Computer Vision3
B M I/​COMP SCI  767 Computational Methods for Medical Image Analysis3
B M I/​STAT  768 Statistical Methods for Medical Image Analysis3

additional requirements

In consultation with their faculty advisor, students will select 6 credits of biology courses, 6 credits of elective courses, and a research ethics course (1 credit). Students will also complete: 

A second‐year literature seminar (4 credits) 

B M I 881 - Biomedical Data Science Scholarly Literature 1 Starting Fall 20192
B M I 882 - Biomedical Data Science Scholarly Literature 2 Starting Fall 20192

A third‐year professional skills seminar (2 credits)

B M I 883 - Biomedical Data Science Professional Skills 1 Starting Fall 20191
B M I 884 - Biomedical Data Science Professional Skills 2 Starting Fall 20191

Three semester‐long research rotations concerning a substantive problem in biomedical data science, advised by a program faculty member in collaboration with a UW faculty member from the biological, biomedical, or population health sciences.

B M I 899 Pre-dissertator Research3

Graduate School Policies

The Graduate School’s Academic Policies and Procedures provide essential information regarding general university policies. Program authority to set degree policies beyond the minimum required by the Graduate School lies with the degree program faculty. Policies set by the academic degree program can be found below.

Major-Specific Policies

Graduate Program Handbook

The Graduate Program Handbook is the repository for all of the program's policies and requirements.

Prior Coursework

Graduate Work from Other Institutions

With program approval, students are allowed to count no more than 9 credits of graduate course work from other institutions toward the graduate degree credit and graduate course work (50%) requirements. Course work earned ten years or more prior to admission to a doctoral degree is not allowed to satisfy requirements.

UW–Madison Undergraduate

For well‐prepared advanced students, a student’s program may decide to accept up to 7 credits numbered 300 or above of required or elective courses from the undergraduate work completed at UW–Madison toward fulfillment of minimum degree and minor credit requirements. However, this work would not be allowed to count toward the 50% graduate course work minimum unless taken at the 700 level or above. This work will not appear on the graduate career portion of UW–Madison transcript nor count toward the graduate career GPA. The Graduate School’s minimum graduate residence credit requirement can be satisfied only with courses taken as a graduate student at UW–Madison.

UW–Madison University Special

After admission to a graduate program, the student’s program may decide to accept up to fifteen University Special student credits as fulfillment of the minimum graduate residence, graduate degree, or minor credit requirements on occasion as an exception (on a case‐by‐case basis). In all these cases, the student would have to pay the difference in tuition for the terms in question. UW–Madison course work taken as a University Special student would not be allowed to count toward the 50% graduate course work minimum unless taken at the 700 level or above. This work will not appear on the graduate career portion of UW–Madison transcript nor count toward the graduate career GPA.

Probation

The status of a student can be one of three options:

  1. Good standing (progressing according to standards; any funding guarantee remains in place).
  2. Probation (not progressing according to standards but permitted to enroll; loss of funding guarantee; specific plan with dates and deadlines in place in regard to removal of probationary status).
  3. Unsatisfactory progress (not progressing according to standards; not permitted to enroll, dismissal, leave of absence or change of advisor or program).

ADVISOR / COMMITTEE

All students are required to conduct a yearly progress report meeting with their advisor, scheduled by December 17 and completed by April 30. Failure to do so will result in a hold being placed on the student’s registration.

CREDITS PER TERM ALLOWED

15 credits

Time Constraints

If students have been absent for five or more years, they must file a new Graduate School application for admission and submit it with a new application fee.

A candidate for a doctoral degree who fails to take the final oral examination and deposit the dissertation within five years after passing the preliminary examination may by require to take another preliminary examination and to be admitted to candidacy a second time.

Other

Funding guarantees are not provided for students in this program. Students are encouraged to explore funding options available across campus.

Graduate School Resources

Take advantage of the Graduate School's professional development resources to build skills, thrive academically, and launch your career. 

1. Articulate the biological context of a research question and the scientific relevance of analysis results.

2. Communicate with scientific and quantitative (computational and statistical) colleagues about data analysis goals, methods, and results.

3. Extract the statistical or computational problems from a scientific problem. Develop, characterize, and implement suitable analysis methods to answer questions from biomedical data. Evaluate the validity of analysis methods.

4. Analyze data; extract knowledge and guide decisions based on biomedical data. Organize data and software so that quantitative analyses are meaningful and reproducible.

5. Critically evaluate quantitative approaches in the scientific literature.

6. Evaluate and develop study designs and recognize limitations and potential biases in research data sets.

7. Identify the ethical and regulatory issues surrounding a research project.

8. As part of a biological, biomedical or population health investigative team, serve as the leader in the area of rigorous computational and statistical investigation.

Faculty: Broman, Buchanan, Burnside, Chappell, Chen, Chung, Craven, Dewey, Doan, Dyer, Elwert, Gangnon, Gianola, Gitter, Keles, Kendziorski, Kim, Lu, Mao, Mendonça, Mumford, Newton, Ong, Page, Palta, Patel, Peissig, Rathouz (Chair), Rosa, Rosenberg, Roy, Singh, Sorkness, Tang, Wahba, Yandell, Velten, Yu, Zhang, Zhu