Requirements
To complete the PhD in Imaging Science, students must do the following:
- Maintain an average grade of B (3.0 grade point average) for all 72 units (up to 24 graduate units may be transferred with approval)
- Complete courses with no more than one grade below B–
- Become integrated with a research group
- Pass a qualifying exam
- Complete one mentored teaching experience, including training
- Successfully defend a thesis proposal
- Complete four research presentations
- Present and successfully defend a dissertation
Courses
Required Core Courses (19 units)
| Code | Title | Units |
|---|---|---|
| BME 5700 | Mathematics of Imaging Science | 3 |
| ESE 5130 | Large-Scale Optimization for Data Science | 3 |
| or BME 5910 | Biomedical Optics I: Principles | |
| ESE 5200 | Probability and Stochastic Processes | 3 |
| ESE 5933 | Theoretical Imaging Science | 3 |
| ESE 5981 | Seminar in Imaging Science and Engineering | 1 |
| ESE 5970 | Practicum in Imaging Science | 3 |
| ESE 8992 | Introduction to the Research Process | 3 |
| Total Units | 19 | |
Elective Imaging Courses
Students choose electives from any of the following categories (at least 12 units):
- Computational Imaging & Theory
- Imaging Sensors & Instrumentation
- Image Formation & Imaging Physics
- Translational Biomedical Imaging
- Medical Physics
Typical Progression of Courses
| Course | Fall Units | Spring Units |
|---|---|---|
| First Year | ||
| Mathematics of Imaging Science (BME 5700) | 3 | — |
| Seminar in Imaging Science and Engineering (ESE 5981) | 1 | — |
| Elective | 3 | 3 |
| Introduction to the Research Process (ESE 8992) | 3 | — |
| Biological Imaging Technology (ESE 5890) | — | 3 |
| Machine learning elective | — | 3 |
| 10 | 9 | |
| Second Year | ||
| Large-Scale Optimization for Data Science (ESE 5130) | 3 | — |
| Theoretical Imaging Science (ESE 5933) | 3 | — |
| Doctoral research | 3 | — |
| Practicum in Imaging Science (ESE 5970) | — | 3 |
| Elective and/or doctoral research | — | 6 |
| 9 | 9 | |
Elective Options
Elective Courses — Computational Imaging & Theory
| Code | Title | Units |
|---|---|---|
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 5103 | Theory of Artificial Intelligence and Machine Learning | 3 |
| CSE 5105 | Bayesian Methods in Machine Learning | 3 |
| CSE 5107 | Machine Learning | 3 |
| CSE 5109 | Advanced Machine Learning | 3 |
| CSE 5403 | Algorithms for Nonlinear Optimization | 3 |
| CSE 5406 | Computational Geometry | 3 |
| CSE 5504 | Geometric Computing for Biomedicine | 3 |
| CSE 5509 | Computer Vision | 3 |
| CSE 5606 | High Performance Computer Systems | 3 |
| ESE 5230 | Information Theory | 3 |
| ESE 5240 | Detection and Estimation Theory | 3 |
Elective Courses — Image Formation & Imaging Physics
| Code | Title | Units |
|---|---|---|
| BME 5910 | Biomedical Optics I: Principles | 3 |
| BME 5940 | Ultrasound Imaging | 3 |
| BME 5XX | Imaging in Nuclear Medicine (to be developed) | |
| BME 5XX | Magnetic Resonance Imaging (to be developed) |
Approved Life Science Courses
| Code | Title | Units |
|---|---|---|
| BBS 3532 | Developmental Biology | 3 |
| BBS 5053 | Immunobiology I | 4 |
| BBS 5068 | Fundamentals of Molecular Cell Biology | 4 |
| BBS 5146 | Principles and Applications of Biological Imaging | 3 |
| BBS 5147 | Contrast Agents for Biological Imaging | 3 |
| BBS 5224 | Molecular, Cell and Organ Systems | 3 |
| BBS 5285 | Current Topics in Human and Mammalian Genetics | 3 |
| BBS 5319 | Molecular Foundations of Medicine | 3 |
| BBS 5480 | Nucleic Acids & Protein Biosynthesis | 3 |
| BBS 5488 | Genomics | 4 |
| BBS 5651 | Neural Systems | 6 |
| BBS 5663 | Neurobiology of Disease | 2 |
| BIOL 4040 | Laboratory of Neurophysiology | 4 |
| BME 5300 | Molecular Cell Biology for Engineers | 3 |
| BME 5380 | Cell Signal Transduction | 3 |
Approved Mathematics Courses
Any graduate-level course within the Department of Mathematics and Statistics is approved.
Qualifying Exam
The qualifying exam will be administered during the spring of the student's second year of graduate school. The examining committee, which will develop and grade the exam, will consist of three members of the Imaging Science PhD Program Committee. The director of the graduate program will approve the committee, the members of which will be suggested by the student and thesis advisor.
Finding a Thesis Research Mentor
Because the PhD is a research degree, the student is expected to become integrated within a research group. By the start of the first semester of study, students will be appointed a thesis advisor who will oversee their PhD research and assume financial responsibility for their stipend, tuition, health insurance and student fees. The thesis advisor must be a faculty member on the Imaging Science PhD Program Committee with the title of professor, associate professor or assistant professor.
Research Presentation/Thesis Proposal
Before the end of their third year, the student will give an oral presentation of their proposed PhD project — with the necessary background to support it — to the Research Advisory Committee. This committee must follow all guidelines for PhD degrees in the McKelvey School of Engineering and consists of five members (the dissertation research mentor plus four other members) with the following requirements:
- No more than three faculty members with primary appointment from any one department;
- Four of the members must be tenured or tenure-track faculty at Washington University;
- Three of the members must be imaging science program faculty members;
- If requested by the research mentor and approved by the co-directors, a sixth member may be added to the committee.
The committee will be chaired by the PhD mentor. At least two weeks prior to the presentation, the student will present a written document outlining the research background, proposed procedures, preliminary results and plans for completion. The required document will typically be between 15 and 30 pages in length, and it must contain a comprehensive bibliography.
The student will be placed on probation if they fail to pass their thesis proposal by the sixth semester. The student will be given a second opportunity to pass the exam during their seventh semester. If the student passes the second exam and meets the other program requirements (e.g., grades), they may continue the program without prejudice. If the student fails the exam a second time, they will be terminated from the PhD program.
After the thesis proposal, but prior to the dissertation defense, students must complete four additional research presentations, such as posters or talks at conferences, seminars, or other similar venues.
Dissertation
Upon completion of the dissertation, the doctoral candidate must work with the graduate program advisor to submit the PhD Dissertation Defense Committee approval and schedule the defense at least one month in advance. The candidate presents the dissertation in a public forum and successfully defends the dissertation before the Dissertation Defense Committee. The dissertation must be approved by the dissertation committee as part of the final examination. Students submit the PhD Dissertation Defense Approval form after their defense.
Contact Info
As part of their degree requirements, PhD students must complete a program-defined Mentored Experience Requirement (MER) as per these guidelines. The Mentored Experience Implementation Plan (MEIP) is the written articulation of a program-defined degree requirement for PhD students to engage in mentored teaching activities and/or mentored professional activities, collectively referred to as the MER.
Mentored Experience Requirement (MER)
Philosophy of Teaching
Imaging science is a discipline based on key underlying physical, experimental, statistical, algorithmic, and computational principles. The students in the program may pursue teaching opportunities in any of these supporting principles or in one of the imaging science courses that integrates them. By teaching these principles or their integration that leads to the design of imaging systems, students must learn the material in more depth than previously required. The process of communicating these ideas to others is a fundamental skill in any professional setting.
Preparatory Engagement
Preparatory Engagement activities are those that represent an introduction to the foundational skills associated with teaching or communication. Pedagogical preparation engagement activities are normally completed before students are permitted to engage in assisting or teaching in a classroom.
Preparatory Engagement activities are required:
- McKelvey MTE Preparatory Trainings
Mentored Teaching Experiences (MTEs)
Assistant in Instruction (AI)
An Assistant in Instruction (AI) is a PhD student who is directly engaged in the organization, instruction, and/or support of a semester-long course primarily taught by a faculty member. An AI receives mentorship from a faculty member related to best practices in classroom engagement, instruction in the field, interpersonal engagement, and other relevant skills. Students and mentors complete a mentorship plan prior to the start of each AI experience. To complete each AI assignment and to ensure that it applies toward their degree requirements, students must register for the appropriate course number for each semester of engagement. Refer to the "Required Pathways for Completion" section below for course numbers and details.
Students will complete an AI experience for one course at 10 MER units. Students work with their graduate supervisor on the timing and content of their AI assignments.
Required Pathways for Completion
Students work with their faculty mentor and their Director of Graduate Studies to plan how and when they will complete their MER. Students register during the normal registration period for courses in accordance with one of these approved pathways.
- Preparatory Engagement
| EGS 8010 | Take one time |