Academics
Undergraduate Minor
The Institute for Computational Medicine is proud to offer an Undergraduate Minor in Computational Medicine, the first educational program in CM, reflecting Johns Hopkins University’s leadership in this field. Like the ICM itself, the Undergraduate Minor in Computational Medicine is integrative and multidisciplinary. The ICM Core Faculty who serve as advisors to the Undergraduate Minor in Computational Medicine hold primary and joint appointments in multiple Johns Hopkins University departments and schools including Biomedical Engineering, Computer Science, Electrical and Computer Engineering, Mechanical Engineering, Applied Mathematics and Statistics (WSE); Neurosurgery, Emergency Medicine, Medicine, and the Divisions of Cardiology and Health Sciences Informatics (SOM); and Health Policy and Management (BSPH).
Undergraduates who are interested in learning about the Minor in Computational Medicine are encouraged to attend the ICM’s annual Computational Medicine Night.
Undergraduates who would like to begin the minor declaration process should complete the CM Minor Prospective Students Form.
Click here to listen to a Question and Answer session regarding the minor.
Click here to view a PDF of a sample curricula by major.
What will I gain from a minor in CM?
With a minor in CM, students will have a solid grounding in the development and application of computational methods in multiple key areas of medicine. Specifically, they will understand how mathematical models can be constructed from biophysical laws or experimental data, and how predictions from these models facilitate diagnosis and treatment of a disease. Graduating students will be conversant with a wide variety of statistical, deterministic and stochastic modeling methods. They will be able to develop a model and to write code to implement it; they will be able to analyze and visualize the resulting data from the simulations. These skills are essential to the advancement of modern medicine, and are prized both in academic research and industrial research. The courses and research opportunities available in the CM minor will place students at the forefront of the application of mathematics, computing and engineering to human health. Whether you go on to medical school, graduate research, or biomedical industries, the comprehensive quantitative training and exposure to cutting edge CM techniques will give you a competitive advantage for working in the medicine of tomorrow – which will be data-driven, predictive, personalized and preventative.
Will I have opportunities to specialize within CM?
Yes. The minor will provide both foundational training and opportunities for specialization in Computational Medicine. Students can select electives from an approved list that match their interests. We also provide examples of curricula in some key subareas of Computational Medicine, including:
Computational Physiological Medicine develops mechanistic models of biological systems in disease, and applies the insights gained from these models to develop improved diagnostics and therapies. Therapies could be diverse drugs, electrical stimulation, mechanical support devices and more.
Computational Molecular Medicine harnesses the enormous amount of disease-relevant data produced by next-generation sequencing, microarray and proteomic experiments of large patient cohorts, using statistical models to identify the drivers of disease and the susceptible links in disease networks.
Computational Anatomical Medicine uses medical imaging to analyze the variation in structure of human organs in health and disease. Such image analysis has been integrated into clinical workflows to assist in the diagnosis and prognosis of complex diseases.
Computational Healthcare is an emerging field devoted to understanding populations of patients and their interaction with all aspects of the healthcare process.
Techniques for and applications in each of these four key subareas will be introduced in the required core courses, so that students will be exposed to the breadth of Computational Medicine, and will be able to identify preferred areas of interest.
Who can declare the computational medicine minor?
The minor is available to all Whiting School of Engineering (WSE), Krieger School of Arts and Sciences (KSAS), and School of Medicine (SOM) premedical undergraduates. Students should have sufficient mathematical and programming background and should plan to complete the minor prerequisites during their first two years of study in order to be prepared for the minor.
Number of Students Who Graduated with the Minor by Department
What are the prerequisites for the minor?
Before attempting the minor, undergraduates will have taken the following courses. For a course to count towards the minor, a minimum grade of C- is required. (Note: In general, courses graded as ‘S/U’ do not satisfy prerequisites, however exceptions will be made for courses taken during semesters in which all courses were graded S/U due to pandemic restrictions):
- Calculus I
- Calculus II
- Intermediate Probability and Statistics: either a single course covering both (EN.553.311, EN.560.348, or EN.540.382) or a course devoted to each (EN.553.420 and EN.553.430).
- At least one additional math or applied mathematics course (at least 3 credits)
- At least one of the following biological sciences course at the 200 level or higher (at least 3 credits):
| Biochemistry & Molecular Engineering (EN.580.221) |
| Biochemistry (AS.020.305) |
| Biochemistry I (AS.250.315/ AS.030.315) |
| The Nervous System (AS.080.305) |
| Biological Physics (AS.171.310) |
| Protein Engineering and Biochemistry Lab (AS.250.253) |
| Protein Biochemistry and Engineering Laboratory (AS.250.254) |
| Genetics (AS.020.303) |
6. At least one of the following computer programming course (at least 3 credits):
| Gateway Computing (EN.500.112, 113 or 114) |
| Computation & Programming for Materials Science & Engineering (EN.510.202) |
| Scientific Computing with Python (EN.553.383) |
| Scientific Computing: Linear Algebra (EN.553.385) |
| Scientific Computing: Differential Equations (EN.553.386) |
| Introduction to Scientific Computing for BME in Python, Matlab & R (EN.580.200) |
| Intermediate Programming (EN.601.120) |
| Intermediate Programming in Java (EN.601.107) |
| Data Structures (EN.601.226) |
What are the core classes needed for the minor?
1. Both of the following are required core courses for the minor and are usually completed junior or senior year:
| Course Num. | Course Name | Semester | Credits |
| EN.580.431 | Introduction to Computational Medicine: Imaging | 1st half of Fall | 2 |
| EN.580.433 | Introduction to Computational Medicine: The Physiome | 2nd half of Fall | 2 |
2. In addition to the courses listed above, one of the following is required:
| Course Num. | Course Name | Semester | Credits |
| AS.110.445 | Mathematical and Computational Foundations of Data Science | Spring | 4 |
| EN.553.450 | Computational Molecular Medicine | Spring | 4 |
| EN.580.430 | Systems Pharmacology & Personalized Medicine | Spring | 4 |
| EN.580.447 | Computational Stem Cell Biology | Spring | 3 |
| EN.580.458 | Computing the Transcriptome | Spring | 3 |
| EN.580.488 | Foundations of Computational Biology and Bioinformatics | Spring | 4 |
| PH.140.628 (71) & PH.140.629 (01) |
Data Science for Public Health I & II | Spring | 4 |
Am I required to attend ICM seminars?
Distinguished Seminar Series
In addition to the elective requirements, students with a declared Computational Medicine minor are REQUIRED to attend no less than 6 ICM Distinguished Seminars in person prior to graduation. Documentation of seminar attendance is two-fold: (1) Students must sign-in at every seminar attended and (2) students must complete the online Seminar Attendance Form. Please note that undergraduates do not need to register for the Distinguished Seminar Series in Computational Medicine course (EN.580.736/7) but do need to attend six ICM seminars and document their attendance to graduate with a Computational Medicine minor.
For the Fall 2021 semester, the seminar attendance requirement has been updated as follows:
ICM seminars will continue to be presented remotely via Zoom this semester. CM minors will be expected to ‘attend’ the live seminars scheduled for the Fall 2021 semester via Zoom. For students in time zones where viewing the seminar live is not feasible, the Fall 2021 seminars can be viewed as recorded. For all Fall 2021 seminars viewed, students must complete a seminar attendance form to receive credit towards the computational medicine minor.
More information on seminar speakers, dates, and topics can be found here.
What are the elective requirements for the minor?
Specific questions regarding the minor can be directed to [email protected].
MS Programs
BME MSE Computational Medicine (CM) Focus Area
The Department of Biomedical Engineering offers a Focus Area in Computational Medicine (BME MSE CM Focus Area) as part of its Masters of Science and Engineering program. The BME MSE Focus Area in Computational Medicine may be completed as a course-based degree program or a thesis-based degree program comprising two CM focus area courses, three CM focus area electives, and at least two electives outside the program. In addition to these 7 courses, students are also required to complete the Distinguished Seminar Series in Computational Medicine course. Required CM courses and focus area electives are taught by core faculty of the Institute for Computational Medicine.
PhD Programs
PhD students generally enter through the PhD programs of the home departments of ICM faculty. Representative departments are described below.
Department of Biomedical Engineering
The PhD program of the Department of Biomedical Engineering is usually ranked as the best BME program in the world. Admissions information is available here.
Department of Computer Science
The Department of Computer Science is one of the most rapidly growing departments within the Whiting School of Engineering. Admissions information is available here.
Department of Applied Math & Statistics
Area of Concentration in Computational Medicine
Computational Medicine is a discipline in which mechanistic models of disease are developed, personalized using patient data, and applied to improve disease diagnosis and treatment. The Department of Applied Mathematics and Statistics (AMS) offers a PhD Area of Concentration (AOC) in Computational Medicine.
The three tracks in this AOC are:
- Computational Anatomy is the mathematical and computational discipline of how to describe anatomic shape and function, as measured using medical imaging techniques and their variations in health and disease. It is a discipline which is poised to have a profound impact on many different areas of medicine by enabling discovery of anatomic biomarkers that are highly predictive of disease.
- Computational Molecular Medicine addresses the problems of how to identify the structure of molecular networks, how to model their dynamics, how network properties are altered in disease, and how this knowledge may be applied to improve disease diagnosis and treatment.
- Computational Physiological Medicine develops integrative models of disease that span multiple levels of biological organization. Models are used to understand disease mechanisms and test in silico approaches for treating disease. The ultimate goal is to personalize these models using patient data and then apply them to deliver improved healthcare.
The AMS AOC in Computational Medicine is directed by Dr. Donald Geman. Dr. Geman is Professor of Applied Mathematics and Statistics and is a Core Faculty member of the Institute for Computational Medicine.
Concentration Requirements
PhD students concentrating in Computational Medicine must matriculate through a degree-granting program at Johns Hopkins University. They must elect to pursue research with an ICM Core Faculty member having training privileges within that degree-granting program.
Students will need to meet all requirements of their “home” degree-granting program. Students will elect to pursue one of the three tracks described above, selecting an ICM Core Faculty member within that track as the research mentor (in addition to an advisor from the home program). Students will be expected to select a minimum of seven courses from the curriculum described below, thus meeting the Maryland Higher Education Commission (MHEC) requirement that an area of concentration provide at least 18 credit hours of focused class instruction at the PhD level, and pursue research with an ICM Core Faculty member leading to the PhD degree.
Concentration Training Faculty
- William S Anderson, Associate Professor, Dept. of Neurosurgery
- Siamak Ardekani, Assistant Research Professor, Dept. of Biomedical Engineering
- Joel Bader, Associate Professor, Dept. of Biomedical Engineering
- Patrick Barta, Research Associate Professor, Dept. of Biomedical Engineering
- Nicholas Charon, Assistant Professor, Dept. of Applied Mathematics & Statistics
- Joshua Epstein, Professor of Emergency Medicine, School of Medicine
- Donald Geman, Professor, Dept. of Applied Mathematics and Statistics
- Rachel Karchin, Assistant Professor, Dept. of Biomedical Engineering
- Feilim Mac Gabhann, Assistant Professor Biomedical Engineering
- Michael I. Miller, Hershel and Ruth Seder Professor, Dept. of Biomedical Engineering
- Rajat Mittal, Professor, Dept. of Mechanical Engineering
- Tilak Ratnanather, Associate Research Professor, Dept. of Biomedical Engineering
- Suchi Saria, Assistant Professor, Dept. of Computer Science
- Sridevi Sarma, Assistant Professor, Dept. of Biomedical Engineering
- Natalia Trayanova, Murray B. Sachs Professor, Dept. of Biomedical Engineering
- Rene Vidal, Assistant Professor, Dept. of Biomedical Engineering
- Joshua Vogelstein, Assistant Professor, Dept. of Biomedical Engineering
- Raimond Winslow, Raj and Neera Singh Professor, Dept. of Biomedical Engineering
- Laurent Younes, Professor and Chair, Dept. of Applied Mathematics and Statistics
Required Core Courses
- Introduction to Computational Medicine I (EN.580.631)
- Foundations of Computational Biology & Bioinformatics II (EN.580.688) or Computational Molecular Medicine (EN.553.650)
Common Engineering and Math Core
(pick 2 or more courses)
Dynamical Systems
520.601 Introduction to Linear Dynamical Systems (3 cr)
520.621 Introduction to Nonlinear Systems (3 cr)
Statistics and Machine Learning
550.430/630 Statistical Theory (choice depends on student qualifications) (4 cr)
550.437/640 Machine Learning (choice depends on student qualifications) (3 cr)
Computational Methods
530.766 Numerical Analysis (3 cr)
530.639 Scientific Computing (3 cr)
Research Area Core Requirements
Click here to view a PDF of the Research Area Core Requirements.
Electives
Electives (non-limiting list; electives may be chosen from courses offered by the JHU Schools of Medicine, Engineering, Public Health and Arts and Sciences with advisor approval)
Dynamical Systems
- 520.614 Linear System Theory (3 cr)
- 550.391 Dynamical Systems (3 cr)
- 580.651 Introduction to Nonlinear Dynamics in Physiology (3 cr)
Calculus of variations
- 520.630 Introduction to the Calculus of Variations and Optimal Control (3 cr)
- 110.427 Introduction to the Calculus of Variations (4 cr)
- 550.493 Mathematical Image Analysis (3 cr)
Statistics and Machine Learning
- 520.651 Random Signal Analysis (3 cr)
- 520.614 Introduction to Information Theory and Coding (3 cr)
- 550.426 Introduction to Stochastic Processes (4 cr)
- 550.436 Data Mining (4 cr)
- 580.466 Statistical Methods in Imaging (3 cr)
- 580.692 Learning Theory II (3 cr)
- 600.774 Kernel Machine Learning (3cr)
Quantitative Life Sciences
- 580.421 Systems Bioengineering I (Cardiovascular System) (4 cr)
- 580.422 Systems Bioengineering II (Nervous System) (4 cr)
- 580.423 Systems Bioengineering III (Systems Biology) (2 cr)