Education, Research & Training:
Graduate Curriculum in Computational Medicine
In the following we describe a graduate curriculum in Computational Medicine. This description applies to both MS and PhD graduate programs.
There are three tracks: i) Computational Anatomy & Medical Imaging; ii) Mathematical Bioinformatics; and iii) Modeling of Biological Systems. Computational Anatomy is the mathematical and computational discipline of how to describe anatomic shape and function, as measured using Medical Imaging techniques, and how to detect shape/function changes in image data that are characteristic of disease. It is a discipline which is poised to have a profound impact on many different areas of medicine by enabling discovery of anatomic markers that are highly predictive of disease. Mathematical Bioinformatics is a discipline that addresses the problem of how best to discover biomarkers in multi-scale biomedical data sets that are highly predictive of disease risk, presence/absence, disease sub-type and therapeutic approach. It provides the analytical approaches that will be the basis of the coming era of “personalized medicine”. Modeling of Biological Systems addresses the question of how to develop computer models of disease that can be used to understand disease mechanisms and to test “in silico” approaches for treating disease. Such models will provide a quantum leap forward in development of new therapies by enabling biomedical researchers to test novel approaches on the computer, select those that are most advantageous and that are predicted to pose the least risk, before testing these approaches in clinical trials.
Program Requirements
MS/Ph.D. students concentrating in Computational Medicine currently must matriculate through a degree-granting program in the JHU Whiting School of Engineering or the School of Medicine. Student may elect to pursue research with an ICM Core Faculty member having training privileges within that degree-granting program. Note that the Department of Applied Mathematics and Statistics offers a Ph.D. Area of Concentration (AOC - as defined by the Maryland Higher Educational Commission) in Computational Medicine.
Currently, the ICM Core faculty are:
- Patrick Barta, Research Associate Professor, Department of Biomedical Engineering
- Aravinda Chakravarti, Henry J Knott Professor and Director, Institute of Genetic Medicine
- Donald Geman, Professor, Department of Applied Mathematics and Statistics
- Joseph Greenstein, Research Assistant Professor, Department of Biomedical Engineering
- Rachel Karchin, Assistant Professor, Department of Biomedical Engineering
- Michael Miller, Herschel and Ruth Seder Professor of Biomedical Engineering
- Rajat Mittal, Professor, Department of Mechanical Engineering
- Tilak Ratnanather, Research Assistant Professor, Department of Biomedical Engineering
- Sridevi Sarma, Assistant Professor, Institute for Computational Medicine, Department of Biomedical Engineering
- Natalia Trayanova, Professor, Department of Biomedical Engineering
- Rene Vidal, Assistant Professor, Department of Biomedical Engineering
- Raimond Winslow, Professor, Department of Biomedical Engineering
- Laurent Younes, Professor, Department of Applied Mathematics and Statistics
Table 1 lists the graduate programs in which the ICM Core Faculty may train students.
Table 1: Ph.D. Programs in Which ICM Core Faculty Participate
Department or Program (click below) |
|||
x |
Geman, Younes |
Geman, Miller, Younes |
|
Greenstein, Sarma, Trayanova, Winslow |
Geman, Karchin, Winslow, Younes |
Barta, Geman, Miller, Ratnanather, Vidal, Younes |
|
Winslow |
Karchin, Winslow |
Vidal |
|
Sarma, Winslow |
Winslow |
Miller, Vidal |
|
x |
Chakravarti, Karchin |
x |
|
Mittal, Sarma |
x |
Vidal |
Students must meet all academic requirements of the “home” degree-granting program in addition to those of the computational medicine curriculum. 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 any advising requirements of the home department. Students will be expected to select a minimum of seven courses from the curriculum described below. We strongly encourage students to elect courses at the graduate level in order to meet MHEC course requirements for the MS and PhD degrees.
Common Engineering and Math Core (pick 2 or more courses):
- Dynamical Systems
- BME/ME/ECE: 580.616/530.616/520.601 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:
Computational Anatomy Core (choose at least 3 below) |
Mathematical Bioinformatics Core (choose at least 3 below) |
Modeling of Biological Systems Core (choose at least 3 below) |
110.439 Introduction to Differential Geometry (4.5 cr)
or 110.646 Riemannian Geometry (3 cr)
or 550.480 Shape and Geometry (3 cr) |
580.488/688, 600.488/688 Foundations of Computational Biology and Bioinformatics II (3 cr) |
530.671 Statistical Mechanics in Biological Systems (3 cr) |
550.635 Topics in Bioinformatics (3 cr) |
530.767 Computational Fluid Dynamics |
|
550.435 Bioinformatics and Statistical Genetics (3 cr) |
580.639 Modeling of Physiological Processes in the Neuron (4 cr) |
|
110.619-620 Lie Groups and Lie Algebras (3 cr) |
550.420 Introduction to Probability Theory (4 cr) |
580.682 Computational Modeling of the Cardiac Myocyte (3 cr) |
580.744 Pattern Theory: From Representation to Inference (3 cr) |
580.687 Foundations of Mathematical Bioinformatics (3 cr) |
580.7XX Modeling Approaches in Cardiac Arrhythmia Research (3 cr) |
Imaging (choose at least 2 course below): |
520/580.610 Computational Functional Genomics (3 cr) |
580.635 Bioelectromagnetic Phenomena (3 cr) |
600.461 Computer Vision (3 cr) |
580.691 Learning Theory I (3 cr) |
Quantitative Life Sciences (choose 2 or more below): |
580.464 Advanced Topics in Computer Vision (3 cr) |
Quantitative Life Sciences (choose 2 or more below): |
520.636 Feedback Control in Biological Signaling Pathways (3 cr) |
520.414 Image Processing and Analysis I (3 cr) |
020.630 Human Genetics (2 cr) |
EN 530.426 Biofluid Mechanics |
520.415 Image Processing and Analysis II (3 cr) |
020.639 Macromolecular Assemblies in Biology (3 cr) |
580.690 Systems Biology of Cell Regulation (3 cr) |
550.493 Mathematical Image Analysis (3 cr) |
020.642 Proteins: Structure, Folding and Interactions (3 cr) |
580.633 Calcium Signals in Biological Systems (3 cr) |
520.608 Image Reconstruction and Restoration (3 cr) |
020.676 Functional Interpretation of Biological Structure (3 cr) |
580.632 Ionic Channels in Excitable Membranes (3 cr) |
520.432/580.472 Medical Imaging Systems (3 cr) |
020.629 Integrated Signals and Biochemistry of Transcriptional Processes in Eukaryotes (2.5 cr) |
580.628 Topics in Systems Neuroscience (1 cr) |
520.746/600.746 Medical Image Analysis (3 cr) |
020.638 Regulation and Mechanisms of the Cell Cycle (2 cr) |
580.630 Theoretical Neuroscience (2 cr) |
580.748 Magnetic Resonance in Medicine (3 cr) |
250.685 Proteins and Nucleic Acids (3 cr) |
530.445 Introduction to Biomechanics (3 cr) |
580.636 Feedback Control in Biological Signaling Pathways (3 cr) |
580.448 Biomechanics: Cells and Organisms (3 cr) |
|
580.690 Systems Biology of Cell Regulation (3 cr) |
580.461 Biological Transport (3 cr) |
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)
Please email icm@jhu.edu for further details.
