Ph.D. Model Program: Applied Mathematics
The overall set of courses must constitute a coherent, rigorous program appropriate for a Ph.D. specifically in the field of Applied Mathematics, and the faculty recommend that students take Applied Math graduate courses to the greatest extent possible and relevant.
Listed here are examples of courses the Applied Math faculty have identified as appropriate for Ph.D. Program Plans in Applied Math. Note that the list is not exclusive, and each student’s individual plan requires review and approval by the CHD.
Examples of courses for students studying machine learning and artificial intelligence:

AM 216 Inverse Problems in Science and Engineering

AM 221 Advanced Optimization

CS 234r Topics on Computation in Networks and Crowds

CS 280r Advanced Topics in Artificial Intelligence

CS 281 Advanced Machine Learning

or CS 181 Machine Learning if more appropriate given the student’s background


ES 250 Information Theory

Other Computer Science or Engineering Sciences courses relevant to the student’s research
Examples of courses for students studying computational math, inference, and prediction

AM 207 Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization

AM 231 Decision Theory

AC 209a/b Data Science 1/2

CS 205 Computing Foundations for Computational Science

ES 255 Statistical Inference with Engineering Applications

200level Statistics courses appropriate for the student’s area of research
Examples of courses for students with an interest in physical modelling and applications:

AM 201/202 Physical Mathematics I/II

AM 203 Introduction to Disordered Systems and Stochastic Processes

AM 205 Advanced Scientific Computing: Numerical Methods

AM 207 Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization

AM 216 Inverse Problems in Science and Engineering

AM 225 Advanced Scientific Computing: Numerical Methods for Partial Differential Equations

AP 225 Introduction to Soft Matter, or other Applied Physics courses

ES 220 Fluid Dynamics

ES 240 Solid Mechanics

Other Applied Physics, ES or FAS technical courses relevant to the student’s research

Examples of Statistical Mechanics courses: AP 284, Physics 262

Examples of Electromagnetism courses: AP 216, Physics 232

Examples of Solid State Physics courses: AP 295a/b

Examples of courses for students with an interest in biological modelling and applications

AM 203 Introduction to Disordered Systems and Stochastic Processes

AM 205 Advanced Scientific Computing: Numerical Methods

AM 217 Instabilities and Patterns in Soft Matter and Biophysics

CS 289 Biologicallyinspired Multiagent Systems

Math 243 Evolutionary Dynamics

MCB 199 Statistical Thermodynamics and Quantitative Biology
Examples of courses for students with an interest in economics:

AM 207 Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization

CS 236r Topics at the Interface between Computer Science and Economics

Econ 2020a/b Microeconomic Theory I/II
Note that taking “Glevel” courses at MIT is certainly an option, as MIT offers a different course selection than is available at SEAS and Harvard. Examples of MIT courses taken by Applied Math PhD students include 2.29, 6.252J, 6.851, 8.334, 16.920, 18.1021,18.335J, 18.336.