The objective of the department’s Master’s program is to produce graduates who achieve a mastery of some of the modern developments Applied Mathematics and Statistics.
The current University Catalog contains a detailed description of the department’s courses, programs, and requirements and a list of the current faculty and their interests. The purpose of this handbook is to present supplemental information; it should be read along with the departmental listing in the Catalog.
The Master of Science in Engineering (M.S.E.) in Applied Mathematics and Statistics ordinarily requires a minimum of two semesters of registration as a full-time resident graduate student. To obtain departmental certification for the master’s degree the student must:
1. Complete satisfactorily at least 8 one-semester courses of graduate work in a coherent program approved by the faculty advisor. All 600-level and 700-level courses (with the exception of seminar and research courses) are satisfactory for this requirement. Certain courses in other departments are also acceptable, and must be approved in advance. At most 3 courses outside the department may be counted toward the Master’s-degree requirements. WSE courses listed as 1- or 2-credit courses count only as one-half course. Approved KSAS graduate courses count as one-half course if the number of meeting hours per week is 1 or 2 and count as a full course otherwise.
2. Meet either of the following options:
(a) Submit an acceptable research report based on an approved project;
(b) Complete satisfactorily 2 additional one-semester graduate courses, as approved by the faculty advisor.
3. Satisfy the computing requirement (see Information for Computing Certification below).
4. Complete an area of focus by taking three courses in one of the following areas:
(a) Probability Theory.
(b) Statistics and Statistical Learning.
(c) Optimization and Operations Research.
(d) Computational and Applied Mathematics.
(e) Discrete Mathematics.
A list of courses that can be counted toward each area of focus will be maintained and updated every year by the department (see below for the current version). Some courses from other departments can be eligible to count toward area of focus. They can be used within the three-course limit specified in point 1) above.
5. Students in the AMS MSE program must pass one of the EN.553.801 seminar sections in at least one semester. (Students are encouraged to register in multiple semesters.)
6. Complete training on the responsible and ethical conduct of research. (Please see the WSE Policy on the Responsible Conduct of Research.)
7. Complete training on academic ethics.
An overall GPA of 3.0 must be maintained in courses used to meet the program requirements. At most two course grades of C or C+ are allowed to be used, and the rest of the course grades must be B- or better.
Substitutions and exceptions are permitted at the discretion of the Department Chair.
Each candidate for the master’s degree must submit for approval by the department a program stating how the degree requirements will be met. This should be done during the first semester of residence. Please note that any changes to this approved program will require new approvals. If you make changes, you will need to submit a revised form, which will need the signed approvals of your advisor, the Academic Affairs Committee, and the Chair. It should not be assumed that changes to your program will be approved during your final semester of study before requesting certification for the Master’s degree. Click here to access the form.
Students are evaluated at the end of each semester, and failure to make what is considered satisfactory progress in the program is grounds for being placed on Academic Probation or dismissal. The section of the handbook on Evaluation provides additional information.
Introduction to Stochastic Processes: 553.626;
Introduction to Stochastic Processes in Finance I, II: 553.627, 553.628;
Monte Carlo Methods: 553.633;
Introduction to Research in Discrete Probability: 553.629 (until Summer 2018 only);
Probability Theory I, II: 553.720, 553.721;
Introduction to Stochastic Calculus: 553.722;
Stochastic Search and Optimization: 553.763;
Models, Simulation and Monte Carlo: 553.764;
Mathematical and Computational Foundations of Data Science: 110.445.
Statistics and Statistical Learning:
Applied Statistics and Data Analysis: 553.613;
Applied Statistics and Data Analysis II: 553.614;
Intro to Statistical Learning, Data Analysis and Signal Processing: 553.616;
Mathematical Modeling: Statistical Learning: 553.617;
Introduction to Data Science: 553.636;
Statistical Theory I, II: 553.730, 553.731;
(Advanced) Bayesian Statistics: 553.632, 553.633;
Practical Scientific Analysis of Big Data: 550.415;
Nonparametric Statistics: 550.434;
Time Series Analysis: 553.639;
Computational Molecular Medicine: 553.650;
Distribution-free Statistics and Resampling Methods: 553.737;
Statistical Uncertainty Quantification: 553.782;
Machine Learning: 553.740;
Statistical Pattern Recognition Theory & Methods: 553.739;
Statistical Inference on Graphs: 553.742;
High Dimensional Approximation, Probability, and Statistical Learning: 553.738;
Mathematical and Computational Foundations of Data Science: 110.445;
Research and Design in Applied Mathematics: Data Mining: 553.602;
Topics in Statistical Pattern Recognition: 553.735.
Optimization and Operations Research:
Optimization in Finance: 553.661;
Network Models in Operations Research: 553.663;
Nonlinear Optimization I: 553.761;
Combinatorial Optimization: 553.766;
Mathematical Game Theory: 553.653;
Nonlinear Optimization II: 553.762;
Convex Optimization: 553.765;
Stochastic Search and Optimization: 553.763;
Introduction to Convexity: 553.665;
Introduction to Control Theory and Optimal Control: 553.797;
Mathematical Modeling and Consulting: 553.600;
Topics in Discrete Optimization: 553.769;
Deep Learning in Discrete Optimization: 553.667.
Computational and Applied Mathematics:
Shape and Differential Geometry: 553.780;
Applied Analysis: 550.491;
Mathematical Image Analysis: 553.693;
Numerical Analysis: 553.681/553.781;
Functional Analysis: 550.683;
Matrix Analysis: 553.792;
Turbulence Theory: 553.793;
Advanced Parameterization: 553.795;
Mathematical Biology: 553.692;
Mathematical Foundations of Computational Anatomy: 553.784;
Computing for Applied Mathematics: 553.688;
Mathematical and Computational Foundations of Data Science: 110.445.
At least one of:
Combinatorial Optimization: 553.766;
Combinatorial Analysis: 553.671;
Graph Theory: 553.672;
but the other two courses may include Introduction to Research in Discrete Probability: 553.629 (until Summer 2018 only) and the Computer Science offerings:
Algorithms I: 601.633;
Randomized Algorithms: 601.634;
Approximation Algorithms: 601.635;
Combinatorics & Graph Theory in Computer Science: 601.630;
Theory of Computation: 601.631;
Practical Cryptographic Systems: 601.645.
This list of courses is based on recent offerings. Not all classes are available every year, and substitute classes may be accepted if approved by the advisor and the Academic Affairs Committee.
IMPORTANCE OF COMPUTER LITERACY AND COMPUTING COMPETENCE
Familiarity with computing is essential to applied mathematics, and students should aim for practical problem-solving capability for computing in applied mathematics. Thus, every department graduate should possess a working knowledge of the utilization of computers and the fundamentals of scientific computing. This includes, but is not limited to, such topics as: computer programming (e.g., FORTRAN or C++), numerical software packages (e.g., MATLAB), symbolic computations (e.g., MAPLE), technical word processing (e.g., LaTeX), and professional presentation (e.g., PowerPoint).
The requirement below is a minimal one, aimed at ensuring that students demonstrate some ability at using the computer for problem-solving through homework assignments and projects.
It is expected that students discuss their plans to meet this requirement with their faculty advisors. As early as possible, students and advisors should agree on a program of work whose satisfactory completion would meet the computing requirement. Students with no previous background in computing should first acquire basic competence during their first year of residence, either by independent study, or by participation in an elementary course.
CERTIFICATION AND EVALUATION
It is recommended that students meet this requirement within their first year of residence.
Students meet this requirement typically by receiving a grade of B- or better in taking an approved AM&S department course. The list of approved courses together with the years in which versions of these courses can be used to meet the requirement can be found here.
The primary source of advice and counseling about a student’s progress is the faculty advisor.
When a student first enters the department, the student is assigned an academic advisor. The academic advisor assists the student in selecting courses and other administrative tasks, and provides general career guidance.
The department conducts semi-annual reviews of all graduate students, and notifies each student in writing of any concerns.
At the end of each academic semester, faculty instructors for each of a student’s courses (even those outside the department) are asked to complete a written evaluation of the student’s performance. If a student is a teaching assistant, the supervising faculty member is asked for a written performance evaluation also.
A full-time master’s student who fails, in a given semester, to receive a grade of B- or better in at least two courses in their master’s program will be placed on Academic Probation. For a full-time master’s student on Academic Probation, failure to pass at least two courses with a B- or better in their master’s program is grounds for dismissal. Also, in any given semester, whether or not a student is on Academic Probation, they may be dismissed if they do not receive any grades of B- or better in their master’s program.
The Department Seminar meets weekly for the presentation and discussion of current research work. Both University scholars and invited guests appear, and a wide variety of topics is covered in the course of a year.
The department sometimes hires Master’s students as temporary TAs or office assistants, with the level of salary dependent on the duties required and qualifications of the student. Students may apply for these positions after having completed at least one semester at JHU. Temporary TA salaries are generally lower than the standard TA salary for similar duties (since the standard TA salary is viewed as primarily financial aid rather than payment for duties).
All students serving as TAs are expected to make appropriate efforts to improve their language and communication skills, which may require participating in training and improvement courses offered by the university.
The Milton S. Eisenhower Library has extensive resources of books and journals and can draw freely on large nearby collections such as the Library of Congress. The department maintains a small reference library of books and journals. Resident full‑time Master’s candidates receive access to shared spaces in Whitehead Hall. The facilities of the University Computing Center are available to all students for research and instruction. The Department also has high‑capability computing resources for graduate students and faculty research in its quarters in Whitehead Hall.
The computing resources of the Johns Hopkins University Department of Applied Mathematics and Statistics exist to meet the computing needs of the Department’s faculty and graduate students for the purposes of instruction and research, and to provide access to relevant information and services of the Internet computer network.
We distinguish between operation (otherwise known as system management or administration) and administrative oversight. Operation is considered to consist of those tasks related to the maintenance of software, hardware, user accounts, and any other tasks which directly affect the status and performance of the physical resources. Administrative oversight will consist of the establishment and maintenance of software licenses, procurement of equipment, establishment of policies, and all other tasks not related to operation.
The computing resources of the Department are to be operated by one or more designated graduate Computer Assistants, possibly in collaboration with members of the faculty. The administrative oversight of the computing resources is the responsibility of the Departmental Research and Facilities Committee.
The computing resources of the Department will be available to those who are eligible for accounts on the Departmental server(s), as described below, for tasks consistent with the conditions of usage described under Acceptable Usage below.
All faculty, staff, and graduate students of the Department are eligible for computer accounts on the Departmental server(s). Accounts for faculty and graduate student users will be maintained through the end of the first full semester after the user’s departure. Accounts for staff members will be maintained at the discretion of the Research and Facilities Committee after the staff member’s departure.
Undergraduate students, students from other departments, and former students from the Department of Applied Mathematics and Statistics, or any other person not specifically eligible as per above may obtain accounts or extend accounts past the normal termination date, as follows. The requester must have the sponsorship of a member of the Departmental faculty. The sponsoring faculty member should present the request to the Research and Facilities Committee and Department Chair for approval, whereupon the operator(s) will be instructed to create or maintain the account as specified.
If resource quotas are enforced, those users with accounts under special dispensation as described above may be required to observe resource limits different from those of eligible Departmental users.
The Department Chair is ultimately responsible for all decisions to create or terminate accounts on the Department’s server(s). The Research and Facilities Committee recommends actions on these matters to the Department Chair. Users will be notified by e‑mail two weeks prior to termination, in order to have time to properly clean out the account.
The users of the Department’s computing resources are responsible for adhering to all Johns Hopkins usage requirements, as well as all local, state, federal, and international laws.
Users are expected to observe the following:
- Accounts are issued on a person-by-person basis. Allowing anyone else to access an account is therefore prohibited, and repeated offenses may result in termination of the user’s account.
- Accounts accessing, altering, duplicating, or deleting any data belonging to other users without that user’s consent is prohibited. This includes, but is not limited to, so affecting system data files maintained by the Operator(s), such as system quota databases or password files.
- Any unauthorized and deliberate action which results in the damage or disruption of a computer system is a violation of Acceptable Usage, regardless of the location of the affected system.
Publication (mail and Web usage)
- Any attempt to forge the source or sender of any network traffic, including but not limited to electronic mail, Web documents, or USENET articles, is prohibited.
- Any attempt to read, write, alter, or delete another user’s electronic mail, USENET articles, etc., is prohibited.
- Sending harassing, obscene, or threatening electronic mail, USENET articles, etc., is prohibited. This includes the sending of electronic “chain letters”.
- Using network or computer facilities for any commercial or for‑profit purpose is prohibited. This
includes, but is not limited to, e‑mail advertisements, professional correspondence that is not
Department-related, and the inclusion of commercial advertising on Web pages.
- Attempt to gain unauthorized access to any computers or networks (Departmental or otherwise) is forbidden.
- Copying of copyrighted materials without permission is not allowed.
The reasons for these restrictions should be clear. Further information and suggestions are available online. Interested users may, for example, consult the Netiquette guide on the World Wide Web.