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Planning & Courses
Overview of planning and courses for applied mathematics (including suggested schedules)
Since students may specialize in a range of areas, the Applied Mathematics concentration is designed to be extremely flexible.
In addition, the structure encourages students to make the most of Harvard’s resources, such as taking courses in other departments, collaborating with researchers from other fields or schools, and participating in the wealth of extracurricular activities available on campus.
Your first mathematics course at Harvard is important for building your foundational knowledge in Applied Mathematics. For choosing that course, advising is available from Applied Mathematics Concentration Advisors, starting with advising fairs before the first week of the Fall and Spring semesters. Questions regarding placement should be directed to Concentration Advisors during office hours or advising fairs. In addition, an advising appointment can be arranged by writing to AM advising, with advising available to any Harvard College student. Mathematics placement is granted based on an appropriate Advanced Placement Examination, the Harvard Mathematics Placement Test, or equivalent college-level coursework taken elsewhere. Bypassing foundation courses must be validated by successful completion (honor grades) of more advanced courses. Students seeking placement based on college-level work done elsewhere will be asked to submit a petition, supplemented by suitable supporting materials.
Bachelor of Arts
The A.B. degree requires 14-15 courses.
SEAS offers undergraduate and graduate courses in Applied Mathematics. SEAS faculty also offer several courses in the section entitled Freshman Seminars, Extra-Departmental Courses, and House Seminars. Many additional courses of interest to applied mathematicians can be found in the Computer Science, Engineering Sciences, Mathematics, Economics, and Statistics sections of the catalog.
Below are some suggested paths for the freshman and sophomore years in the Applied Mathematics concentration. There are many possible pathways through the degree. Interested students should consult with the Directors, Associate Director, or Assistant Director of Undergraduate Studies for guidance.
Schedule M - Student has no advanced placement for mathematics.
|Year||Fall Courses||Spring Courses|
Sample plans of study from students who started in Ma are available from concentration advisors
Schedule 1 - Student has no advanced placement for mathematics.
|Year||Fall Courses||Spring Courses|
Sample plans of study from students who started in 1a are available from concentration advisors
Schedule 21a - Student places out of Math 1a and Math 1b.
|Year||Fall Courses||Spring Courses|
Schedule 21b - Student documents having taken AM 21a/Math 21a.
|Year||Fall Courses||Spring Courses|
Students in the Classes of 2019 or later must follow the new requirements below.
Two to five courses in calculus and linear algebra (see Notes, part d):
i. Mathematics Ma/Mb, Mathematics 1a
ii. Mathematics 1b
iii. Applied Mathematics 21a, Mathematics 21a, 23b, 25b, or 55b.
iv. Applied Mathematics 21b, Mathematics 21b, 23a, 25a, or 55a
Five to seven courses (see item 1.d.i, below) from the following categories. Students must take courses from at least 5 of the 8 categories listed below. Of those, students must take at least one course in Computation and one course in Probability and Statistics. In addition, students must take a course drawn from at least one “continuous” category (Differential Equations or Analysis) and one drawn from at least one “discrete” category (Algebra, Optimization, or Discrete Mathematics). Students must show evidence of satisfying prerequisites for a course to count towards the concentration.
a. Computation: First course: Applied Mathematics 111 and/or Computer Science 50.
Additional courses: Applied Mathematics 205, 207; Computer Science 51, 61, 109a, 109b, 181, 182, 205
b. Probability and Statistics: First course: either Statistics 110 or Mathematics 154, but not both.
Additional courses: Statistics 111, 139, 171, other courses above 110; Mathematics 117; Applied Mathematics 126
c. Differential Equations: Applied Mathematics 105, 108, 202; Mathematics 110
d. Analysis: Applied Mathematics 104, 201, 202; Mathematics 112, 113, 114, 115, 118r
e. Algebra: Linear Algebra: Applied Mathematics 120, Mathematics 121
Abstract Algebra: Applied Mathematics 106/206; Mathematics 122, 123, 124
f. Optimization: Applied Mathematics 121; Mathematics 116
g. Discrete Mathematics: Applied Mathematics 107; Mathematics 152, 155r; Computer Science 121, 124, 125
h. Modeling: Applied Mathematics 50, 91r, 115; Economics 985; or an approved advanced technical elective from outside of the student’s application area
Remarks: For AM/Ec students, we usually recommend real analysis (Math 112) and either AM121 or Math 116. The latter two classes cover optimization with different perspectives.
Five courses from an area of application in which mathematics has been substantively applied, selected to provide a coherent and cumulative introduction to mathematically-oriented aspects of the field. See Areas of Application for sample five-course plans.
i. Five Foundation courses and five Breadth courses are required for students starting in Mathematics M.
ii. Four Foundation and six Breadth courses are required for students starting in Mathematics 1a.
iii. Three foundation and six Breadth courses are required for students starting in Mathematics 1b.
iv. Two Foundation and seven Breadth courses are required for students starting in Applied Mathematics 21a, Mathematics 21a, 23a, 25a, or 55a. Students starting in 21a may take two foundation and seven breadth courses, or may choose to take Mathematics 101 (or 102) as a third Foundation course (if taken in the freshman or sophomore year). If choosing a third foundation course, these students are then required to take only six courses in the Breadth category.
Recommendations for honors are based on the grade point average of the final program of study, the rigor of the overall record, and the satisfaction of the honors modeling requirement. The Committee on Undergraduate Studies in Applied Mathematics votes the level of English honors to be recommended (Honors, High Honors, Highest Honors). To be eligible for English honors, all students must satisfy an honors modeling requirement, in which a paper is written where mathematical analysis is used to understand some aspect of the world around us. The honors modeling requirement can be satisfied in three ways:
- Writing a senior thesis, and turning it in, automatically satisfies the honors modeling requirement for English honors. However, it does not automatically satisfy the Breadth modeling section (v) of the plan of study. Most students who write senior theses register for one semester of Applied Mathematics 91r, which satisfies Breadth section (v) of the plan of study. Applied Mathematics 99r cannot be used for Breadth section (v) of the plan of study, since this course is not letter-graded.
- Taking the honors modeling course, Applied Mathematics 115. The last third of this class is spent working on an independent project. A grade of B- or above automatically satisfies the honors modeling requirement.
- A project, undertaken in AM 91r, in which a mathematical analysis of a problem is undertaken. Papers describing the project must be turned in to the concentration, via AM advising, for evaluation by the end of the final exam period in the semester in which the 91r is undertaken.
Recommendations for honors are based primarily on the grades and rigor of the courses in a student's final program of study, and the evaluation of the thesis.
Students who satisfy the modeling requirement without a thesis are eligible for Honors. The GPA cutoff for Honors is 3.5.
A thesis is required for High or Highest honors. There are no set GPA cutoffs for High and Highest honors. In any given year, final GPA cutoffs will depend on the rigor of the eligible students' programs of study. A student's level of honors will be determined by these cutoffs and the quality of their thesis.
Students with sufficient advanced placement credit to qualify for advanced standing may graduate with a bachelor’s and master’s degree in four years. Any student considering this option should discuss requirements with our Office of Academic Programs (Pierce 110) or with the Concentration Advisors.
The secondary field in Mathematical Sciences is jointly sponsored by the the Mathematics Department and the Applied Mathematics concentration. Students are required to take four courses in either Mathematics, Applied Mathematics, or Statistics of which at most two can be in Statistics. The Mathematics and Applied Mathematics courses must be numbered 104 or higher; Statistics courses must be numbered 110 or higher.
A thesis is a more ambitious undertaking than a project. A project that meets the honors modeling requirement (either through Applied Mathematics 115, 91r, or through other means) can be extended to a thesis with about one semester of work. See the above section on English Honors for details on how to satisfy the honors modeling requirement. Obviously the more time that is spent on the thesis the more substantial the outcome, but students are encouraged to write a thesis in whatever time they have. It is an invaluable academic experience.
The thesis should make substantive use of mathematical, statistical or computational modeling, though the level of sophistication will vary as appropriate to the particular problem context. It is expected that conscientious attention will be paid to the explanatory power of mathematical modeling of the phenomena under study, going beyond data analysis to elucidate questions of mechanism and causation rather than mere correlation. Models should be designed to yield both understanding and testable predictions. A thesis with a suitable modeling component will automatically satisfy the English honors modeling requirement; however a thesis won't satisfy modeling Breadth section (v) unless the student also takes Applied Mathematics 91r.
To write a thesis, student's typically enroll in Applied Mathematics 91r or 99r or both during their senior year. Applied Mathematics 99r is graded on a satisfactory/unsatisfactory basis. Usually concentrators will have completed their programs of study before beginning a thesis; but in those situations where this is necessary, students may take Applied Mathematics 91r for letter-graded credit, for inclusion in Breadth section (v) of the plan of study, with the thesis serving as the substantial paper on which the letter grade is based.
Students specializing in mathematical economics may substitute one of the Economics 985 thesis seminars for Applied Mathematics 99r. These seminars are full courses for letter-graded credit which involve additional activities beyond preparation of a thesis. They are open to Applied Mathematics concentrators with suitable background and interests.
Students wishing to enroll in Applied Mathematics 99r or 91r should bring the application available from the Office of Academic Programs (Pierce 110), signed by the thesis supervisor, to the Directors, Associate Director, or Assistant Director of Undergraduate Studies, who will sign the student’s study card.
Students often find a thesis supervisor at this time, and work with their supervisor to identify a thesis problem. Students may enroll in Econ 985 (strongly recommended when relevant), AM 91r, or AM 99r to block out space in their schedule for the thesis.
All fourth year concentrators are contacted by the Office of Academic Programs. Those planning to submit a senior thesis are requested to supply certain information. This is the first formal interaction with the concentration about the thesis.
A tentative thesis title approved by the thesis supervisor is required by the concentration.
The student should provide the name and contact information for a recommended second reader, together with assurance that this individual has agreed to serve. Thesis readers are expected to be teaching faculty members of the Faculty of Arts and Sciences or SEAS. Exceptions to this requirement must be first approved by the Directors, Associate Director, or Assistant Director of Undergraduate Studies. For students writing theses in mathematical economics, the second reader will be chosen by the Economics Department.
Thesis due at 4 pm. Electronic copies in PDF format should be delivered by the student to the two readers and to firstname.lastname@example.org (which will forward to the Directors of Undergraduate Studies, Associate and Assistant Director of Undergraduate Studies) on or before that date. An electronic copy should also be submitted via the SEAS online submission tool on or before that date. SEAS will keep this electronic copy as a non-circulating backup and will use it to print a physical copy of the thesis to be deposited in the Harvard University Archives. During this online submission process, the student will also have the option to make the electronic copy publicly available via DASH, Harvard’s open-access repository for scholarly work. More information can be found on the SEAS Senior Thesis Submission page.
Contemporaneously, the two readers will receive a rating sheet to be returned to the Office of Academic Programs before the beginning of the Reading Period, together with their copy of the thesis and any remarks to be transmitted to the student.
The student may pick up the readers' comments from the Office of Academic Programs in late May, after the degree meeting to decide honors recommendations.
The thesis is evaluated by two readers, whose roles are further delineated below. The first reader is the thesis adviser. The second and reader is recommended by the student and adviser, who should secure the agreement of the individual concerned to serve in this capacity. The reader must be approved by the Directors, Associate Director, or Assistant Director of Undergraduate Studies. The second reader is normally are teaching members of the Faculty of Arts and Sciences, but other faculty members or comparable professionals will usually be approved, after being apprised of the responsibilities they are assuming. For theses in mathematical economics, the choice of the second reader is made in cooperation with the Economics department. The student and thesis advsier will be notified of the designated second reader by mid-March.
The roles of the thesis adviser and of the outside reader are somewhat different. Ideally, the adviser is a collaborator and the outside reader is an informed critics. It is customary for the adviser's report to comment not only on the document itself but also on the background and context of the entire effort, elucidating the overall accomplishments of the student. The supervisor may choose to comment on a draft of the thesis before the final document is submitted, time permitting. The outside reader is being asked to evaluate the thesis actually produced, as a prospective scientific contribution — both as to content and presentation. The reader may choose to discuss their evaluation with the student, after the fact, should that prove to be mutually convenient.
The thesis should contain an informative abstract separate from the body of the thesis. At the degree meeting, the Committee on Undergraduate Studies in Applied Mathematics will review the thesis, the reports from the two readers and the student’s academic record. The readers (and student) are told to assume that the Committee consists of technical professionals who are not necessarily conversant with the subject matter of the thesis so their reports should reflect this audience.
The length of the thesis should be as long as it needs to be to make the arguments made, but no longer!
Thesis Examples (for download)
The most recent thesis examples across all of SEAS can be found on the Harvard DASH (Digital Access to Scholarship at Harvard) repository here.
Note: Additional samples of old theses can be found in McKay Library. Theses awarded Hoopes' Prizes can be found in Lamont Library.
Recent thesis titles
|Siddhant Agrawal||Does Integration of Bricks with Clicks Affect Online-Offline Price Dispersion?|
|Marianne Aguilar||Predicting Mood in College Students: Developing a Predictive Model from Multivariate Time Series|
|James Almgren-Bell||An Agent-Based Numerical Approach to Lenski’s Long Term Experiment|
|Dennis Bao||The Price Isn't Right: The Effect of Passive Ownership on Stock Price Informativeness|
|Jacob Bindman||Surveying Harvard Dining: An Architectural and Mathematical Assessment of
University Dining Services and Spaces
|Matthew Bouchard||Information and Market Efficiency: Evidence from the Major League Baseball Betting Market|
|Jacqueline Chea||Investigating the Causal Effects of Climate Change on Lyme Disease Incidence|
|Jiafeng Chen||Causal Inference in Matching Markets|
|Eryk Dobrushkin||A Game Theoretic Approach to the Israeli-Palestinian Conflict|
|Christopher Fenaroli||The Closed-End Fund Puzzle: New Evidence from Business Development Companies|
|Kabir Gandhi||Applied Linear Algebra and Big Data Course Book|
|Michael Giles||The 'Taxi Driver' Process and Passive Mobile Sensing on Networks|
|Brooke Istvan||From a Hashtag to a Movement: Modeling Conversation Around #MeToo on Twitter|
|Katrina Kraus||License to Exclude: The Concentrated Costs of Occupational Licensing|
|Joshua Kuppersmith||Geographic Clustering for Neighborhood Boundaries:
A Spatial Analysis of Chicago using Public Data
|Matthew Leifer||Don't Hate the Players, Hate the Game: Designing a Provably Trustworthy
Stock Market in the Age of High-Frequency Trading
|Lars Lorch||Constrained Bayesian Neural Networks|
|Manuel Medrano||Toward a Khipu Transcription “Insistence”: A Corpus-Based Study of the Textos Andinos|
|Amil Merchant||Fairness in Machine Learning
Methods for Correcting Black-Box Algorithms
|Lia Mondavi||A Fork's Impact: The Reach of Mission-Driven Fine Dining|
|Anant Pai||A Quantitative Analysis of Reproductive Rights and Right to Life
Advocacy Organization Mission Statements
|McKenzie Parks||Learning Strategies for Bidding in Online Advertisement Auctions with Noisy Feedback|
|Christina Qiu||Administrative Assistance and the Labor Integration of Migrant Roma
Informal Settlement Residents: Results from the MOUS Program in Grenoble
|Leah Rosen||Genome-Wide Analysis of NET-seq Data to Understand RNA Polymerase II Behaviour
Around Transcription Factor Binding Sites
|Dustin Swonder||Effects of Job Displacement on Prescription Opiate Demand:
Evidence from the Medical Expenditure Panel Survey
|Serena Tchorbajian||Determinants of Volatility: Calling it Quits on the VIX|
|Elias Tuomaala||The Bayesian Synthetic Control: A Probabilistic Framework for Counterfactual Estimation
in the Social Sciences
|Varun Varshney||Growing Pains
The Effect of Hospital Market Consolidation on Health Care Quality and Wages
|Katherine Wang||Subsidized Contraception and Teen Fertility: The Effects of Medicaid Family
Planning Program Eligibility Expansions on the Teen Birth Rate
and 12th Grade Dropout Rate
|Tomislav Žabčić-Matić||One Step Back, Two Steps Forward
A Machine Learning-Powered Options Trading Strategy
|Dario Zarrabian||Tip Nudging: A 3-Part Experimental Analysis of Influencing Tips
through Technological Default Menus
|FeiYang Zhu||Real-Time Scheduling for a Variable-Route Bus System
|Jerry Anunrojwong||Designing Prediction Markets with Informational Substitutes|
|Tallulah Axinn||An Electronic Health Record Interface for Clinical Use in Inpatient Settings|
|Nan Chen||Labour-market Impacts of Industrial Robots: Evidence from US occupations|
|Katherine Cohen||HIV Dynamics: Predicting Treatment Efficacy and Rebound|
|Neil Davey||The Impact of Medicaid Expansion on the Quality of Care in the United States|
|Jake Gober||Gaming the Blockchain: How Subversive Mining Strategies Can be Profitable|
|Alexander Goldberg||Differentially Private Inference over Exponential Random Graph Models|
|Lydia Goldberg||A Comparison of Sketch-Based Cardinality Estimation Algorithms|
|Jack Heavey||Weighting the Dice: A demographic based look at slot machine profits.|
|Benjamin Li||Automation-Driven Job Displacement and Subsequent Employment Outcomes: Individual-Level Evidence|
|Alexander Munoz||Markov-based model of cervicovaginal bacterial dynamics predicts community equilibrium states in young South African women|
|Akash Nandi||Money Illusion and Attitudes Toward Trade|
|Samuel Plank||Tweeting and Dialing: Quantifying Influence in Online and Offline Protest|
|Katherine Playfair||What is the role of social value on complex decision-making tasks?|
|Katherine Scott||Pointing Isn't Always Rude: Using Pointer Networks to Improve Word Prediction in a Language Model|
|Mirai Shah||Mumps at Harvard: Modeling the Spread of Disease on College Campuses|
|Paul Stainier||Adaptation in Corn Production Sensitivity to Extreme Weather Events: the Effect of Weather Calculation Procedures|
|Isabel Steinhaus||Testing for Racial Discrimination in Mortgage Lending via Mortgage Default Equations|
|Aron Szanto||Defuse the News: Predicting Information Veracity and Bias in Social Networks via Content-Blind Learning|
|Shahd Tagelsir||Fertilizer Supply Chain in Ethiopia|
|Johnny Tang||Wrong Turns and Right Tails: Identifying Fraudulent Detours in New York City Taxi Rides|
|Haruka Uchida||I'm Not Sexist, I Just Prefer My Companies to Be: An Experiment in Discrimination by Customers|
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|Kevin Xie||Urbanization and Social Networks: Evidence from a Development Context|
|Kelsey Young||The Role of Merit-Based Scholarships on Economic Mobility in the United States|
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