Princeton is an excellent place to learn and do applied math – a lot of professors in many different departments, from engineering to biology to physics, are interested in this area. The strength of the math department and the breadth of possible applications is outstanding. Further, the department is very flexible with undergraduate requirements, so you can usually do anything that you want, as long as it has some mathematical component. The applied math program itself is small, but encourages its students to work with professors outside the core program. If you like math, but also care seriously about some other field, consider being a math major with an applied focus.

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#### Opportunities

**Employment possibilities**

Applied math is a very broad notion, and it can be tailored to fit many interests and possible career paths. After graduation you can work in finance, computer science, consulting, engineering, or a number of other quantitative fields – as long as you make sure to take the requisite courses. The combination of rigorous mathematical thinking with an awareness of real-world applications is valuable to potential employers.

If you like math, but also care about some other field, you will likely enjoy applied math. However, you will have the freedom to pursue your curiosity and explore a variety of applications that you may not be familiar with at this point. Have an open mind! Take courses in economics, physics, biology, computer science, neuroscience or any other discipline that you don’t know too much about. Maybe you’ll discover a new passion! In addition, you get to see what kind of math they are using, and focus on that in your math studies. As a personal example, this is how I realized just how important probability and statistics were across many applications, and motivated me to pursue them further.

**Work with excellent researchers, in a broad array of fields**

Princeton faculty members are absolutely brilliant as a rule. It is an amazing opportunity to study with them. As a math major with an applied focus, you can do basically anything and everything with a quantitative component. This means that you have access to faculty not just from the math department, but also from many other ones.

#### Challenges

**Planning coursework**

It may not be entirely straightforward to plan your courses. This is in contrast to math, where it is quite clear what to do: take intro 200-level classes, then take the 300-s, and so on. For applied math, the courses are not polished in the same way. Many crucial applied math courses are offered in other departments. For instance, optimization and statistics are in ORFE, algorithms is in Computer Science. Further, depending on what application you are interested in, additional advanced courses are required. However, at this point you may not know what you are interested in. In any case, it is worth to think ahead, and talk to upperclassmen, grad students and faculty.

**Logistics of arranging work with faculty from other departments**

It is sometimes challenging to arrange independent work with faculty from other departments. Other departments assign independent work differently, and sometimes earlier than the math department. Therefore, the faculty members you are interested in working with may already have a large number of students. This has happened to me two times! However, in both cases the faculty members agreed to also supervise me, so in this particular case things worked out.

**Scattered groups sharing your interests**

You may find that, while the people and groups sharing your interests exist, they are scattered across several departments. This is important, because you may want to hang out with those people (undergrads and grad students), attend their seminars, and see what they are they working on to get a better picture of the field. This scattered organization brings an extra level or organizational challenge.

#### Program in Applied and Computational Math (PACM) Certificate

Princeton offers the PACM certificate program for undergraduates interested in applied math. The certificate, in addition to looking good on a resume, comes with a series of seminars in which students can both present their independent work and hear about the projects undertaken by other certificate students.

The certificate has three requirements: coursework, independent work, and certificate seminars. The required coursework is five classes from a wide range of pure and applied math topics, which is fairly flexible. The independent work component consists of a research project with a professor in addition or building off of other independent work requirements, i.e., junior papers and senior thesis. Finally, the certificate seminars are as described above: they entail giving a presentation on your independent work and listening to presentations of others.

For more detailed information on the certificate and on the PACM program, please see their website here.

#### A Sample Applied Math Curriculum

Statistics and data analysis. Many fields of science, engineering and business are increasingly data-driven, and thus several fields are becoming interested in how to deal with massive data. This is classically a sub-field of statistics, but more recently people from applied math, computer science (machine learning) and computational biology have become seriously interested in it.

#### Course list

APC 520/MAT 540 Mathematical Analysis of Massive Data

COS 226 Algorithms and Data Structures

COS 402 Artificial Intelligence

COS 423 Theory of Algorithms

ECO 312 Econometrics: A Mathematical Approach

EEB 414/MOL 414 Genetics of Human Populations

MAT 217 Honors Linear Algebra

MAT 218 Analysis in Several Variables

MAT 322 Algebra with Galois Theory

MAT 325 Topology

MAT 327 Introduction to Differential Geometry

MAT 331 Analysis II: Complex Analysis

MAT 332 Analysis III: Integration Theory

MAT 390 Probability Theory

MAT 391 Random Processes

MAT 90 Representation Theory of Finite Groups

MOL 342 Genetics

MOL 455/ COS 455 Introduction to Genomics and Computational Biology

ORF 522 Linear Optimization

ORF 524 Statistical Theory and Methods

ORF 525 Generalized Regression Models

ORF 527 Stochastic Calculus

ORF 565 Empirical Processes and Asymptotic Statistics

PHY 105 Advanced Physics (Mechanics)

PHY 106 Advanced Physics (Electromagnetism)

PHY 207 Mechanics and Waves

#### Outlook and Advice

Applied math is really everywhere – consider, for instance, the problem of predicting new data points from past data. This is essentially a mathematical problem, yet it can be used in almost any field. I would encourage students in applied math to take courses in different departments and talk to a professors whose research sounds interesting. I would also add that they shouldn’t be afraid of not knowing enough math or, conversely, working on a project without “enough” math. What really matters is that you can pick up the necessary tools to understand and solve the problem you’re working on – from my perspective, applied math is all about taking problems from other disciplines and using mathematical techniques to solve them.

**Contacts**

Carlee Joe-Wong ’11 (cjoe[at]math[dot]princeton[dot]edu)

Edgar Dobriban ’12 (dobribanedgar[at]gmail[dot]com)

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