Testimonials from recent graduates
"I was very fortunate and received Wallace Memorial Fellowship from the Princeton Graduate School in 2014, and I think Kevin Chen was awarded this last year which is interesting since both were in the graduate certificate program! I think this is directly testament to the quality of the program aiding us in our computational pursuits. It was an excellent training for my post-graduate career plans as an engineer/scientist." - George Khoury, Chemical & Biological Engineering
"This program not only introduced me to fundamental best-practices in programming like version control systems, automated documentation generation, and object abstraction, but also allowed me to deepen my understanding in topics relevant to research, including code optimization and machine learning. It was an invaluable part of my time at Princeton, and I’ve recommended the program to all of my friends doing computational work." - Alexander Holiday, Chemical & Biological Engineering
Introduction and Rationale
Computation is now a crucial tool for discovery in the sciences, engineering, and increasingly so in the humanities. Scientific computation is also a diverse field. It requires a working knowledge of numerical analysis (to develop new and more accurate algorithms), best-practices in software engineering (to implement and maintain ever-growing scientific software systems), computer science (to exploit emerging trends in hardware and programming practices), and statistics and data modeling (to analyze and interpret the massive digital data sets that are now routinely collected in all fields).
The graduate certificate in computational and information science is only open to Princeton University graduate students who are currently enrolled. It is designed to recognize the achievements of students who have undertaken comprehensive training in these topics, both through formal course work and through research in their subject area.
The certificate program was originally proposed and designed to be part of the Program in Integrative Information, Computer and Application Sciences (PICASso) by Professor J.P. Singh, with the resources required to administer the program now provided by the Princeton Institute for Computational Science and Engineering (PICSciE).
To earn the certificate, students must complete four requirements: (1) take for credit and pass two of the three approved core courses, (2) take for credit and pass one approved elective course, usually this is a course specific to their research discipline, (3) give a seminar/public talk (in their home department) on their thesis research, and (4) write a thesis which contains a significant computational component, as judged by the thesis advisor who must write a short letter to certify this component.
Scroll down for more details or go to the FAQ page for additional information.
Online application is now available, and is on-going throughout the year.
The following describes in more detail each of these requirements:
Students must take any two of the three core courses. This requirement is designed to guarantee all students who earn the certificate have a solid foundation in the basic principles of scientific computation and data analysis. The core courses are:
APC 524: Software Engineering for Scientific Computing (Fall semester).
Covers the tools and techniques that are crucial for effective use of computation in any discipline. Topics include structured programming in compiled versus scripting languages, software management tools, debugging, profiling and optimization, and parallel programming for both shared and distributed memory systems.
APC 523: Numerical Algorithms for Scientific Computing (every Spring semester).
A broad introduction to numerical algorithms used in scientific computing. The course begins with a review of the basic principles of numerical analysis, including sources of error, stability, and convergence. The theory and implementation of techniques for linear and nonlinear systems of equations and ordinary and partial differential equations are covered in detail. Examples of the application of these methods to problems in engineering and the sciences permeate the course material. Issues related to the implementation of efficient algorithms on modern high-performance computing systems are discussed.
COS 424/SML302: Fundamentals of Machine Learning (every Spring semester).
This course will focus on some of the most useful approaches to the problem of analyzing large complex data sets, exploring both theoretical foundations and practical applications. Students will gain experience analyzing several types of data, including text, images, and biological data.
One course required. This requirement is designed to give students expert training in their subject. Elective courses can be selected from any graduate-level course on campus, provided the course contains a significant computational component. In special circumstances, advanced undergraduate level courses may also count towards the elective. Each student must seek approval of the certificate program director for the course they select as an elective. Approval may be granted for courses already taken. In general, the elective course will be offered by the student’s home department.
Some examples of suitable elective courses are:
- AOS 575: Numerical Prediction of the Atmosphere and Ocean
- APC 522/MAT 522: Introduction to Partial Differential Equations
- AST 560: Computational Methods in Plasma Physics
- CEE 505: Statistical Methods for Data Analysis, Modeling and Experimental Design
- CEE 513: Introduction to finite element methods
- CEE 532: Advanced Finite-element Methods
- COS 323: Computing and Optimization for the Physical and Social Sciences
- COS 324: Introduction to Machine Learning
- COS 511: Theoretical Machine Learning
- CEE 525: Applied Numerical Methods
- COS 551 / MOL 551: Introduction to Genomics & Computational Molecular Biology
- COS 557 / MOL 557: Analysis & Visualization of Large-Scale Genomics Data Sets
- CBE 554 / APC 544: Topics in Computational Nonlinear Dynamics
- COS 402: Machine Learning and Artificial Intelligence
- COS 522: Computational Complexity
- COS 598: Systems and Analytics of Big Data
- ELE 535: Machine Learning and Pattern Recognition
- ELE 585: Parallel Computation
- GEO 441 / APC 441: Computational Geophysics
- GEO 422: Data, Models & Uncertainty in the Natural Sciences
- GEO 522: Inverse Methods: Theory and Applications
- MAE 501: Mathematical Methods of Engineering Analysis I
- MAE 502: Mathematical Methods of Engineering Analysis II
- MAE 557: Simulation and Modeling of Fluid Flows
- ORF 522: Linear and Nonlinear Optimization
- ORF 523: Convex and Conic Optimization
- ORF 544: Stochastic Optimization
- QCB 455 / MOL 455 / COS 455: Introduction to Genomics and Computational Molecular Biology
- MSE 504 / CHM 560 / PHY 512 / CBE 520: Monte Carlo and Molecular Dynamics Simulation in Statistical Physics & Materials Science
- NEU 537/ MOL 537 / PSY 517: Computational Neuroscience & Computing Networks
- ORF 531 / FIN 531: Computational Finance in C++
- ORF 538: Analytical and Computational Methods of Financial Engineering
- SOC 596: Computational Social Science
The ability to communicate their research to a broad audience, as well as interact with students across disciplines on shared tools and challenges, is an important skill for all students. In order to encourage both of these goals, as part of the certificate program students are required to give a research seminar on their thesis research sometime before graduation. Normally, this would be scheduled in the last year of research so there are significant results to the report. The seminar (a public talk of at least 30 minutes) may be organized and hosted directly by PICSciE, or it may be in the home department. In either case, the program administrator must be informed well in advance so that the seminar can be broadly advertised by PICSciE.
The final requirement for the certificate is that the student’s thesis research must include a significant computational component, broadly defined. Since the role of computation differs across disciplines, the program will rely on the judgment of experts in the specific discipline to certify whether the goal of a “significant computational component” has been achieved. Thus, the student’s adviser is asked to write a short letter outlining the role of computation in the thesis, and to certify that this work required a significant computational component as part of the original research it contains.
Faculty Advisers in the Program
A significant fraction of the faculty rely on computation for their research, and all are potential advisers for students in the program. Below we list some of the key faculty participants (including all the associated faculty with PICSciE) we expect will serve as advisers, however we emphasize this list is not exhaustive.
James Stone, Chair of Department of Astrophysical Sciences, Lyman Spitzer, Jr. Professor of Astrophysical Sciences, Professor of Applied & Computational Mathematics and member of PICSciE's Executive Committee
Key Faculty Participants
- David August, Computer Science
- Venkatramani Balaji, Atmospheric and Oceanic Science
- Ravin Bhatt, Electrical Engineering
- Adam Burrows, Astrophysical Sciences
- Roberto Car, Chemistry
- Emily Carter, Mechanical & Aerospace Engineering
- Maurizio Chiaramonte, Civil & Environmental Engineering
- Jonathan Cohen, Psychology
- Peter Constantin, Mathematics
- Pablo Debenedetti, Chemical & Biological Engineering
- Steve Jardin, Plasma Physics
- Ioannis Kevrekidis, Chemical & Biological Engineering
- Laura Landweber, Ecology & Evolutionary Biology
- Naomi E. Leonard, Mechanical & Aerospace Engineering
- Simon Levin, Ecology & Evolutionary Biology
- Kai Li, Computer Science
- Robert Lupton, Astrophysical Sciences
- Luigi Martinelli, Mechanical & Aerospace Engineering
- Athanassios Panagiotopoulos, Chemical & Biological Engineering
- Warren B. Powell, Operations Research & Financial Engineering
- Frans Pretorius, Physics
- Peter Ramadge, Electrical Engineering
- Jennifer Rexford, Computer Science
- Alejandro Rodriguez, Electrical Engineering
- Clarence Rowley, Mechanical & Aerospace Engineering
- Szymon Rusinkiewicz, Computer Science
- Annabella Selloni, Chemistry
- Kaushik Sengupta, Electrical Engineering
- Mona Singh, Computer Science
- Jaswinder Singh, Computer Science
- Alexander Smits, Mechanical & Aerospace Engineering
- David Spergel, Astrophysical Sciences
- Anatoly Spitkovsky, Astrophysical Sciences
- John Storey, Molecular Biology
- William Tang, Astrophysical Sciences/Plasma Physics
- Robert Tarjan, Computer Science
- Jeroen Tromp, Geosciences
- Olga Troyanskaya, Computer Science
- Christopher Tully, Physics
- Hakan Tureci, Electrical Engineering
- Janet Vertesi, Sociology
- David Wentzlaff, Electrical Engineering
- Ned Wingreen, Molecular Biology
Administration of the Program
PICSciE administers the program, and part of its responsibility is to appoint a program director each year, to advertise the program, to identify students who are working to achieve the certificate, and to ensure these students understand all of the requirements and to help them meet them.
Upon completion of all requirements, and at the receipt of an M.A./M.S. or Ph.D. diploma in his or her discipline, the program director will recommend them to the PICSciE Executive Committee, who must give final approval to award the certificate. Only the program director can recommend students for the certificate to the Executive Committee. The program director shall award the student a letter of certification in Computational and Information Science.
James Stone, Graduate Certificate Program Director
Ma. Florevel (Floe) Fusin-Wischusen, Institute Manager & Program Administrator
For more information, see the Graduate Certificate Program FAQ page.