At Stanford, we've offered MS and PhD degrees in computational mathematics for over 30 years. For the last 10 years, ICME has been the central home of computational mathematics on campus. We conduct ground breaking research, train and advise our graduate students and provide over 60 courses in computational mathematics and scientific computing at both the undergraduate and graduate level to the Stanford community.
Research & PhD Program
We develop innovative computational and mathematical approaches for complex engineering and scientific problems. We attract talented PhD students from across the globe. They are advised in research by 50 faculty from 20 departments, covering a wide variety of fields including statistics and data science, control, optimization, numerical analysis, applied mathematics, high-performance computing, earth sciences, flow physics, graphics, bioengineering, genomics, economics and financial mathematics, molecular dynamics, and many more. PhD graduates find outstanding positions in industry, at national laboratories, as well as in academia.
For complete details, please view the Stanford Bulletin:
Master of Science Program
Our 2 year MS degree provides a solid introduction to computational mathematics and computing. ICME MS graduates find positions in industry, or continue with a PhD in ICME or disciplinary programs.
Recommended background: strong foundation in mathematics with courses in linear algebra, numerical methods, probabilities, stochastics and programming proficiency in C++ and MATLAB.
In addition to our standard MS degree program, we offer several specialized MS degree tracks:
Computational Geosciences Track
Designed for students interested in the skills and knowledge required to develop efficient and robust numerical solutions to Earth Science problems using high-performance computing, the CompGeo curriculum is based on four fundamental areas: modern programming methods for Science and Engineering, applied mathematics with an emphasis on numerical methods, algorithms and architectures for high-performance computing, and computationally-oriented Earth Sciences courses. Earth Sciences/computational project courses give practice in applying methodologies and concepts.
Recommended background: strong foundation in mathematics with courses in linear algebra, numerical methods, probabilities, PDEs, and programming proficiency in FOTRAN or C++.
Data Science Track
Students in the Data Science track will develop strong mathematical, statistical, and computational and programming skills through the general master's core and programming requirements. This track is designed to provide a fundamental data science education through general and focused electives requirement from courses in data sciences and related areas.
Recommended background: strong foundation in mathematics with courses in linear algebra, numerical methods, probabilities, stochastics, statistical theory, and programming proficiency in C++ and r.
Imaging Sciences Track
Designed for students interested in the skills and knowledge required to develop efficient and robust computational tools for imaging sciences, the Imaging Sciences track curriculum is based on four fundamental areas: mathematical models and analysis for imaging sciences and inverse problems; tools and techniques from modern imaging sciences from medicine, biology, physics/chemistry, and earth science; algorithms in numerical methods and scientific computing; and high performance computing skills and architecture oriented towards imaging sciences. This program serves both as a terminal degree for students who are interested in a professional career in computational imaging sciences and also as a preparation for a higher level degree in imaging research.
Recommended background: strong foundation in mathematics with courses in linear algebra, numerical methods, probabilities, stochastics, and programming proficiency in C++ and MATLAB.
Mathematical & Computational Finance Track
An interdisciplinary program that provides education in applied and computational mathematics, statistics, and financial applications for individuals with strong mathematical skills. The MCF track is designed to prepare students to assume positions in the financial industry as data and information scientists, quantitative strategists, risk managers, regulators, financial technologists, or to continue on to their Ph.D. in ICME, MS&E, Mathematics, Statistics, Finance and other disciplines.
Recommended background: strong foundation in mathematics with courses in linear algebra, numerical methods, probabilities, stochastics, real analysis/pde, programming, proficiency in C++, and interest in finance/internship or industry experience.
For complete details, please view the Stanford Bulletin:
