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Master of Science

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At Stanford, ICME has offered MS and PhD degrees in computational mathematics for over 30 years. We conduct groundbreaking 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. We welcome applications from well-qualified students to study in our graduate programs.

Master of Science

Through robust coursework in computational mathematics and computing, ICME MS program is designed to provide students with the knowledge and skills necessary for a professional career or doctoral studies.  This is done through coursework in mathematical modeling, scientific computing, advanced computational algorithms, and application field. 

Recommended background: strong foundation in mathematics with courses in linear algebra, numerical methods, probabilities, stochastics and programming proficiency in C and MATLAB.

The ICME MS program includes the option to pursue either the general degree track or a specialization area in one of the following 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 FORTRAN or C.

Learn more by visiting the CompGeo Website

Data Science Track

Students in the Data Science track will develop strong mathematical, statistical, computational, and programming skills through the 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 (MCF)

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 doctoral programs in related fields. 

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.

Learn more by visiting the MCF Website

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