University of Massachusetts Amherst

What you'll study

This programme combines rigorous mathematical theory with practical computational techniques. Core topics typically include numerical analysis, numerical linear algebra, scientific computing, numerical solution of partial differential equations, optimisation, and scientific programming. Elective options often cover advanced topics such as high-performance computing, computational statistics and data analysis, stochastic modelling and uncertainty quantification, spectral methods, and algorithm design.

Students choose from coursework-only and research/thesis tracks. The coursework track emphasises advanced classes and computational projects, while the thesis track pairs students with faculty supervisors for a substantial research project that applies numerical methods to problems in physics, engineering, finance or data science. Practical components commonly include programming assignments in languages such as Python, C/C++ or MATLAB, project work using parallel computing environments, and collaboration with research groups across campus.

Entry requirements

• A recognised undergraduate degree in mathematics or a closely related discipline (for example applied mathematics, physics, engineering or computer science) with strong performance in core mathematical subjects.
• Preparation in calculus, linear algebra, differential equations and probability/statistics; prior coursework or demonstrable experience in numerical analysis or programming is strongly recommended.
• Application materials typically include transcripts, a personal statement describing academic and research interests, letters of recommendation, and a curriculum vitae. Some applicants may also submit examples of programming or project work.
• International applicants must demonstrate English language proficiency according to the university’s requirements.

Career prospects

Graduates with a master’s specialising in computational mathematics are prepared for roles that require quantitative modelling and algorithmic implementation. Common career paths include data scientist, quantitative analyst, computational scientist, software or algorithm engineer, and modelling specialist in sectors such as finance, energy, engineering, pharmaceuticals and government research labs. The degree also provides a strong foundation for continued study at doctoral level in applied mathematics, computational science or related disciplines.

Why study at University of Massachusetts Amherst

UMass Amherst has a large and active Department of Mathematics and Statistics with faculty working in numerical analysis, applied and computational mathematics, and interdisciplinary areas. Students benefit from access to campus research centres and computing resources, opportunities to collaborate with departments such as computer science, engineering and statistics, and a range of seminar and research projects. The department supports graduate students through teaching and research assistantships and fosters connections with regional research labs and industry partners, making it a strong choice for students seeking both rigorous mathematical training and applied computational experience.