University of Massachusetts Amherst

What you'll study

The programme combines core quantitative topics with applied biological and computational modules. Typical coursework covers mathematical modelling of biological systems (ordinary and partial differential equations), stochastic processes in biology, computational genomics and sequence analysis, algorithms for bioinformatics, statistical methods for high‑throughput data, machine learning for biological data, systems and network biology, and numerical methods and high‑performance computing.

Students normally choose a mix of mandatory and elective modules to build either a more mathematical/theoretical profile or an applied/computational profile. Examples of course themes you can expect:

• Mathematical Modelling of Physiological and Ecological Systems
• Probability, Stochastic Processes and their Applications in Biology
• Algorithms and Data Structures for Bioinformatics
• Statistical Inference for Genomics and Transcriptomics
• Systems Biology and Network Analysis
• Machine Learning and Data Mining for Biological Data
• High‑Performance Computing for Large Biological Datasets
• Computational Structural Biology and Molecular Simulation (elective)

The degree can include a research thesis or a supervised project depending on the chosen track. Research projects are often interdisciplinary and supervised jointly by faculty from mathematics/statistics, computer science and life sciences, giving practical experience in experimental design, data analysis, software tools and reproducible research practices.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, statistics, computer science, engineering, biology or a closely related discipline, with solid preparation in calculus and linear algebra. Programming experience (e.g. Python, R, C/C++) and familiarity with probability and basic statistics are strongly recommended.

Typical application materials include:

• Official academic transcripts demonstrating quantitative coursework
• A personal statement describing research interests and relevant experience
• Letters of recommendation, preferably including at least one from an academic who can speak to mathematical or computational ability
• A CV/resume summarising technical skills and research or project experience

International applicants must meet English language proficiency requirements. The programme evaluates applicants holistically; relevant research experience, strong quantitative background and programming skills strengthen an application. Note that specific departmental policies on standardized tests (e.g. GRE) and minimum GPA vary and should be confirmed on the programme website.

Career prospects

Graduates move into a wide range of roles across academia, industry and public sector research. Common career paths include:

• Computational biologist or bioinformatician in biotech and pharmaceutical companies, performing genomic, transcriptomic and proteomic analyses
• Data scientist or machine learning engineer specialising in life‑science datasets
• Biostatistician or quantitative analyst in clinical research organisations and health‑care analytics
• Research scientist or research technician in university labs, government agencies and national research centres
• Software developer for scientific tools and pipelines supporting biological data processing
• Further academic training via PhD programmes in computational biology, bioinformatics, biostatistics or applied mathematics

The programme’s emphasis on interdisciplinary collaboration and practical project work helps graduates demonstrate the technical and collaborative skills sought by employers in genomics, precision medicine, agricultural biotech, environmental modelling and related fields.

Why study at University of Massachusetts Amherst

UMass Amherst offers an interdisciplinary environment where mathematics, statistics, computer science and life sciences collaborate closely. The Biomathematics, Bioinformatics and Computational Biology programme leverages faculty expertise across departments, enabling supervised projects that cross traditional boundaries.

Students benefit from institutional resources that support computationally intensive research, including access to regional high‑performance computing facilities and campus research centres focused on data and life sciences. The campus also fosters industry connections across the Massachusetts biotech ecosystem, and collaborative opportunities with neighbouring institutions in the Five College Consortium and state research initiatives.

Overall, UMass Amherst provides both the quantitative training and the applied research environment suited to students who want to build technical competence and move into research or industry roles that require rigorous computational approaches to biological problems.