Universities / Stanford University
MS in Computational and Mathematical Engineering, Mathematical and Computational Finance track (MCF)
A long-established Stanford programme that blends financial mathematics with software engineering, data science and machine learning. Very small, highly selective cohorts with a practical component in risk analytics and AI in fintech.
What you’ll study
Core
- Numerical linear algebra
- Convex optimization
- Stochastic differential equations
- Programming abstractions
- Parallel computing (MPI, OpenMP, CUDA)
- Master's research
- Financial risk analytics
- Artificial intelligence in financial technology
Electives
- Statistical arbitrage
- Machine learning for electronic markets
- Decentralized finance
- Mining massive data sets
- Blockchain market design and microstructure
Source: mcf.stanford.edu ↗
Before you apply
- Maths
- Minimum of three university mathematics classes; Recommended: linear algebra, numerical methods, probability, statistics, real analysis
- Programming
- Python and C++ topics students should be prepared to study
- Tests
- GRE/GMAT not required or accepted
- Other
- No work experience required; no formal prerequisites listed
Source: mcf.stanford.edu ↗
After graduation
- Employment
- Programme reports 'nearly 100% placement' for students seeking internships and full-time industry jobs in recent years (no class year given)
- Employers
- Goldman Sachs, Morgan Stanley, JP Morgan, Bank of America, Citigroup, Tudor Funds, Two Sigma, BlackRock, GMO, Cubist, Citadel, QRT, Vanguard, Balyasny, Millennium, IMC
Source: mcf.stanford.edu ↗
Want to see what employers ask for? Browse open quant roles.
Need-blind admissions; assistantships few and not guaranteed. Tuition follows School of Engineering graduate rates (no figure published on the page).
Checked 6 November 2026 against the sources linked on this page. Fees, units and requirements change each year, so confirm them on the programme’s website before applying.
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