Quant Guide 2022: Stony Brook University

Brookhaven, New York, US

 

 

Stony Brook University’s Quantitative Finance MS has grown steadily in recent times: from 11 students two years ago, to 21 last year, and now to 31. This bucks the trend of some institutions whose quant master’s cohorts have declined in size, possibly as an effect of the Covid-19 pandemic.

Teaching staff are unchanged from last year, and Stan Uryasev, professor in Stony Brook’s department of applied mathematics and statistics, is still the programme’s director. The effects of the pandemic on learning were limited, says quantitative finance co-ordinator Laurie Dalessio: academic performance stayed constant throughout periods of remote learning, and the programme is unaware of any negative impacts on employment. In fact, the employment rate, at 85%, has risen from last year’s 78%.

Instructors on the three-semester programme include professor of applied mathematics James Glimm, known for his work on partial differential equations (PDEs), quantum field theory and scientific computing; statistician Wei Zhu, whose research focuses on biostatistics and quantitative finance; and Pei Fen Kuan, an associate professor who investigates computational approaches to genomics.

Modules on offer are split between core and elective classes. Core classes include topics such as: analytical methods for applied mathematics and statistics; advanced stochastic models; and financial derivatives. Electives are split across five focus areas: modelling and risk management in finance; machine learning and big data; statistics and data analytics; stochastic calculus, optimisation and operations research; and computational methods and algorithms.

The course also has an emphasis on programming skills, encouraging less-experienced students to complete a range of foundational classes in languages such as Python, Matlab and C++.

View this institution’s entry in the 2021 guide

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