The University of Chicago’s Master’s in Computational and Applied Mathematics (MCAM) empowers you to develop skills to make a tangible impact in as little as nine months. Work with faculty to tackle inverse problems in medical or geophysical imaging, build fast algorithms for massive data sets, or optimize power grids, freight networks, nuclear reactors, or climate models alongside UChicago researchers.
This rigorous and versatile Master's in Computational and Applied Mathematics prepares you for work in industry, research, or future doctoral studies. You’ll complete at least one of two tracks: the Computational Mathematics Track or the Applied Analysis and Modeling Track. Join one of the leading applied mathematics graduate programs, and learn from faculty at the forefront of modeling, data, and computation.
Why UChicago’s MCAM is Among the Best Applied Mathematics Graduate Programs
| A Curriculum Built for Modern Science & Industry | Research That Bridges Disciplines | Careers with Momentum |
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Build graduate-level foundations in analysis, numerical methods, optimization, scientific computing, PDEs, stochastic modeling, and data-driven inference. Explore a wide-range of electives across top-ranked departments in the Physical Sciences Division. View Curriculum |
Collaborate with renowned researchers from math, statistics, and computer science, physics, chemistry, and other scientific and mathematical areas at UChicago. Explore applications of theory to modern challenges in health, energy, computing, and more with a master's in applied mathematics and computation. Explore Research |
Build foundational skills prized by national labs, R&D groups, finance and technology firms, and top PhD programs. Access to mentoring, career coaching, and a powerful alumni network help make MCAM one of the best applied mathematics graduate programs. Career Outcomes |
Customize Your Academic and Career Path
You will choose among the many introductory graduate courses offered to the computational and applied mathematics (CAM) PhD students, covering traditional CAM physics-based modeling fields (dynamical systems, PDE) as well as more recent data-based modeling areas (data assimilation, machine learning) and their interactions, e.g., in uncertainty quantification.
Research Directions
Interested in pursuing a PhD program? We encourage you to consider the thesis option for your master's in applied mathematics and computation. This allows you to work with a faculty mentor on applied math research.
You’ll also collaborate with master’s and PhD graduate students from CAM and other disciplines. Join the rich and distinguished academic activities of the Committee on Computational and Applied Mathematics (CCAM), including our research seminars and colloquia.
Career Pathways
Our graduates contribute to discovery science, lead analytics teams, build robust software and models, and enter top PhD programs. With rigorous training, cross-disciplinary access, and exceptional advising, MCAM is one of the best applied mathematics graduate program experiences for students seeking both depth and versatility.
Graduate with the skills to pursue careers in:
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Featured Faculty
Our computational mathematics graduate programs are offered by the Committee on Computational and Applied Mathematics (CCAM), with renowned faculty from the departments of statistics, mathematics, computer science, and other departments in the Physical Sciences Division.
Guillaume Bal
Director, CCAM, and Professor, Departments of Statistics and Mathematics
Bal’s research interests include partial differential equations (PDEs) with random coefficients and theory of inverse problems. He is the 2011 winner of the Calderón Prize Lecture given by the Inverse Problems International Association (IPIA).
Lek-Heng Lim
Professor, Department of Statistics and member of the Executive Committee, CCAM
Lim is the recipient of the 2023 Vannevar Bush Faculty Fellowship and the 2022 John Simon Guggenheim Memorial Foundation Guggenheim Fellowship in Applied Mathematics. He studies machine learning and other applied subjects using tools that have been developed in pure math fields like algebra, geometry and topology.
Rebecca Willett
Worah Family Professor, Departments of Statistics and Computer Science
Willett focuses on signal processing, machine learning, and large-scale data science. Willett has active collaborations with researchers in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.
CAM and Statistics Student Seminar
Every Wednesday, CAM and Statistics graduate students run a weekly seminar—picking speakers, topics, and formats. Recent talks ranged from GPU acceleration for elliptic PDEs to nonnegative tensor factorization and LLM training economics, and even a playful look at New York Times games through a computational lens.
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