Mathematics and Computer Science

A leader in the computing sciences, the MCS Division provides the numerical tools and technology for solving some of our nation’s most critical scientific problems.

In addition to our world-class research, we develop the software for some of the fastest, most powerful computer systems in the world: systems that are enabling scientists to tackle problems previously considered infeasible.

Announcement

Iwasaki receives postdoctoral performance award

Shintaro Iwasaki, a postdoctoral appointee in the Mathematics and Computer Science (MCS) Division, has received an Argonne 2020 Outstanding Postdoctoral Performance Award in the area of applied research.

Programming Models, Runtime Systems

Announcement

MACSER leader to deliver webinar on computing cascading failure probabilities

Mihai Anitescu, senior computational mathematician at Argonne National Laboratory and leader of the MACSER project, will present a webinar talk Oct. 7, 2020, on computing the probabilities of cascading power grid failures.

Monte Carlo Methods

Announcement

Paul Manns named recipient of the 2020 Wilkinson Fellowship

Paul Manns has been awarded the 2020 J.H. Wilkinson Fellowship in Scientific Computing at Argonne National Laboratory.

J.H. Wilkinson Fellow in Scientific Computing

High school students taking part in Big Data Camp meet virtually with Guadalupe Medina (upper left), a Community College Internship (CCI) Program intern working in Argonne’s Strategic Security Sciences Division (specifically in cybersecurity).

Feature Story

Students connect STEM skills to real-world challenges through the 2020 Virtual Big Data Camp

At Argonne’s 2020 Virtual Big Data Camp, high school students discovered professional uses for their STEM and data skills, while also becoming immersed in Argonne’s dynamic science community.

Computational Science, Computational Methods, Computer Science and Engineering

A visualization of the trajectory produced by the learned RL policy on one QAOA test problem with two parameters. The policy takes strides in the energy landscape of the parameter space towards a maximum. (Image by Prasanna Balaprakash / Argonne National Laboratory.)

Feature Story

Scientists use reinforcement learning to train quantum algorithm

Scientists are investigating how to equip quantum computers with artificial intelligence and machine learning approaches.

Applied Mathematics and Statistics, Optimization, Computational Science

Awards and Recognition

Argonne National Laboratory

Iwasaki receives postdoctoral performance award

MACSER leader to deliver webinar on computing cascading failure probabilities

Paul Manns named recipient of the 2020 Wilkinson Fellowship

Students connect STEM skills to real-world challenges through the 2020 Virtual Big Data Camp

Scientists use reinforcement learning to train quantum algorithm

Argobots technology named finalist for the 2020 R&D 100 Awards

5 Q’s for Adam Szymanski, Software Engineer at Argonne National Laboratory

Chameleon cloud computing testbed launches third phase

MCS Focus Areas

Advanced Architectures and Systems

Computational Engineering and Methods

Data Management and Storage Systems

Mathematical Modeling and Optimization

Foundations of ML, Data Analysis, and Statistics

Numerical Software and Mathematical Libraries

System Software and Performance Engineering

CONTACT US

Mathematics and Computer Science General Inquiries

Related Organizations

Machine Learning

Exascale Computing in the MCS Division

MACSER: Multifaceted Mathematics for Rare, High-Impact Events in Complex Energy and Environment Systems

MPICH: A High-Performance, Portable Implementation of MPI

Related Content

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OUR LEADERSHIP

Valerie E. Taylor

Paul Hovland

Stefan M. Wild

Argonne National Laboratory