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.

Research Highlight

Recognizing software as essential for scientific discovery

In the introduction to the July 2021 special issue of the journal SoftwareX, Keahey and her coeditors present a strong argument for giving software the respect it deserves – and the funding that must accompany it.

Photo of Center for Nanoscale Materials building. (Image by Argonne National Laboratory.)

Awards and Recognition

Three Argonne projects receive DOE funding for breakthroughs in quantum information science

Tomorrow’s quantum sensors, computers and networks will share, process and secure exponentially more information — starting with the scientific data that will make this wave of the future possible.

Awards and Recognition, Quantum Information Science, quantum materials

Awards and Recognition

Shin Wins Young Author Award at model predictive control conference

Sungho Shin, a postdoctoral appointee in the Mathematics and Computer Science (MCS) Division at Argonne National Laboratory, has won a Young Author Award at the International Federation of Automatic Control – Nonlinear Model Predictive Control Conference.

Awards and Recognition

DOE awards $1.2M to Argonne and Northwestern to maximize scientific data sent over 5G network

5G supports computing from edge devices, such as cameras and weather sensors, to the radio towers between the edge and the cloud. This new funding will help researchers extract data from every point along this digital continuum.

Computer Science and Engineering, Artificial Intelligence

Research Highlight

Estimating cloud coverage with deep learning methods: Key to solar power production

In a study published in a special issue of the journal Atmosphere, a team of researchers from Argonne National Laboratory presented a novel ground-based approach using machine learning for estimating cloud cover.

Machine Learning, Deep Learning, Artificial Intelligence

In the News

RENCI to join researchers in a collaboration to increase reliability and efficiency of DOE scientific workflows by leveraging artificial intelligence and machine learning methods

RENCI

In the News

What an ‘intelligent hurricane’ can uncover

Smart Energy International

Awards and Recognition

Sneak preview: New platform allows scientists to explore research environments virtually

Computer Science and Engineering, Artificial Intelligence, Data Science/Data Analysis

Argonne National Laboratory

Recognizing software as essential for scientific discovery

Three Argonne projects receive DOE funding for breakthroughs in quantum information science

Shin Wins Young Author Award at model predictive control conference

DOE awards $1.2M to Argonne and Northwestern to maximize scientific data sent over 5G network

Estimating cloud coverage with deep learning methods: Key to solar power production

RENCI to join researchers in a collaboration to increase reliability and efficiency of DOE scientific workflows by leveraging artificial intelligence and machine learning methods

What an ‘intelligent hurricane’ can uncover

Sneak preview: New platform allows scientists to explore research environments virtually

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

OUR PEOPLE

OUR LEADERSHIP

Valerie E. Taylor

Paul Hovland

Stefan M. Wild

Argonne National Laboratory