RESEARCH AREAS

Georgia Tech faculty with advanced supercomputer

The Computational Science and Engineering division at Georgia Tech was established in 2005 to better reflect the critical role that computation plays at Georgia Tech and in the broader technology community. Along with theory and experimentation, computation has gained widespread acceptance as a key component in the advancement of knowledge and practice in science and engineering disciplines. The division is devoted to the systematic study of computer-based models of natural phenomena and engineered systems.

Research in computational science and engineering at Georgia Tech spans many areas ranging from the development of new computational methods that may be applied to one or more fields in science and/or engineering to novel computational approaches specific to a particular domain such as biology or aerospace engineering.

Because Georgia Tech views computation as the driver of future advances in science and engineering, the Computational Science and Engineering division was created to be a truly interdisciplinary unit that crosses the conventional academic boundaries found between research disciplines. Faculty from all walks of computing, sciences, and engineering collaborate within six core areas.

High Performance Computing
supercomputer cluster

Many computational problems in science and engineering require the most powerful computers available today. The widespread deployment of multicore and manycore architectures has highlighted the need to exploit parallel computing techniques. Research in high performance computing at Georgia Tech spans algorithms, software, tools and applications that exploit modern high performance computing platforms ranging from multicore/manycore machine architectures to graphics processing units to supercomputers.

Current research is examining areas such as heterogeneous architectures, parallel algorithms, performance tuning, and a variety of computation intensive applications in science and engineering.

Data Analytics, Machine Learning and Visualization
data visualization

The Internet, widespread deployment of sensors, and scientific advances (such as the mapping of the human genome) have resulted in a tsunami of data. In many fields, the challenge has shifted from collecting a sufficient amount of data to understanding and gaining insight from the massive amount of data available. Research in data analytics, machine learning, and visualization is concerned with developing computational methods to extract knowledge from large and complex interrelated data sets. Current research spans machine learning, clustering, dimension reduction, pattern recognition, visualization, and analysis of massive data sets. Much of this research focuses on search and information retrieval for documents and information on the World Wide Web and analyzing use of the Web.

Modeling and Simulation
modeling and simulation

Modeling and simulation has become indispensable in many fields to analyze systems and to design new products. The computation intensive nature of many simulations require the exploitation of parallel computing platforms. Other simulations, e.g., models embedded in the real world, require execution on geographically distributed computing platforms. Current research spans both continuous and discrete event simulation. Research areas include numerical simulation techniques, execution on parallel and distributed computing platforms, statistical analysis of simulation data, and novel techniques for modeling systems.

Computational Mathematics
computational mathematics

Mathematical models form the underlying basis for much of the research in computational science and engineering today. Current research in computational mathematics spans both discrete and continuous mathematics. Areas of research include matrix computation, numerical solution of integral and differential equations, uncertainty estimation, geometric modeling, continuous and discrete optimization, inverse problems, numerical analysis, and statistics.

Computational Science
computational science

Computation has become essential for scientific discovery in fields such as biology and chemistry. The mappings of the human genome and advances in biomedical and computational technologies have transformed the field of biology. Computational science research spans areas such as molecular dynamics, quantum mechanics, chemical reactions, cosmology and astrophysics, biomolecules, biophysics, systems biology, bioinformatics, and biomedical devices.

Computational Engineering
computational engineering

Computation has become ubiquitous in virtually all fields of engineering practice in the analysis and design of devices and systems. Computational engineering research is exploring new frontiers in areas such as high performance materials, large-scale nonlinear structural analysis, mechanics, computational fluid dynamics and turbulence, combustion, aerodynamics and aeroelasticity in aircraft design, transportation and energy, nanotechnology, sustainability, water and environment, manufacturing, supply chains and logistics, homeland security and defense, and the design of new devices in medicine.