• 2 Gallery Ranks Pavis © 2016 IIT 5467
  • Molecule Simulation 3D © 2016 IIT 1922
  • ADB Brochure Picture2 2156 749x388 © 2016 IIT 5111
  • Molecule Simulation 3D © 2016 IIT 1919

Massive computation and the emerging exascale infrastructures have a major impact on IIT’s Strategic Plan. Two major developments in computational sciences are the massive simulations of physical systems, repeated numerous times to generate robust statistics, and data analysis in general, including images, video, sound and speech and many others, including the mining of vast datasets to identify unexpected patterns (i.e., big data analytics). The latter is primarily tackled by artificial intelligence (AI) approaches, specifically computer vision, machine learning and pattern recognition.

Massive simulations are impacting on many fields including drug discovery, material science, nanoparticle design, and, more generally, condensed matter physics.

Data analysis and mining are playing a major role in many technologies and diverse application domains, such as robotics, psychology, neuroimaging, life sciences, and bio- and neuroscience areas, such as computational biology, genomics and many other -omics fields.

Computational activities need informatic infrastructures, including high-performance computing (HPC), big data storage, and cloud computing. Further, the rapid growth of more and more powerful Graphic Processing Units (GPU) allows the effective management of machine learning and, specifically, deep learning architectures, impossible to do just a few years ago. 

In this context, one of the main objectives of 2018-2023 Computational Sciences Research Domain is to establish a dynamic interplay between HPC and big data analytics. We are achieving this by developing a new portfolio of HPC codes, AI methods and approaches, ranging from new tools for multiscale simulations to the design of machine learning and deep learning algorithms.

Ultimately, the long-term aim is to investigate and improve the knowledge in the several scientific and technological domains above quoted, in order to design intelligent systems able to cope with real-life situations, and solve actual applications in industry, health, and societal scenarios.