Bionformatics Click for more details

    Computational Healthcare Click for more details

    Computational Science and Engineering Click for more details

    Computational science and engineering (also called scientific computing) is a multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems arising in various applications.Computational science frameworks include the development of numerical algorithms to solve various problems, methods to extract knowledge from large scientific data, and methods to model and simulate natural phenomena. The three main mathematical tools that dominate the field are linear algebra, mathematical optimization, and partial differential equations. The research in this field is often coupled with high-performance computing.

    Computational and Biological Vision Click for more details

    Vision is arguably the important of all senses, without which our life would be qualitatively different. For a wide range of applications, from "seeing robots" to the restoration of sight for the blind, it is indispensable to understand vision as an information processing mechanism. Such an inquiry is the primary task of Computational Vision , a scientific discipline that explores and studies vision from an interdisciplinary computational point of view both for the automatic analysis and interpretation of visual signals (i.e., images and videos) and for explaining and understanding biological, and in particular, human vision. Unlike Computer Vision that was mentioned above (which is a field of inquiry that is largely detached from other vision sciences), exploring vision as a whole must involve an interdisciplinary research where computational inquiry goes hand in hand with behavioral, cognitive, and neuroscience explorations. We therefore complement the development of algorithms for the automatic analysis of images and videos with computational modeling of visual functions, behavioral and psychophysical experimentation with humans and animals, and the computational exploration of physiological and anatomical aspects of visual cortical regions.

    Computer Music Click for more details

    Computer-music concentrates on the involvement of computers in music activities - be it composition, performance, research or education. Computers can be used to compose music, or to assist musicians in performance, improvisation, or investigation of music. Computational methods and techniques such as programming languages, data analysis, knowledge representation, machine learning, algorithmic design, sound analysis and generation are being used for extending, generalizing, investigation and improving traditional music processes. The scope of research in computer music is wide and the methods in use cover a wide variety of computational approaches.

    Digital Humanities Click for more details

    The rapid development of computers and computational tools from the middle of the 20th century is changing the face of science from the ground up. Computational paradigms penetrate almost every scholarly field and generate new collaborations between disciplines, allowing problems whose solutions appeared impossible become resolvable, while new challenges of a whole new scale take their place. Among the most interesting collaborations is Digital Humanities, or Computational Humanities and Social Sciences. This interdisciplinary field attempts to apply computational thinking and computational tools, both theoretical and applied, to challenges that emerge in these two sciences, including the analysis of historic texts, pattern discovery in huge archives, analysis of social networks, solving archaeological and anthropological puzzles, and many others.

    Network Science Click for more details

    Network Science deals with the analysis of Complex networks such as social networks and technological networks. These networks are often characterized by a hierarchical structure, heavy tail degrees distribution, and the small-world property, meaning that the mean distance between pairs of nodes is small relative to the network's size. The research on this area is quite diverse and include both the analysis of complex networks from data and the study of theoretical models aiming to understand how these complex networks gain these properties.

    Operations Research Click for more details

    Time Series Analysis Click for more details

    Time Series Analysis primarily deals with the development of methods and tools for processing time-series data. Generally speaking, any data collection which can be ordered sequentially may be regarded as a time series. There are numerous scientific and engineering examples involving sequential data such as heartbeats of patients, the behavior of stocks, music tunes, etc. Many of the problems in machine learning are also relevant in time series analysis including prediction of future data instances, signal interpolation, model regression for extracting parameters, and much more. Methods and tools originated in the study of time series data are employed in various industries, facilitating the processing and analysis of sequential information.