Computational Biology (Bioinformatics)
Computational methods for advancement of modern biology and medicine: Understanding biological systems using combinatorial and statistical models. Analysis of biological systems and networks. Models for understanding disease, human mutation and evolution. Development of algorithms for analysis of very high volume biomedical data, including gene expression, sequence variation, protein interactions and metabolic networks.
Development and implementation of efficient algorithms and research in Structural Biology, such as structural comparison of proteins, biomolecular recognition, computer-aided drug design and protein folding. Formulation of these problems in a solid computational framework which allows application of graph-theoretical, string matching, and geometric matching algorithms.
Historical Document Analysis
Digital humanities; large-scale manuscript analysis; manuscript reconstruction; computational paleography.
Natural Language Processing
Machine translation. Semantic analysis.
Computational Geometry and Robotics
Computational and Combinatorial Geometry and Application
Design and analysis of efficient algorithms for basic geometric problems and their applications to robotics, computer graphics, computer vision, image processing, pattern recognition, geographical data processing, solid modeling and computer-aided design, VLSI design, statistics, and operations research. Combinatorial analysis of geometric structures.
Algorithmic motion planning; Assembly planning and automated manufacturing; Design and implementation of integrated robotics systems; Robot kinematics, kinematics structures with many degrees of freedom.
Computational Neuroscience and Machine Learning
Biologically motivated learning and visual preprocessing related to brain functions, high order elements and their application; Neural computation; Medical and biological applications of neural computation; Various aspects of computational learning theory, including statistical learning and statistical parameter estimation, learning complexity analysis, machine learning.
Visual Computing, Geometric modeling and Computer Graphics
Algorithms for image and texture synthesis, digital surface geometry, digital photography, image and video processing, geometric and graphics modeling.
Computer Vision and Pattern Recognition
Human face recognition. Analysis of acoustical signals. Texture discrimination. Motion detection Analysis and segmentation of noisy range images. Cluster analysis. Model selection. Object recognition for robotics and CAD/CAM applications. Recognition of partially occluded objects. Articulated objects, 3D scene recognition.
Advanced database applications, including data integration, object-oriented and semi-structured information, Web-based applications, Big Data, Data Provenanace and the interaction between textual information and databases. Study of data models, query processing and optimization (and algorithms thereof).
Image and Signal Processing
Use of wavelets for signal and image processing and other numerical applications; Compression of still and animated images; Adaptive denoising for speech and textured images; Feature detection and discrimination from wavelet dictionaries. Machine learning tools for signal morphology analysis, with applications to EEG, MEG, fMRI, and heart sounds.
Algorithms for dealing with different systems of calendar date and time of day.
Applications: Data Mining & Search
Economics and Computation