Symbolic Computation

• computer algebra

• symbolic techniques applied to numerics

• hybrid symbolic and numeric algorithms

• numerics and symbolics for geometry

• programming with constraints, narrowing

Numerical Computing

• iterative approximation of fixed points

• solving systems of nonlinear equations

• numerical and symbolic algorithms for differential equations

• numerical and symbolic algorithms for optimization

• parallel algorithms for numerical computing

• scientific visualization and image processing

Logic and Programming

• automatic reasoning

• formal system verification

• formal verification and synthesis

• software quality assessment

• static analysis

• timing analysis

Artificial Intelligence

• intelligent systems for scientific computing

• recommender and expert systems for scientific computing

• scientific knowledge management

• agent-based complex systems modeling and development

• uncertain reasoning in scientific computing

• computational intelligence

• soft computing

• machine learning

• data mining, text mining and web mining

• natural language processing

• computer vision

• intelligent hybrid systems

Distributed Computing

• parallel and distributed algorithms for clouds, GPUs, HPC,P2P systems, autonomous systems. Work should focus on scheduling,scaling, load balancing, networks, fault-tolerance, gossip algorithms, energy saving

• applications for parallel and distributed systems, including work on cross disciplinary (scientific) applications for grids/clouds, web applications, workflow platforms, network

• measurement tools, programming environments

• architectures for parallel and distributed systems,including self-managing and autonomous systems, negotiation protocols, HPC on clouds, GPU processing, PaaS for (inter)cloud,  brokering platforms, mobile computing

• modelling of parallel and distributed systems including models on resources and networks, semantic representation,negotiation, social networks, trace management, simulators

• any other topic deemed relevant to the field

Advances in the Theory of Computing

• Data Structures and algorithms

• Combinatorial Optimization

• Formal languages and Combinatorics on Words

• Graph-theoretic and Combinatorial methods in Computer Science

• Algorithmic paradigms, including distributed, online, approximation, probabilistic, game-theoretic algorithms

• Computational Complexity Theory, including structural complexity, boolean complexity, communication complexity, averagecase complexity, derandomization and property testing

• Logical approaches to complexity, including finite model theory

• Algorithmic and computational learning theory

• Aspects of computability theory, including computability in analysis and algorithmic information theory

• Proof complexity

• Computational social choice and game theory

• New computational paradigms: CNN computing, quantum, holographic and other non-standard approaches to Computability

• Randomized methods, random graphs, threshold phenomena and typical-case complexity

• Automata theory and other formal models, particularly in relation to formal verification methods such as model checking and runtime verification

• Applications of theory, including wireless and sensor networks, computational biology and computational economics

• Experimental algorithmics