SYNASC 2022 : 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

SYNASC aims to stimulate the interaction among multiple communities focusing on defining, optimizing and executing complex algorithms in several application areas. The focus of the conference ranges from symbolic and numeric computation to formal methods applied to programming, artificial intelligence, distributed computing and computing theory. The interplay between these areas, in fact, is essential in the current scenario where economy and society demand for the development of complex, data intensive, trustable and high performant computational systems.

In this context we invite for

research paper submissions
special session proposals
satellite workshop proposals
tutorial proposals

Submitted research papers must contain original research results and should not be submitted or published elsewhere.

There are four categories of submissions:

Regular papers describing fully completed research results (up to 8 pages in the two-columns paper style).
System descriptions and experimental papers describing implementation results of experimental data, with a link to the reported results (up to 4 pages in the two-columns paper style).
Work in progress papers, describing ongoing work and/or preliminary results (up to 4 pages in the two-columns paper style).
PhD students short papers, describing ongoing work and research challenges of PhD students (up to 4 pages in the two-columns paper style).

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List of Topics

SYNASC is organized within six tracks:

Symbolic Computation:
-- computer algebra
-- symbolic analysis
-- symbolic combinatorics
-- symbolic techniques applied to numerics
-- hybrid symbolic and numeric algorithms
-- numerics and symbolics for geometry
-- programming with constraints, narrowing
-- applications of symbolic computation to artificial intelligence and vice-versa

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
-- automated testing

Distributed Computing:
-- modelling of parallel and distributed systems
-- parallel and distributed algorithms
-- architectures for parallel and distributed systems
-- applications for parallel and distributed systems
-- acceleration of AI or Big Data applications using distributed and parallel computing
-- networked intelligence and Internet of Things

Artificial Intelligence:
-- knowledge discovery, representation, and management
-- automated reasoning, uncertain reasoning, and constraint strategies
-- recommender and expert systems
-- intelligent systems, agents, and networks
-- agent-based complex systems
-- AI-based systems for scientific computing
-- machine learning – including deep learning models and technologies
-- explainable and trustworthy AI
-- information retrieval, data mining, text mining and web mining
-- computational intelligence - including fuzzy, neural and evolutionary computing
-- AI applications: natural language processing, computer vision, signal processing, stock market, computational neuroscience, robotics, autonomous vehicles, medical diagnosis, cybersecurity, digital design, online education, algorithm invention and analysis

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, average-case 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