The Algorithmic Foundations (AF) program seeks proposals addressing foundational computer science and engineering research and education to advance the areas of design and analysis of algorithms, computational complexity, and the rigorous experimental study and application of algorithms to other areas within and outside computer science. Broad categories of interest within AF include, but are not limited to:

  • Algorithms – deterministic and randomized, optimal and approximate with performance analysis and approximation guarantees. Resource constraints on time, space, and other resources
  • Algorithms for applications in other areas of computing such as artificial intelligence, databases, languages and compilers, networks and operating systems and other fields such as Biology, Physics, Chemistry, and Engineering, accompanied by their theoretical or empirical analysis
  • Combinatorial and Graph-Theoretic Algorithms – algorithms for classical and new problems, mathematical results in combinatorics with application to algorithm design
  • Computational and Communication Complexity – new techniques, completeness, reductions, relation between complexity classes, new complexity measures, inapproximability
  • Computational Biology – novel algorithmic techniques for protein structure, gene and protein network discovery, sequence analysis, simulation and analysis of biological systems
  • Computational Geometry – geometric algorithms accompanied by rigorous analysis, algorithms with applications to graphics, robust algorithms
  • Cryptography – primitives for privacy, confidentiality, authentication, etc., new protocols, algorithms to break cryptosystems, post-quantum cryptography, side channel attacks, connections with computational complexity
  • Data structures – abstract data types, analysis of classical and new data structures, distributed data structures
  • Machine learning – new algorithmic techniques accompanied by rigorous analysis
  • Models of computation – automata, bounded-action devices, distributed, hybrid, online, parallel, probabilistic, quantum, reactive, sequential, streaming, and other models and relationships between them
  • Numeric, symbolic, algebraic algorithms for scientific computation; correctness proofs, smoothed analysis, symbolic constraint satisfaction, hybrid numeric-symbolic computation
  • Optimization – algorithms with rigorous analysis for linear, convex, and non-linear programming; applications of optimization techniques to combinatorial problems
  • Parallel and distributed computing – new models for computation on heterogeneous multicore and many core processors, memory-hierarchy-aware and memory-hierarchy-oblivious algorithms, parallel and distributed algorithms
  • Quantum information science – new algorithms for computation and communication, their complexity, simulation of quantum systems, study of entanglement, decoherence, error correction and quantum information processing (Note: Proposals on quantum communication may be more appropriate for the CIF program.)