Organized by

CSP-SIG-Mathematical Aspects of Computer Science (SIG-MACS) and 

Logic and Computability Lab, DCS - UPDiliman

Workshop on Models, Algorithms, Computability and Discrete Structures  (WMACS) is part of the initiative of the CSP – Special Interest Group on Mathematical Aspects of Computer Science (SIG-MACS) to promote Models of Computations, Algorithmics, Computation Theory and Discrete Structures in the country.  This is one of the pre-conference Workshop of PCSC 2026 at Immaculate Concepcion University at Davao  City.

WMACS is consist of lectures on fundamental areas of computer science which are mathematical in character. The lectures will be synoptic and tutorial in nature and will cover topics from courses that are sometimes offered as upper division undergraduate courses and some introductions to emerging techniques and computing models in computer science.

WMACS provides a venue for teachers, researchers and graduate students of Computer Science, Computing, and Mathematics to share, upgrade knowledge, and foster active collaborative research on areas of Computer Science which are mathematical or theoretical in character. In this edition, we have the following:

Tutorials (tentative):

Models, Computability and Complexity

Henry Adorna (UP Diliman)

Spiking neural P systems: parallel and brain-inspired model of computation

Francis Cabarle (UP DIliman)

A tutorial on spiking neural P systems (in short, SNP systems) is given in a few hours. SNP systems are part of the latest generation (gen3) of neuron models based on spiking (biological) neurons. The human brain has served as inspiration for models of computation since the start of the 20th century, including finite automata and artificial neurons. The gen3 models are closer to biological neurons, with timing of the spikes gaining further importance and role in the computations.

Computations in SNP systems put greater emphasis on spike timing in the computation: a common way to receive the output n, where n is some natural number, is if a designated output neuron spikes the first time at step x and the second time at step y, the output n = x - y or the interval between the firing of the spike by the output neuron.

SNP systems are both powerful and efficient: they are powerful in the sense of Turing machines, i.e. SNP systems are algorithms capable of general purpose computation for problem solving; they are efficient in the sense that SNP systems can solve computationally difficult problems in reasonable (polynomial) time. 

The tutorial can be split in three parts: part 1 is an introduction to preliminaries for SNP systems such as regular expressions, followed by definitions of syntax, semantics, examples of SNP systems; part 2 is an introduction to the computing power and efficiency of SNP systems; part 3 includes further examples, applications, exercises, or research directions on SNP systems, variants, and related models.

Reference:

Leporati, A., Mauri, G. & Zandron, C. Spiking neural P systems: main ideas and results. Nat Comput 21, 629–649 (2022). https://doi.org/10.1007/s11047-022-09917-y 

Introduction to Recursion Theory

Alfonso Labao (UP Diliman)

The workshop provides an introduction to some of the core notions of computability or recursion theory. It first provides an introduction to basic computability concepts under the Turing Machine paradigm. Afterwards, it introduces the use of primitive recursive functions over the natural numbers and shows how the very same computability concepts can be relativized under the functional paradigm. The workshop concludes with short previews on other possible paradigms for which one can do recursion theory.

Reference:

Hinman, PG, "Recursion Theoretic Hierarchies", Cambridge University Press, 2017

Short Presentations (tentative):

Selected 15-minute informal (short paper) presentation or discussions on some research done and/or research-in-progress on the areas of covered by the workshop (please see list of topics below) may  be accommodated after the tutorial sessions. 

Topics of Interest (but not limited to) : Computability, Automata Theory and Formal Languages, Complexity Theory, Theory of Algorithms, Parallel Algorithms, Numerical Analysis, Combinatorial Optimization, Network Theory, Logic and Logic Programming, Theory of Programming Languages, Coding Theory, Cryptography and Computer Security, Discrete Mathematic for Computer Science, Computational Mathematics, New Challenges (BioInformatics, Heuristics, P Systems, Quantum Computation etc.).

Important Dates:

1. Abstract Submissions: 30 March 2026

Please send Abstract to ha@dcs.upd.edu.ph  on or before 30 March 2026.

2. Acceptance/Rejection Notification: 06 April 2026

3. Workshop MACS: 23 April 2026

For Inquiries please contact:

Henry Adorna, UPD <hnadorna@up.edu.ph>