The Mathematics of Complex Systems:
Theory and Applications

January 19-31, 2026 | Recife, Brazil

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About the School

🏛️

Host Institution

A growing mathematics department with expertise in:

  • Nonlinear dynamics
  • Differential equations
  • Topological methods
  • Algebra
  • Celestial Mechanics

📍 Campus location: Rua Dom Manuel de Medeiros, Recife

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Support Institutions

Local Partners

  • Universidade Federal de Pernambuco (UFPE)

International Partners

  • University of Amsterdam (UVA)
  • Dutch Institute For Emergent Phenomena (DIEP)
  • Korteweg-de Vries Institute for Mathematics (KDVI)

We are prospecting other institutional partners.

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Objectives

  • Develop local research capacity in applied mathematics
  • Foster North-South scientific collaborations
  • Advance research in complex systems
  • Promote mathematical education
  • Promote gender balance in STEM (43% women participants)

Team

📋 Organizing Committee

Strategic Planning & Logistics

👩🏫

Dr. Rubia Araújo

Local Coordinator

Adjunct Professor
Department of Mathematics
UFRPE, Brazil

🔬

Dr. Fernando A.N. Santos

External Coordinator

Assistant Professor
Korteweg-de Vries Institute for Mathematics, UvA, Netherlands

🤝🏛️

Dr. Hélio Teixeira Coelho

Institutional Articulation

Full Professor, UFPE
Member of ABC & APC

📚

Dr. Marcelo Piropo

Team Member

Adjunct Professor
Department of Mathematics
UFRPE, Brazil

📊

Dr. Pablo Rodriguez

Team Member

Adjunct Professor
Department of Statistics
UFPE, Brazil

👩📚

Dr. Paula Cadavid

Team Member

Adjunct Professor
Department of Mathematics
UFRPE, Brazil

⚙️🖥️📊

Dr. Leon Silva

Team Member

Associate Professor
Department of Mathematics
UFRPE, Brazil

🎓 Teaching Team

Course Leaders & Practitioners

🔬

Dr. Fernando A.N. Santos

Topological Data Analysis

Assistant Professor
University of Amsterdam (UvA)

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Dr. Danilo B. de Souza

Epidemiological Modeling

Postdoctoral Fellow
BCAM - Basque Center for Applied Mathematics, Spain

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Dr. João Gondim

Applied Epidemiology

Adjunct Professor
Department of Mathematics
UFPE, Brazil

🧮

Dr. Luiz Felipe Martins

Statistical Mechanics

Associate Professor
Department of Mathematics
Cleveland State University, EUA

🧬

Dr. Fernanda Selingardi

Computational Neuroscience

Assistant Professor
Universidade Federal de Alagoas, Brazil

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Dr. Leonardo Gollo

Computational Neuroscience

Associate Professor
Campus Universitat de les Illes Balears.
Palma de Mallorca. Spain

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Dr. Marilyn Gatica

Computational Neuroscience

Postdoctoral Research Assistant
Network Science Institute
Northeastern University London, UK

CIMPA

Dr. Suzanne Touzeau

Scientific Officer

Quality Assurance & Academic Supervision
Centre International de Mathématiques Pures et Appliquées

Courses

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Mathematical Modeling in Neuroscience

Instructors:Fernanda Selingardi (UFAL)

Keywords: Neural dynamics, synchronization phenomena, criticality in neural networks, brain network analysis, differential equations, dynamical systems

Practical Focus: Hands-on modeling of neural systems using Python.

Description:

This course explores mathematical models used to understand, model and analyse neural dynamics and brain function. Topics include, but are not restricted This course explores mathematical models used to understand, model and analyse neural dynamics and brain function. Topics include, but are not restricted to, brain network analysis, applications of topology in neuroscience, synchronisation phenomena, criticality in neural networks, and applications of differential equations and dynamical systems to neuroscience. Participants will learn about the latest research and develop skills in modelling and analysing neural systems, both theoretically and numerically.to, brain network analysis, applications of topology in neuroscience, synchronization phenomena, criticality in neural networks, and applications of differential equations and dynamical systems to neuroscience. Participants will learn about the latest research and develop skills in modelling and analysing neural systems, both theoretically and numerically.

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Topological Data Analysis in Complex Systems

Instructor: Fernando A.N. Santos (UvA)

Keywords: Persistent homology, computational topology, simplicial complexes, high-order interactions, real-world applications

Practical Focus: Analysis of complex datasets from neuroscience, epidemiology, and finance using Python libraries (GUDHI, Ripser).

Description:

Participants will learn about topological methods for analysing complex data. The course includes, but is not limited to, concepts such as simplicial complexes, persistent homology, computational topology, and high- order interactions along with their applications in understanding the structure and dynamics of complex systems. Practical sessions will involve applying these methods to real-world data, such as epidemics, neuroscience, and finance, to name a few. Students will learn modelling and numerical skills (in Python) in topological data analysis.

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Mathematical Modeling of Epidemics

Instructors: Danilo Souza (BCAM), João Gondim (UFPE)

Keywords: Compartmental models, network analysis, stochastic processes, parameter estimation, disease control strategies

Practical Focus: Simulation of disease outbreaks using SIR/SEIR models and network-based approaches in Python.

Description:

This course introduces mathematical models in epidemiology, focusing on the spread of infectious diseases. It includes, but is not restricted to, classic epidemic models, compartmental models, stochastic processes, parameter estimation, anddisease control strategies for and prevention. Case studies on recent epidemics will provide practical insights, both theoretical and in numerical analysis.

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Markov Decision Processes and Reinforcement Learning

Instructors: Luiz Felipe Martins (CSU)

Keywords: Markov Decision Processes, Reinforcement Learning, Sequential Decisions, Robotics, Industrial Automation, Health Care, Finance, Game Playing, TorchRL

Practical Focus: Application of TorchRL to create and optimize MDP models.

Description:

A Markov Decision Processes (MDP) is a model for an agent making sequential in an environment. The agent’s actions influence the evolution of the environment, and garners rewards. The goal of the agent is to choose actions in a way to maximize overallrewards. Reinforcement Learning is a methodology to solve these problems that approximately mimics how humans learn: the agent chooses actions, observes results, and updates behavior accordingly. Applications of RL include robotics, industrial automation, health care, finance and game playing. This course is an introduction to both the mathematical framework and computational techniques necessary to create MDP models and use RL to find optimal solutions. In the applied section of the course, students will use TorchRL to create and optimize a specific MDP model.

Research Talks & Thematic Sessions

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Algebra

Organizers: Bárbara Costa (UFRPE) & Rodrigo Gondim (UFRPE)
Description

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1 [Speaker] [Institution]
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2 [Speaker] [Institution]
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Differential Geometry

Organizers: Fábio Reis (UFPE) & Jorge Hinosoja (UFRPE) & Jogli Gidel (UFRPE)
Description

This thematic session will address central aspects of differential geometry, including the global theory of submanifolds and the intrinsic theory of manifolds. These theories naturally emerge as developments from the study of differentiable surfaces in three-dimensional Euclidean space and today form a consolidated and expanding field that investigates the intrinsic geometric properties of manifolds as well as objects immersed in Riemannian or semi-Riemannian manifolds, known as ambient spaces. By definition, submanifolds inherit properties from the spaces in which they are embedded, but they also possess their own intrinsic geometry. Therefore, the study of their geometry can be conducted from two complementary perspectives: intrinsic geometry, which considers only the induced metric, and extrinsic geometry, which analyzes how the submanifold is immersed in the ambient space. Both perspectives are connected through the second fundamental form of the immersion. Throughout this session, topics such as immersions and submersions in Riemannian and semi-Riemannian manifolds, curvature theories, and applications of analytical techniques to obtain global results on submanifolds will be explored. Classical and recent examples will also be discussed to illustrate the relevance of studying submanifolds in the broader context of differential geometry, including connections with mathematical physics and global theory.

1 [Speaker] [Institution]
[Presentation Title]

This session will explore recent advances in differential geometry with applications to complex systems. We will discuss geometric methods for analyzing dynamical systems, manifold learning techniques, and applications to data science. The session will include presentations on Riemannian geometry, symplectic structures, and geometric approaches to machine learning.

2 [Speaker] [Institution]
[Presentation Title]

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Mathematical Analysis

Organizers: Eudes Mendes (UFRPE) & Felipe Wergete Cruz (UFPE)
Description

This session will present recent developments in PDEs and Geometric Analysis, including new results, techniques, and applications, with a focus on current trends.

1 [Speaker] [Institution]
[Presentation Title]

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2 [Speaker] [Institution]
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Dynamical Systems

Organizers: Eduardo Leandro (UFPE) & Marcelo Pedro (UFRPE) & Thiago Dias (UFRPE) & Anete Soares (UFRPE)
Description

The Dynamical Systems thematic session shall focus on the applications of Dynamical Systems in Classical and Celestial Mechanics, with an emphasis on several aspects of periodic (specially relative equilibrium) solutions and central configurations. In a broader sense, the interplay with areas such as Algebra, Analysis and Geometry will be explored in order to approach some of the most challenging research problems in current times.

1 [Speaker] [Institution]
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2 [Speaker] [Institution]
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Complex Systems in Physics and Statistics

Organizers: Viviane Oliveira (UFRPE) & Paulo Duarte Neto (UFRPE)
Description

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Combinatorics

Organizers: Gabriel Coutinho (UFMG) & Luiz Paulo Moreira (UFPE)
Description

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1 [Speaker] [Institution]
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Neurosciences

Organizers: Fernanda Selingardi (UFAL)
Description

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1 [Speaker] [Institution]
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2 [Speaker] [Institution]
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Stochastic Modeling

Organizers: Pablo Rodriguez (UFPE)
Description

Stochastic modeling originated from efforts to understand problems in areas like statistical physics, population dynamics, genetics, and communication systems. Over time, it has evolved into a broad set of methods for analyzing random structures and interacting systems, providing a powerful tool for studying complex phenomena in the social, applied, and natural sciences. This session aims to unite young and experienced experts from different backgrounds to showcase recent work, exchange ideas, and promote further research and collaboration.

1 [Speaker] [Institution]
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2 [Speaker] [Institution]
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Schedule

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xx:00 - xx:00
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xx:00 - xx:00
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xx:45 - xx:15
Talk

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📢 Schedule subject to changes - Last update: 25/01/2026

Practical Information

📍 Venue

  • UFRPE Campus
    Rua Dom Manuel de Medeiros, S/n Dois Irmãos, Recife - PE

  • UFPE Campus
    Av. Prof. Moraes Rego, 1235 Cidade Universitária, Recife - PE

🏨 Accommodation

Shared options available near campus

Registration

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Contact

📧

Email

For inquiries, please contact us at:

cimpaschoolrecife@gmail.com
📍

Address

Universidade Federal Rural de Pernambuco (UFRPE)
Rua Dom Manuel de Medeiros, s/n
Dois Irmãos, Recife - PE, Brazil
CEP: 52171-900