Tiago Pereira da Silva

COVID-19, Mathematics

A vivid image from mathematician Tiago Pereira da Silva’s childhood is running barefoot down the street, as shoes were considered a luxury, reserved for school. Until he attended university, the furthest he had traveled was to an aunt’s home, 200 km from his own. Today, however, his scientific pursuits have taken him around the globe.

Despite earning a degree in physics from the University of São Paulo, Tiago chose to follow a career in mathematics. He completed his doctorate in nonlinear dynamics at the University of Potsdam, Germany. His post-doctoral journey led him to prestigious institutions such as Imperial College London in England, the Institute of Pure and Applied Mathematics in Rio de Janeiro, the Humboldt University of Berlin, Germany, and the Helmholtz Center for Mind and Brain Dynamics, also in Germany. In addition to his role as a professor at the State University of São Paulo, Tiago enjoys playing soccer in his leisure time.

Open Calls

Covid-19 emergency fund
Science Call 1
Array
(
    [0] => WP_Post Object
        (
            [ID] => 12500
            [post_author] => 6
            [post_date] => 2023-11-27 16:07:26
            [post_date_gmt] => 2023-11-27 16:07:26
            [post_content] => This project aims to formulate a mathematical theory to shed light on emergent behavior in complex networks of non-linear dynamic systems. Examples of such complex networks include the brain, power grids, social networks, protein networks, and sensors in smart cities. These networks exhibit a mixed global behavior, with significant phenomena occurring on finite time scales. Consequently, conventional tools are inadequate for studying these systems. This proposal seeks to bridge this gap by developing a theory for emergent phenomena in complex networks. This could unlock vast potential, such as the ability to reconstruct system rules based on their behavior. If successful, this research could enable the prediction of critical transitions and the prevention of catastrophes.
            [post_title] => Predicting Critical Transitions: A reconstruction of complex networks
            [post_excerpt] => 
            [post_status] => publish
            [comment_status] => closed
            [ping_status] => closed
            [post_password] => 
            [post_name] => predicting-critical-transitions-a-reconstruction-of-complex-networks
            [to_ping] => 
            [pinged] => 
            [post_modified] => 2024-09-02 18:57:46
            [post_modified_gmt] => 2024-09-02 18:57:46
            [post_content_filtered] => 
            [post_parent] => 0
            [guid] => https://serrapilheira.org/projetos/predicting-critical-transitions-a-reconstruction-of-complex-networks/
            [menu_order] => 0
            [post_type] => projeto
            [post_mime_type] => 
            [comment_count] => 0
            [filter] => raw
        )

)

Projects

Predicting Critical Transitions: A reconstruction of complex networks
Science / Mathematics

This project aims to formulate a mathematical theory to shed light on emergent behavior in complex networks of non-linear dynamic systems. Examples of such complex networks include the brain, power grids, social networks, protein networks, and sensors in smart cities. These networks exhibit a mixed global behavior, with significant phenomena occurring on finite time scales. Consequently, conventional tools are inadequate for studying these systems. This proposal seeks to bridge this gap by developing a theory for emergent phenomena in complex networks. This could unlock vast potential, such as the ability to reconstruct system rules based on their behavior. If successful, this research could enable the prediction of critical transitions and the prevention of catastrophes.

Amount invested

1st phase: R$ 100,000.00
2nd phase: R$ 996,000.00 (R$ 696. 000,00 + R$ 300.000,00 optional bonus for the integration and training of people from underrepresented groups in science)
  • Topics
  • Catastrophes
  • cérebro
  • complex networks
  • critical transitions
  • non-linear dynamic systems
  • Power networks
  • Smart cities