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marcio:home [2020/06/28 22:45]
argollo
marcio:home [2020/06/28 23:09] (atual)
argollo
Linha 9: Linha 9:
 * Visiting Professor (Jan. 05 - Aug. 05), UFF, Rio de Janeiro, Brazil.\\  * Visiting Professor (Jan. 05 - Aug. 05), UFF, Rio de Janeiro, Brazil.\\
 * Associate Professor (Dec. 06- ), [[http://www.if.uff.br |IF-UFF]], Rio de Janeiro, Brazil  * Associate Professor (Dec. 06- ), [[http://www.if.uff.br |IF-UFF]], Rio de Janeiro, Brazil
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-===== Research ===== 
-I have been introduced to the fascinating field of statistical mechanics of complex systems at the beginning of my undergrad years. I have learned many interesting problems like the traveling salesman problem, memory storage and retrieval in attractor neural networks, complex networks and other biologically-inspired problems. Some of these studies resulted in [[http://scholar.google.com/citations?user=hdWhavkAAAAJ|publications]]. 
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-The key ideas and tools to study such phenomena are the concepts of universality and the statistical analysis of toy models. 
You can find my CV [[http://lattes.cnpq.br/2172046254229187|here]]. You can find my CV [[http://lattes.cnpq.br/2172046254229187|here]].
-I take part in a team effort developing [[http://pseudomonas.procc.fiocruz.br|a computational model for Pseudomonas aeruginosa]], a multi-drug resistant pathogen associated with a broad spectrum of infections in humans. My focus is on the reconstruction of its metabolic network.+During my MSc years we developed a strategy to increase pattern recognition in attractor neural networks. The idea was to reduce the similarity of memories, replacing each one with the pattern resulting from successive iterations of a (pseudo)chaotic cellular automaton rule. We also proposed a new dynamics that improved the Hopfield-Tank approach for solving the traveling salesman problem. Advantages of a neural-network implementation of the latter problem are their direct in-silico and optical implementations.
 +My PhD thesis is divided in two parts. One deals with small-world networks, their geometric properties and the critical behavior of the average shortest-path length near zero addition of long-range bonds. We prove that the transition from regular to random behavior is first order. On the second part of the thesis nonequilibrium phase transitions are studied. We define a lattice model of structural rigidity and a translation of the latter problem into a reaction-diffusion one, finding numerical estimates of the set of critical exponents of the model.
 +===== Research =====
 +I have been introduced to the fascinating field of statistical mechanics of complex systems at the beginning of my undergrad years. I have learned many interesting problems like the traveling salesman problem, memory storage and retrieval in attractor neural networks, complex networks and other biologically-inspired problems.
-During my MSc years we developed a strategy to increase pattern recognition in attractor neural networks. The idea was to reduce the similarity of memories, replacing each one with the pattern resulting from successive iterations of a (pseudo)chaotic cellular automaton rule. We also proposed a new dynamics that improved the Hopfield-Tank approach for solving the traveling salesman problem. Advantages of a neural-network implementation of the latter problem are their direct in-silico and optical implementations. +Some of these studies resulted in [[http://scholar.google.com/citations?user=hdWhavkAAAAJ|publications]].
- +
-My PhD thesis is divided in two parts. One deals with small-world networks, their geometric properties and the critical behavior of the average shortest-path length near zero addition of long-range bonds. We prove that the transition from regular to random behavior is first order. On the second part of the thesis nonequilibrium phase transitions are studied. We define a lattice model of structural rigidity and a translation of the latter problem into a reaction-diffusion one, findind numerical estimates of the set of critical exponents of the model. +
 +Currently, I take part in a team effort developing [[http://pseudomonas.procc.fiocruz.br|a computational model for Pseudomonas aeruginosa]], a multi-drug resistant pathogen associated with a broad spectrum of infections in humans. My focus is on the reconstruction of its metabolic network.
===== Teaching ===== ===== Teaching =====
marcio/home.txt · Última modificação: 2020/06/28 23:09 por argollo
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