CV Lattes
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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Luis Gustavo Nonato has a degree in mathematics from Universidade Estadual Paulista Júlio de Mesquita Filho - Brazil (1991), master (1994) and PhD. (1998) degrees in Applied Mathematics from Pontifícia Universidade Católica do Rio de Janeiro - Brazil. He was a visiting scholar at Scientific Computing and Imaging Institute - University of Utah (2008-2010) and a visiting professor at the New York University (2016--2018). He is currently full professor at Universidade de São Paulo. Gustavo has experience in Data Science, Visualization, Geometry Processing, and Applied Mathematics. (Source: Lattes Curriculum)
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The Data Science Center for Smart Industry acts as an advanced data analytics hub for industry, aiming to improve operational efficiency, product quality, innovation and sustainability through the application of data science and analytics techniques of data in industrial processes. Contact with industries will be mediated by SENAI and operationalized through workshops and thematic meeting…
Since the 1970s, Brazilian society has been undergoing a process of transition from military dictatorship to democracy. With this political transition, it was expected that conflicts would be progressively resolved, reducing violence. However, that didn't happen. Indeed, the transition was accompanied by an explosion of internal conflicts, much of which associated with urban crime. There …
The focus of this proposal is the transfer of mathematical knowledge to other areas of science, technology and industry, through a structured research center specially tailored for this purpose. All mathematical knowledge is ultimately applicable, if not directly, through other areas of knowledge. In some areas the application of mathematics is almost immediate. However, the practical imp…
In many areas of the Sciences and Engineering, images provide important information about real problems and the assignment by a machine of one or multiple labels per image (annotation) leads to a decision about the problem. However, the machine learning process requires the manual isolation and identification (label assignment) of the content of interest, named sample, in training images.…
This project aims at developing new techniques and paradigms toward contributing to the progress of scientific and abstract data visualization, having as main focus the processing and handling of high-dimensional, multimodal time-varying data. The new visual paradigms to be developed should create visual metaphors from multidimensional projection and phylogenetic trees, allowing for under…
During the visit of Professor Geovan Tavares we will investigate and develop techniques for processing massive amounts of data, focusing mainly on the visualization and modeling of music data. The goal is to study the theory of non-negative matrices and the possible ramifications of such a theory in the context of modeling and visualization of massive data, mainly targeting music database…
(Only some records are available in English at this moment)
Crime in Brazil presents growing challenges for public administration. Among these, accessing, understanding, and explaining impunity is essential for improving the system. This scientific initiation project proposes the study and development of inferential and predictive methods for the analysis and modeling of crime data in the state of São Paulo, with an emphasis on impunity. Bayesian …
Between 2000 and 2006, the Center for the Study of Violence at USP (NEV-USP) developed the "Study of Criminal Impunity in the Municipality of São Paulo" (1988-1997) within the scope of the CEPID/FAPESP program. This research analyzed the flow of the criminal justice system, based on the monitoring of homicide occurrences from the initial police report to the investigations and legal proce…
In recent years, studies in criminology have increasingly considered urban infrastructure as a crucial factor that can influence the occurrence of crimes. However, few have specialized in analyzing the road network as a possible determinant for the incidence of these occurrences in certain locations. In this context, this project aims to present a computational approach to analyze the inf…
(Only some records are available in English at this moment)
A recurring question in the analysis of the temporal evolution of crimes is the correlation between Urban Segments (SU), such as the industrial sector, central area, high, medium and low standard regions, and the most common Crime Types (TC), for example , homicides, thefts, among others. Another relevant issue in this context is to understand what the criminal evolution associated with e…
Undeniably, the crime situation in large urban areas is becoming increasingly serious. The contribution of science with more accurate and effective methods to predict and prevent occurrences that affect the safety of society grows every day. Machine learning and data science have played a key role in this public safety context. With the advent of Deep Neural Networks (DNN), the expectatio…
Crime patterns appear from the interaction among millions of individuals (e.g., criminals, citizens, police) acting in a medium (e.g., city, road networks, neighborhoods). Often agents' collective behavior is complex but shaped by the medium's design - as the medium limits the range of possible actions each agent can take. We study how to predict and visualize effects of (causal) interven…
(Only some records are available in English at this moment)
Responsible AI is a hot topic nowadays. One of the issues related to this topic is the need to understand predictions made by predictive models. In particular, models used in the health and criminal field need to be reliable and trustworthy. In other words, the model's accuracy is not the only important thing, understanding the model's behavior against input data is also essential. In ord…
Analyzing origin-destination flows is an important problem that has been extensively investigated in several scientific fields, in particular by the visualization community. The problem becomes especially challenging when involving massive data, demanding mechanisms such as data aggregation and interactive filtering to make the exploratory process doable. However, data aggregation tends t…
The main goal of our PhD project is to develop a new crime forecasting methodology based in graph signal processing and deep learning by modeling São Paulo city in a natural street-level manner, based not only in past historical crime data, but also in other social and urban information that might enhance predictions. It is undeniable that crimes in Brazil have not only grown in recent ye…
(Only some records are available in English at this moment)
| 3 / 3 | Ongoing grants |
| 11 / 7 | Completed research grants |
| 6 / 6 | Ongoing scholarships in Brazil |
| 32 / 18 | Completed scholarships in Brazil |
| 7 / 7 | Completed scholarships abroad |
| 59 / 41 | All research grants and scholarships |
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