Leandro de Castro Silva, Ph.D.
Expert in artificial intelligence and natural computing Florida Gulf Coast University
- Fort Myers FL
Leandro de Castro Silva's main lines of research are artificial intelligence, natural computing and machine learning,
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Biography
His main lines of research are Artificial Intelligence, Natural Computing and Machine Learning, with applications in Data Science and Optimization. Leandro de Castro is the main author of the book “Artificial Immune Systems: A New Computational Intelligence Approach” (Springer-Verlag, 2002); one of the organizers of “Recent Developments in Biologically Inspired Computing” (Idea Group Publishing, 2004); author of “Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications” (CRC Press, 2006), author of the book “Natural Computing: An Illustrated Journey” (Livraria da Física, 2010), organizer of the book “Nature-Inspired Computing Design, Development, and Application” (IGI-Global, 2012) and main author of the book “Introduction to Data Mining: Basic Concepts, Algorithms and Applications” (Saraiva, 2016, in Portuguese). He was the Founding Editor-in-Chief of the International Journal of Natural Computing Research (IJNCR) between 2010 and 2015, published by IGI-Global. He has more than 250 papers published in national and international journals and conferences.
Leandro also has extensive entrepreneurial experience, having already participated in the founding of three Artificial Intelligence startups and invested, as an angel investor, in other three. Over the past three years (2020-2022) he was recognized as among the 2% most influential researchers in the world based on scientific impact indices monitored by PLoS Biology. He is currently a Visiting Associate Professor at the School of Technology at Unicamp, a Principal Investigator in the Center of Science, Technology and Development for innovation in Medicine and Health hosted at the University of São Paulo, and a Full Professor at Florida Gulf Coast University (FGCU).
Areas of Expertise
Accomplishments
Honorable Mention for Outstanding Innovation and Entrepreneurship, Mackenzie Presbyterian Institute
2019
Honorable Mention for Outstanding Scientific Research, Mackenzie Presbyterian Institute
2019
Medal of the 150th Anniversary of the Foundation of the MPI as a Distinguished Researcher, Mackenzie Presbyterian Institute (MPI)
2021
Education
Catholic University of Santos, Brazil
MBA
Strategic Business Management
2008
Universidade Estadual de Campinas
Ph.D.
Computer Engineering
2001
Universidade Federal de Goiás
B.Sc.
Electrical Engineering
1996
Universidade Estadual de Campinas
M.Sc.
Computer Engineering
1998
Languages
- English
- French
- Portuguese
- Spanish
Selected Event Appearances
Hey Digital! 2.Data and Computational Journalism
Seminar
Brazilian Congress on Information Systems
Congress
19th International Conference on Distributed Computing and Artificial Intelligence
Congress L'Aquila (Italy)
Patents
Automatic system for physical classification of coffee samples and process for their analysis
PI08049033
2010
Selected Articles
An entrepreneurial maturity level assessment methodology: a case study in the business incubator of Mackenzie Presbyterian University
International Journal of Innovation2021
Objective of the study: To propose and evaluate the DIMEP methodology for diagnosing and monitoring the maturity of startups in academic institutions, based on dimensions, subdimensions and maturity levels for evaluating startups at different entrepreneurial journey stages. Methodology: Exploratory, with the proposal of DIMEP, based on the concepts of entrepreneurial journey in academic environments and critical success factors for startups; and its application in a case study to evaluate the entrepreneurial journey promoted in the innovation ecosystem of a Brazilian university. Originality / Relevance: Stimulating student entrepreneurship requires a monitoring methodology throughout the entrepreneurial journey. The literature still lacks well-structured proposals in this regard. DIMEP emerges as an evaluation methodology with demonstrated applicability in this context. Main Results: DIMEP was evaluated during a pre-acceleration program of the university under study, proving to help monitor the projects, allowing an objective assessment of their progress and difficulties, diagnosing particular aspects of each project and the most challenging dimensions for all the program’s startups. Theoretical / Methodological Contributions: A generic methodology for diagnosing the maturity of innovative ventures. Its positive aspects include generality, structuring around a well-defined entrepreneurial journey, the possibility of carrying out qualitative and quantitative analyzes and flexibility to customize different universities’ entrepreneurship journeys. Social / Managerial Contributions: DIMEP has applicability potential in evaluating startups in other institutions, contributing to the development of their innovation and entrepreneurship ecosystems, concerning monitoring the entrepreneurial journey.
Use of artificial intelligence in biblical citation recommendations in the New Testament
Revista Científica Multidisciplinar Núcleo do Conhecimento2023
Religion occupies a prominent place in people’s daily lives and is made explicit to the public or the faithful through preaching or exposition of their sacred texts. The Holy Bible is the religious literature of Christianity, and its text has a unique nature of interpretation and knowledge extraction, that is, through the reading done by specialists (theologians). However, an automated knowledge extraction or that involves some automatic mechanism intelligence to support the interpretation (hermeneutics) of the Biblical text is not observed in the literature. Probably this gap in the literature is caused by the complexity of the biblical textual corpus and the multiplicity of genres it has, being an interpretative challenge even for human specialists. Therefore, this article primarily seeks to build an automated way through artificial intelligence (AI) to provide contextual biblical quotations from the four gospels of the New Testament for the construction of sermons or development of homiletics, which is the art of producing religious sermons for teaching and interpretation of the Biblical message. The methodology used in this article seeks to employ artificial intelligence techniques to implement the proposed solution, that is, a hybrid recommendation system to quote texts from Biblical passages. The AI techniques involved are text mining, natural language processing and supervised learning. Secondarily, this work aims to verify whether the combination of natural language processing techniques and machine learning can provide subsidies for the recovery or extraction of knowledge from complex textual corpus analogous to the biblical corpus. The results show that the proposed hybrid recommendation system is capable of extracting semantic and contextual meaning from the Biblical text, fundamental in the construction of homiletics. The performance evaluation metrics indicate the robustness of the results and consequently validate the findings of this research. Therefore, the combination of these techniques can be extrapolated by the scientific community to aid in the interpretive recovery of complex textual corpus.
An Investigation Into Different Text Representations to Train an Artificial Immune Network for Clustering Texts
International Journal of Interactive Multimedia and Artificial Intelligence2023
Extracting knowledge from text data is a complex task that is usually performed by first structuring the texts and then applying machine learning algorithms, or by using specific deep architectures capable of dealing directly with the raw text data. The traditional approach to structure texts is called Bag of Words (BoW) and consists of transforming each word in a document into a dimension (variable) in the structured data. Another approach uses grammatical classes to categorize the words and, thus, limit the dimension of the structured data to the number of grammatical categories. Another form of structuring text data for analysis is by using a distributed representation of words, sentences, or documents with methods like Word2Vec, Doc2Vec, and SBERT. This paper investigates four classes of text structuring methods to prepare documents for being clustered by an artificial immune system called aiNet. The goal is to assess the influence of each structuring method in the quality of the clustering obtained by the system and how methods that belong to the same type of representation differ from each other, for example both LIWC and MRC are considered grammarbased models but each one of them uses completely different dictionaries to generate its representation. By using internal clustering measures, our results showed that vector space models, on average, presented the best results for the datasets chosen, followed closely by the state of the art SBERT model, and MRC had the overall worst performance. We could also observe a consistency in the number of clusters generated by each representation and for each dataset, having SBERT as the model that presented a number of clusters closer to the original number of classes in the data.
