Bernardo Gonçalves  Bernardo Gonçalves photo         

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Research staff member
IBM Research, Brasil
  +55dash11dash21323921

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Professional Associations:  ACM

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More information:  Ph.D. in Computational Modeling (LNCC'2015)

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I'm doing a second Ph.D., in philosophy, specifically the philosophy of science and technology (with a focus on Artificial Intelligence), at the University of São Paulo. I study the Turing Test, its ontological and pragmatic implications for what it means these days to be a human being (or a machine), and for the societal impact of AI. I'm a member of the (professional philosophy) Association for Scientiae Studia, and the Association for the Philosophy and History of Science of the Southern Cone.

My computer science research is currently concerned with the semi-automatic construction of knowledge bases (so-called AKBC) for specific domains, which relies on natural language processing and also generally available information sources like Wikidata, Wordnet, FrameNet etc. I'm also an expert on a variety of knowledge engineering, data quality, and ETL (for data warehousing) problems, towards answering keyword, natural language, or structured queries based on high-quality (trustable) structured data. Please find my publications at DBLP. (For Brazilians, here's my CNPq Lattes: http://lattes.cnpq.br/3537386106760841).

At IBM Research Brasil, I work under the leadership of Dr. Renato Cerqueira on the design and application of AI techniques to serve the natural resources industry. In my 1-year postdoc at the University of Michigan (the top-5th CS department in the world) under the guidance of (h-index 88) Prof. H. V. Jagadish, I have developed a (machine learning) Bayesian smoothing algorithm, for the disambiguation of search and natural language queries issued against a relational database, by building on information available from the database schema and a user-interaction log. In my Ph.D. at the National Laboratory for Scientific Computing (LNCC)/Brazil, supervised by Prof. Fabio Porto, I have developed a technique, named Y-DB, to extract synthetic scientific datasets from competing mathematical models (seen as alternative hypotheses, and given in MathML), and then generate a probabilistic relational database whose structure is defined automatically with correctness guarantees. The "magic" here is to unveil the implicit structure in a mathematical model and translate it automatically onto a probabilistic relational model (so-called U-relations). As part of that research, I have fixed the status of a classical AI algorithm on causal reasoning proposed in the 1950's by Nobel-laureate Herbert Simon. 

I earned my Ph.D. in Computational Modeling (with focus on Data Science) in January 2015 from the National Laboratory for Scientific Computing (LNCC) in Brazil. Recently my thesis has been nominated to the 2016 nation-wide edition of 'Prêmio CAPES de Teses'. During my Ph.D. I've been awarded an IBM PhD Fellowship (2013-14), and a FAPERJ 'Bolsa Nota 10' PhD Distinguished Scholarship (2013-15). I also hold a M.Sc. and a B.Sc. in Computer Science from the Federal University of Espirito Santo (UFES) in Brazil.