Bernardo Gonçalves

Biography

I'm doing a new Ph.D. in philosophy (of science and technology, in particular of 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. Since early 2017, I'm a member of the (professional philosophy) Association for Scientiae Studia (in Portuguese). Besides, my computer science research is currently concerned with the semi-automatic construction of knowledge bases (so-called AKBC), which relies on natural language processing and 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 postdoc at the University of Michigan/Ann Arbor under the guidance of Prof. H. V. Jagadish, I have developed a Bayesian smoothing algorithm, Bsmooth, 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'm doing a new Ph.D. in philosophy (of science and technology, in particular of 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. Since early 2017, I'm a member of the (professional philosophy) Association for Scientiae Studia (in Portuguese). Besides, my computer science research is currently concerned with the semi-automatic construction of knowledge bases (so-called AKBC), which relies on natural language processing and 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 postdoc at the University of Michigan/Ann Arbor under the guidance of Prof. H. V. Jagadish, I have developed a Bayesian smoothing algorithm, Bsmooth, 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.

Research Interest

Algorithms and Theory Knowledge Knowledge Discovery and Data Mining Reasoning Statistics