Laboratory of Computational Intelligence

My main research area is artificial intelligence methods for decision-support systems, with a focus on interactive problem solving and experiential learning in knowledge-based systems. The emphasis is on representing and utilizing knowledge that captures the semantics and pragmatics of real-world problem solving settings. Special fields of interest, and topics for students, are case-based reasoning, knowledge level architectures, ontologies, knowledge modeling/representation, combined reasoning methods, and AI-related aspects of cognitive science and philosophy. Type of applications focused are in intelligent decision support, knowledge discovery from data, computer-assisted learning/tutoring, and knowledge management. Particular application areas are oil well drilling, medical decision making, and fish farming.

I got my PhD in Computer Science from the University of Essex, UK, in 1997, doing research on data mining, and I have continued to do research on data mining up to the present. In terms of data mining applications, since 2005 my research has been focusing on creating new data mining methods for bioinformatics; initially focusing on the prediction of protein functions, and more recently focusing on classificaton methods for analysing data about ageing-related genes and proteins. In the academic year 2009-10 I did a research-oriented master's degree (MPhil) in Biological Sciences at the University of Liverpool, UK. My MPhil thesis was entitlted: "A Data Mining-Based Approach for Investigating the Relationship Between DNA Repair Genes and Ageing".

Alípio M. Jorge is an associate professor at the Department of Computer Science of the Faculty of Science of the University of Porto and the coordinator of LIAAD , the Artificial Intelligence and Decision Support Lab of UP. LIAAD is a unit of INESC TEC (Laboratório Associado) since 2007. Previously LIAAD was part of LIACC. He is PhD in Computer Science by U. Porto, MSc. on Foundations of Advanced Information Technology by the Imperial College and BSc. in Applied Maths and Computer Science, currently Computer Science (U. Porto). His research interests are Data Mining and Machine Learning, in particular association rules, web intelligence and data mining for decision support. His past research also includes Inductive Logic Programming and Collaborative Data Mining. He lectures courses related to information processing, data mining, decision support and database marketing.

Não informado.

Professor of the Computer Science Department and Researcher at ISISTAN Research Institute at Exact Sciences Faculty of the UNICEN. Coordinator of the Interest Group in Artificial Intelligence. This group is part of SADIO, the Argentine Society for Informatics and Operations Research. Director of the Intelligent Agents Group at ISISTAN Research Institute.

Trabalhei no setor privado e na academia em Portugal, Irlanda,Brasil e no Reino Unido. Eu me especializo em Raciocínio Probabilístico e Processamento de Linguagem Natural. Porém, já trabalhei com outras técnicas em Aprendizado de Máquinas, como variantes de “Deep Learning” e áreas como classificação de imagens. O tópico da minha tese de PhD era mineração de texto, e o meu trabalho de pós-doutorado foi construir Redes Bayesianas via texto para o domínio da cana-de-açúcar. As Redes Bayesianas foram avaliadas no mercado de comodidades de São Paulo. Eu também fui um bolsista de pesquisa no Projeto ROCSAFE H2020 na Universidade Nacional da Irlanda, Galway.

Possui graduação em Ciência da Computação pela Pontifícia Universidade Católica de Minas Gerais (2006), mestrado em Ciências de Computação pelo Instituto de Ciências Matemáticas e de Computação (ICMC) da Universidade de São Paulo (USP), doutorado em Ciências de Computação pelo Instituto de Ciências Matemáticas e de Computação (ICMC) da Universidade de São Paulo (USP) e doutorado em Ciência de Computadores pela Universidade do Porto (Portugal), obtido em regime de dupla titulação. Suas principais áreas de atuação são a Inteligência Artificial, o Aprendizado de Máquina e a Mineração de Dados e Textos. Seu foco atual de pesquisa inclui Agrupamento Semissupervisionado de Dados, Mineração de Textos e Mineração de Dados Educacionais. Atualmente, é Professor Adjunto junto à Faculdade de Computação da Universidade Federal do Mato Grosso do Sul.

Não informado.

His main research interests are in the fields of: linguistic modeling, fuzzy decision making, aggregation, consensus, information retrieval, recommender systems, digital library, web retrieval, web quality and bibliometric.

I am a lecturer in Engineering and Geoscience at the Federal University of Western Pará (UFOPA), Brazil, and an associate lecturer at the State University of Maranhão. I lead the Applied Computing Research Group and coordinate the Laboratory of Applied Computing, both in UFOPA. Currently, I hold a scholarship in Technological Development and Innovative Extension from CNPq. My international experience includes research visits to the University of Kent in the United Kingdom, the University of Leipzig in Germany, and the Universidad Del País Vasco in Spain. I've also served as a Research Project Coordinator for UFOPA's Prorectorate for Research and Innovation. My primary research areas encompass data science, social networks, and electronic markets.

Knowledge extraction based on evolutionary learning, Genetic fuzzy systems, Data Mining, Data preparation, Subgroup discovery, Computing With Words and Decision Making, Bibliometrics, Real Coding Genetic Algorithms, Memetic Algorithms.

Grigorios Tsoumakas received his B.Sc. in Informatics from the Department of Informatics of the Aristotle University of Thessaloniki (AUTh), Greece, in 1999 an M.Sc. in Artificial Intelligence from the Division of Informatics of the University of Edinburgh, UK, in 2000 and a PhD in Machine Learning from the Department of Informatics of AUTh in 2005. He currently is an Assistant Professor at the Department of Informatics of AUTh. His research interests include Machine Learning, Data Mining and Distributed Computing. He is a member of the Hellenic Artificial Intelligence Society and the Special Interest Group on Knowledge Discovery and Data Mining of the Association for Computing Machinery.

Jacson Barbosa received the B.S. degree in Computer Science from the Federal University of Goiás (UFG) (2007), the M.S. in Computer Science from Instituto de Informática (INF) at the Federal University of Goiás (2011), and a Ph.D. degree in Computer Science from the Institute of Mathematical and Computer Sciences (ICMC) at the University of São Paulo (USP) in 2021. He is a professor at the Federal University of Goiás (UFG), Brazil. His research interests are software engineering, mining software repositories, data and text mining, and machine learning.

I'm a researcher at LIAAD - INESC TEC, the Laboratory of Artificial Intelligence and Decision Support of the University of Porto.

I am an associate professor at the Department of Computer Science of the Faculty of Sciences of the University of Porto (UP). I am the current director of the Master's Degree in Network and Information Systems Engineering at this Faculty. I am an associate professor at the Department of Computer Science of the Faculty of Sciences of the University of Porto (UP). I am the current director of the Master's Degree in Network and Information Systems Engineering at this Faculty.

Currently I'm an Associate Professor at Computer Science Department and Director of the ISISTAN Research Institute of the UNICEN University at Tandil, Argentina. Also I'm a research fellow of the National Council for Scientific and Technical Research of Argentina ( CONICET ) and Coordinator of the Informatics Area of the Na t ional Agency for Scientific Research Promotion ( FONCyT ). My research interests include intelligent aided software engineering, software architecture and frameworks, agent technology and software visualization.

Possui graduação em Bacharelado Em Estatística pela Universidade Estadual de Campinas (1987), mestrado em Engenharia Elétrica pela Universidade Estadual de Campinas (1992) e doutorado em Ciencia da Computacao pela Universidade de São Paulo (2009). Atualmente é pesquisadora do Centro Nacional de Pesquisa Tecnológica em Informática para a Agricultura desde 1989. Tem experiência na área de Probabilidade e Estatística, com ênfase em Mineração de Dados e Textos, atuando principalmente nos seguintes temas: analise de dados, mineração de textos, estatística experimental, desenvolvimento de software científico (Python, R, C++ e Java).

Prof. Nada Lavrac is Head of the Department of Knowledge Technologies (since 2004), was Head of Intelligent Data Analysis and Computational Linguistics research group (in 1999-2003) at the Department of Intelligent Systems, and researcher of Jožef Stefan Institute, Ljubljana, Slovenia (since 1978). She is Full Professor at University of Nova Gorica and Deputy Head of Information and Communication Technologies Program at Jozef Stefan International Postgraduate School. She was visiting professor at Bristol University, UK (1997-2002, teaching parts of courses Introduction to Machine Learning and Learning from pguctured Data) and at Klagenfurt University, Aupgia (1987-2002, teaching courses on Knowledge Acquisition, Data Mining and Decision Support).

Nitin Indurkhya first coined the term "Big Data" in a predictive data mining book that he co-authored with Sholom Weiss in 1998. He isDr. Nitin Indurkhya the founder of AI Data-Miner LLC, which has been engaged in data-mining, text mining, language technologies consulting and education since 1997. The company developed and markets several data-mining and text-mining toolkits, including the Enterprise Data-Miner toolkit (the first data-mining software to gain the 100% pure Java certification). He is an adjunct faculty member at the School of Computer Science and Engineering at the University of New South Wales in Sydney, Australia. He was director of Cloud Research Lab at Samsung Research America and had been Principal Research Scientist at eBay in Silicon Valley. Dr. Indurkhya worked at AT&T Bell Labs.

Pavel Brazdil is a member of LIAAD Inesc Tec, where he conducts most of his research. Pavel Brazdil is a founder of a strong Machine Learning / Data Mining group that exists since 1988 and now is a part of LIAAD Inesc Tec (Laboratory of AI and Decision Support). Since 1988 he is Full Professor (Prof. Catedrático) at the Faculty of Economics (FEP) of University of Porto where he has been teaching courses on Information systems, Data Mining and Text Mining. He has supervised 12 PhD students. Although he has officially retired in mid-July 2015, he continues his R&D activities, including teaching at Master and Doctoral courses (e.g. MAPi, MADSAD) and supervision of post-graduate students. Pavel Brazdil is a Fellow of ECCAI, European Coordinating Committee for AI (since 2008). There more than 3000 citations to his work on Google Scholar and his h-index there is 26.

Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. An internationally leading researcher in the areas of mining highly structured data and the evaluation and improvement of machine learning models using ROC analysis, he has also published on the logic and philosophy of machine learning, and on the combination of logic and probability. He is author of Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012).

Possui graduação em Informática pelo Instituto de Ciências Matemáticas e de Computação pela Universidade de São Paulo com ênfase em Sistemas de Apoio à Tomada de Decisão, e mestrado e doutorado em Ciências de Computação e Matemática Computacional pelo Instituto de Ciências de Computação e Matemática Computacional da Universidade de São Paulo na linha de pesquisa Inteligência Computacional. Desde 2007 tem atuado em projetos de Mineração de Dados e Textos. Mais especificamente, já atou nas áreas de extração de tópicos, segmentação de textos, extração de informação, pré-processamento de textos, e aprendizado supervisionado e semissupervisionado para classificação de textos, músicas, aspectos e sentimentos. Tem trabalhado como revisor para periódicos e conferências internacionais e nacionais na área de inteligência computacional. Atualmente é docente da Universidade Federal de Mato Grosso do Sul (UFMS).

Rodrigo Martinez-Bejar, Spanish computer engineer. Achievements include research in knowledge-based systems, knowledge acquisition, modelling and management, intelligent agents, natural language processing and knowledge discovery.

Possui graduação em Bacharelado em Ciência da Computação pela Universidade de São Paulo (2001), mestrado em Ciências da Computação e Matemática Computacional pela Universidade de São Paulo (2003) e doutorado em Ciências da Computação e Matemática Computacional pela Universidade de São Paulo (2006). Atualmente é professor adjunto da Universidade Federal do ABC, onde coordenou o programa de pós-graduação em Ciência da Computação de fevereiro de 2011 a março de 2015, elaborando a proposta de criação do mestrado (2011) e doutorado (2015) do programa. Tem experiência na área de Ciência da Computação, com ênfase em Inteligência Artificial, atuando principalmente nos seguintes temas: descoberta de conhecimento, aprendizado de máquina, mineração de dados, tratamento de classes desbalanceadas e inteligência artificial.

Following his Ph.D., Stan was an Assistant Professor in the Department of Mathematics and Computer Science, Warsaw University. He joined University of Guelph in 1977, and Acadia University in 1980. Since 1981 at the University of Ottawa, as of 2011 a Distinguished University Professor (on leave). For many years in charge of graduate studies in Computer Science at the University of Ottawa, and a founding father of the Graduate Certificate in Electronic Commerce at University of Ottawa in 1999. Also affiliated with the Institute for Computer Science of the Polish Academy of Sciences as a Professor, Stan has worked at universities in the U.S, Europe, and Latin America. Recognized internationally for his work in text mining, applications of Machine Learning, and data privacy, author and co-author of more than 250 research paper.

Não informado.

He holds a Ph.D. in Computer Science, a Master's degree in Computer Science, and a Bachelor's degree in Information Systems, all from the Faculty of Computing (FACOM) at the Federal University of Mato Grosso do Sul (UFMS). Currently, he is a Professor at the Federal University of Mato Grosso do Sul. He has extensive experience in the development of Web and Mobile applications, as well as in the management of software projects and technological innovation. He is the coordinator of the Scientific Computing and Innovation Lab (LivES). Since 2007, he has been managing and participating in technological innovation and research projects. His expertise lies in Computer Science, Information Systems, Software Engineering, and Artificial Intelligence, with a primary focus on topics such as Web and Mobile Engineering, Machine Learning, Artificial Intelligence Engineering, Opinion Mining, Data-Driven Software Engineering, Software Reusability, Software Product Lines, and Frameworks.

Professor Vapnik gained his Masters Degree in Mathematics in 1958 at Uzbek State University, Samarkand, USSR. From 1961 to 1990 he worked at the Institute of Control Sciences, Moscow, where he became Head of the Computer Science Research Department. He then joined AT&T Bell Laboratories, Holmdel, NJ, having been appointed Professor of Computer Science and Statistics at Royal Holloway in 1995.