Prof. Piotr Jedrzejowicz
Gdynia Maritime University
Dr. Piotr Jedrzejowicz is a professor of information systems and head of the Information Systems Department, Gdynia Maritime University. His research interests include artificial intelligence, machine learning, agent technology, combinatorial optimization, and decision support systems. Prof. Jedrzejowicz has published 4 books and over 200 papers in the international scientific journals and proceedings. Prof. Jedrzejowicz is an elected member of the Committee of the Computer Science, Polish Academy of Science and member of the Scientific Council, Polish Society of Artificial Intelligence. For the current term, he is also an elected chairman of the Polish Chapter IEEE SMC Society. During his career, Prof. Jędrzejowicz has been a visiting professor in Germany, Great Britain, China, Sweden, and a research fellow at the School of Computer Science, McGill University, Montreal. He has been leading several research projects and serving as a general chair, program chair and program committee member in numerous international scientific conferences. He has been invited to edit several special numbers of international scientific journals. He is an editor of the Hindawi/Wiley periodical "Mathematical Problems in Engineering", and also a member of the Steering Committee of the ICCCI conference series.
Current trends in the population-based optimization
Population-based methods are used to deal with computationally difficult optimization problems. The outcome of the current research effort in the field of the population-based optimization can be broadly categorized as a new, stand-alone, P-B metaheuristics, ensemble P-B metaheuristics including multi-population and multi-agent approaches, new hybrid approaches involving P-B metaheuristics, and improvements and successful modifications of the earlier known P-B metaheuristics. Research results obtained during the last few years in each of the above categories are briefly discussed. Some of the most interesting approaches are presented in a more detailed manner. The last part of the talk includes comments on directions of future research in the population-based optimization.
Prof. Costin Bădică
University of Craiova, Romania
Costin Bădică is Professor at the Department of Computers and Information Technology, Faculty of Automation, Computers and Electronics, University of Craiova, Romania since 2006. He obtained the Ph.D. in Automatic Control in 1999. The research of Prof. Costin Bădică is at the intersection of Artificial Intelligence, Software Engineering and Distributed Systems. He obtained results in applied formal representations and reasoning, as well as intelligent distributed multi-agent systems, platforms and programming. Prof. Costin Bădică co-initiated the “Intelligent Distributed Computing – IDC” series of conferences. Prof. Costin Bădică is Founding Member of Romanian Association of Artificial Intelligence. He organized as Guest Editor many international journal special issues and he managed many national and international research projects.
Agent and optimization for collective profitability, freight transportation brokering and structured project scheduling
The talk will be focused on methods and applications of agent systems in combination with mathematical optimization and constraint logic programming in various areas, including collective profitability, freight transportation brokering and structured project scheduling. The talk will include theoretical and experimental results, highlighting challenges and limitations of our approaches.
Prof. Nicolas Spyratos
Paris-Sud (Paris-Saclay) University, France
Nicolas Spyratos is an Emeritus Professor at the University of Paris-South, Scientific Advisor of the Japan Science and Technology agency (JST) and member of the National Council of Research and Innovation of Greece. Prior main assignments include being a full professor of Computer Science at the University of Paris-South (1983-2011), Researcher at INRIA and then scientific advisor (1976-1985) and researcher at the Canadian Bell-Labs (Bell Northern Research, 1974-1976). His research interests include database theory, conceptual modeling, digital libraries and more recently data analytics. He has published extensively in international journals and conferences in his areas of research.
HIFUN – A High Level Functional Query Language for Big Data Analytics
We present HIFUN, a high level query language for defining analytic queries, independently of how these queries are evaluated. We use four operations on functions, namely composition, pairing, function restriction and Cartesian product projection. These operations combine functions to create HIFUN queries in much the same way as the operations of the relational algebra combine relations to create algebraic queries. The contributions of this work are: (a) the definition of a formal framework in which to study analytic queries in the abstract; (b) the encoding of HIFUN queries either as Map Reduce jobs or as SQL group-by queries; and (c) the definition of a formal method for rewriting HIFUN queries and its mapping to rewriting Map Reduce jobs and SQL group-by queries.
Prof. Eneko Agirre
University of the Basque Country, Spain
Eneko Agirre is Professor at the University of the Basque Country and member of the IXA Natural Language Processing group and the HiTZ reseach center. His research focuses on lexical and computational semantics, with applications in information retrieval and machine translation. He has produced more than 100 peer-reviewed articles. He has been president of the ACL SIGLEX, member of the editorial board of Computational Linguistics, and has received two Google research awards. He is currently an action editor of the TACL journal.
Cross-linguality and machine translation without bilingual data
Machine translation is one of the most successful text processing application. Current state-of-the-art systems leverage large amounts of translated text to learn how to translate, but is it possible to translate between two languages without having any bilingual data? In this presentation we will show that this is indeed the case. We will first map the word embedding spaces of two languages to each other, with and without seed bilingual dictionaries. This allows to produce accurate bilingual dictionaries based on monolingual corpora alone, with the same quality as supervised methods. Based on these mappings, it is then possible to train machine translation systems without accessing any bilingual data.