Research

Research groups
Numbers
Publications

Research groups

Albcom

The primary mission of ALBCOM is to do research in computation and formal methods, broadly. It is organized into four broad research groups:

More specific research topics include, parallel and sequential algorithms, complex networks, verification, combinatorics, complexity, data structure, game theory, algorithms for VSLI design, metaheuristics and other approximate algorithms for tackling combinatorial optimization problems, graph theory, and logic verification.

GIE

The GIE conducts research in Computer Graphics, Solid Modeling, and applications built on top of these technologies. The goal is to develop interactive, computer-based systems where the user interacts through a graphics interface. Specifically, the GIE works on:

  • Parametric solid modeling
  • Brep models based on triangulations
  • Modeling and visualization in medical and bioengineering applications
  • Geographical information systems

GPLN

The Natural Language Processing Group (GPLN) began its activity in the CS department in 1988 and it is been an interdiciplinary group since the beginning. Nowadays consists on about thirty people and counts on the regular collaboration of the linguistic research group from Barcelona University.

In 1993 and 1995, the group was considered as part of the High Quality Research Group (GRQ93-3015, GRQ1995-566) by the Research and Technology Interdepartmental Commission (CIRIT) of la Generalitat de Catalunya. Since 1997, the group is been considered as a consolidated research group in all the Catalan Research Strategy Editions (1997-SGR-00051, 1999-SGR-00150, 2001-SGR-0000254, 2005-SGR-0130).

The tasks we develop are among others:

  • Analysis and desambiguousness processes (morphology, syntaxes, semantics)
  • Natural language automatic and learning
  • Automatic translation
  • Automatic summaries
  • Information extraction
  • Knowledge representation
  • Lexico-semanthic knowledge acquiring
  • semi-automatic lexico-semantiques ontology building

In 1999, GPLN and GPS group from Signal theory and Communications Department from UPC unified to create the research centre TALP (Tecnologia i Aplicacions del Llenguatge i la Parla).

KEMLG

The main goal of Knowledge Engineering and Machine Learning Group (KEMLG) is the analysis, design, implementation and application of several Artificial Intelligence techniques, to support the operation or behaviour analysis of real-world complex systems or domains. The research is focussed on the analysis, design, management or supervision of these domains, such as in the health and medical field, in environmental processes and systems, and in the industrial and enterprise sector. Main research effors are done in the following directions:

  • Computational Argumentation
  • Data Mining and Knowledge Discovery
  • Electronic Institutions and Multi-agent Systems
  • Intelligent Decision Support Systems
  • Machine Learning
  • Organization Theory and Multi-agent Systems
  • Reasoning within temporal domains

Specific research efforts are undertaken in analysis and development of intelligent agents, understanding of coalition setting dynamics, social structure dynamics analysis, formal model construction for norms and conventions for e-commerce, information flow process design, temporal episode-based reasoning, experience-based argumentation techniques, hybrid methods in Statistics and Artificial Intelligence, belief or bayesian networks, case-based reasoning, knowledge-based systems, supervised and unsupervised machine learning techniques, knowledge model identification and knowledge model building, knowledge representation, ontologies, social networks, semantic web, web services, and directory service study.

LARCA

LARCA is an international research group working on data mining, machine learning, data analysis, and mathematical linguistics. We typically approach problems from sound mathematical principles, using modelling tools and techniques from algorithmics, computational complexity, automata theory, logic, discrete mathematics, statistics, and dynamic systems. We are also starting partnerships with companies and other institutions to apply our solutions to real-world problems.

Research at the group can be roughly divided into four lines:

  • Data mining and machine learning: Predictive and explanatory models, recommender systems, knowledge discovery in databases,computational learning theory, analysis of structured, non-relational data: sequences, trees, graphs, linguistic data, XML analysis
  • Massive data analysis with emphasis on data streams: Efficient algorithms, scalability, real-time analytics, mining with limited resources
  • Applications of data mining techniques to specific domains: Social network analysis, finances, healthcare data, utility data, computer system performance analysis, transportation networks, sports analytics, ecological data
  • Mathematical linguistics: Logic-based approaches to linguistic analysis, quantitative linguistics, language evolution and acquisition

 

LOGPROG

MOVING

The MOVING (Modeling, Visualization and INteraction in Graphics) group performs research, education and training activities in:

  • Visualization
  • Geometric and volume modelling
  • Physically-based animation
  • Virtual reality
  • Advanced interaction

The Moving activity is aimed at the generation and transference of relevant research results in fields like virtual reality, immersive interaction and modelling, visualization of complex n-dimensional information and advanced 3D graphic interaction systems for achieving efficient and usable systems. The objective of the Moving group is to train new researchers and PhD students in these fields while keeping international cooperation on research activities and research projects.

SOCO

The Soft Computing Group is active in the area of research on Soft Computing systems since 1997. It is formed by several researchers of the CS (Computer Science) Department at the UPC (Universitat Polit├Ęcnica de Catalunya). The activities of this research group can be summarized in the following research lines:

  • Supervised and Unsupervised Neural Networks
  • Fuzzy Systems
  • Genetic Algorithms and Evolutionary Strategies
  • Support Vector Machines
  • Feature Selection and Extraction
  • Pattern Recognition
  • Computer Vision

Numbers

Live data from: http://futur.upc.edu/CS#resum-grafic


Publications

Live data from: http://futur.upc.edu/publications

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