The Internet Computing & Systems Optimization (ICSO) group focuses on the use of Intelligent Algorithms & Data Science (including optimization, simulation, analytics, and machine learning methods) to support complex decision making in different application fields that range from smart cities, to sustainable transportation and logistics, production, real-time positioning, bioinformatics and computational finance. To attain this goal, the group promotes the use of biased-randomized algorithms, simheuristics (combination of metaheuristics with simulation), learnheuristics (combination of metaheuristics with machine learning), and agile-optimization algorithms (combination of biased-randomized algorithms with massive parallel computing). These algorithms allow us to face complex decision making in real-life scenarios characterized by uncertainty and dynamic conditions, as well as by the need of providing real-time solutions.
The group is mainly composed of mathematicians, computer engineers, industrial engineers, statisticians and economists. Our aim is to develop problem solving methodologies and technologies that have a social impact. For that we apply an interdisciplinary approach, considering the sustainable development goals applying a gender perspective.
The Distributed, Parallel, and Collaborative Systems (DPCS) group is focused on the development of distributed and parallel systems at different scales (small groups, clusters, or Internet). In particular, we pay special attention to peer-to-peer, shared memory, message passing computing paradigms, and HPC scientific applications. In addition, we are also interested in the design of collaborative systems and applications.
