These are the areas of research that this Research area is involved with:
Computational Biology and Bioinformatics. Our approach to Computational Biology is grounded in a Systems Biology perspective.
We believe that biological regulatory mechanisms, including gene expression and protein interactions, cannot be fully understood by mere
identification of components, products, emsembles, and connections. Therefore, we wish to supplement the surplus of gene and protein
sequence information now available, with
network models built from the integration of information from several sources. For instance, we are involved in the prediction of
protein function from gene sequence, structure and textual information. We are also interested in studying multigenic function,
interactions in genetic regulatory networks, Microarray data analysis, computer modeling of RNA Editing, and understanding the
representation and communication of information in
living systems at large.
Network and Multi-Agent Modeling. Simulation of social structures that emerge from individual agent interactions
(e.g. the Web, markets, organizations, cultures, languages). We study the structure, dynamics and knowledge exchanges of communities
of agents. We are interested in computer models capable of capturing a wide range of levels: from physical dynamics to individual belief.
We look particularly at the emergence and prediction of social trends. Related to this research goal is the study of Economics.
We are interested in models of the economy as composed of large numbers of interacting and adapting agents endowed with decision-making
abilities.
Adaptive Computation. New adaptive computational techniques based on ideas drawn from adaptive systems in nature:
natural evolution, the brain, immune systems, and social systems such as insect colonies or economic markets.
We are interested in using the adaptive mechanisms found in nature as design principles for computing architectures.
Understanding such mechanisms via computer simulation, theoretical
modeling, and mathematical analysis leads to new insights about natural adaptive behavior, and conversely about efficient
computing paradigms. Related to this interest is the study of global coordination and emergent computation, and evolutionary
computation and optimization.
Distributed Learning. Design of systems capable of adaptive, open-ended, learning based on
interacting agents which share and trade knowledge in an environment. Models we are interested in range from Artificial Swarms, Artificial Immune Systems,
Evolutionary Algorithms, to Adaptive Web Agents endowed with Reinforcement Learning and Knowledge Structures.
