Luis Rocha's
Current Research Projects

Statement of Research Interests

Complex Adaptive Systems and Computational Intelligence

Almost all interesting processes in nature are highly cross linked. In many systems, however, we can distinguish a set of fundamental building blocks, which interact nonlinearly to form compound structures or functions with an identity that requires more explanatory devices than those used to explain the building blocks. This process of emergence of the need for new, complementary, modes of description is known as hierarchical self-organization, and systems that observe this characteristic are defined as complex . Examples of these systems are: gene networks that direct developmental processes; immune networks that preserve the identity of organisms; social insect colonies; neural, physiological, and technological networks that produce intelligence; ecological networks; social networks comprised of transportation, utilities, and telecommunication systems, as well as economies.

I am interested on basic and applied research on simulations and analysis of complex adaptive systems, as well as on the development of informatics applications to understand and control such systems. Therefore, I work in the interdisciplinary area of systems science and complex systems; more specifically, in the sub-fields of complex adaptive systems (CAS) and computational intelligence (CI). Please check the research group on Complex Adaptive Systems and Computational Intelligence (CASCI) I lead, for a more details about our research and how to collaborate and study with us.

Complex Adaptive Systems

Collective Behavior in Automata Networks

Due to my work dealing with language-like aspects of evolutionary systems (usefulness and characteristics of genotype-phenotype mappings), I became particularly interested on the problem of how information, symbols, representations, computation and the like can arise from a purely dynamical system of many components. Therefore I have been working on the modeling of collective or emergent computation both in biochemical regulation and generic complex dynamical systems. Details of all these projects are described in the Collective Dynamics research thread in my CASCI lab.

Evolutionary Models of RNA Editing

When the process of RNA Editing became well known in 1993 [Benne, 1993], it became quite clear that there were more complicated linguistic-like processes at play in gene regulation in biology. Since then I have been working on computational models to study the evolutionary implications of genotype editing in the living organization. Our latest agent-based model of genotype editing presents a novel architecture for evolving agents in which coding and non-coding genetic components are allowed to coevolve. Details of our models of RNA editing project are available in my CASCI lab.

Artificial Immune Systems for Collective Classification

Recently, we started to work on an agent-based Model of T-Cell Cross-regulation dynamics to produce bio-inspired algorithms for Text Classification such as Spam detection and biomedical article classification. Details of our model of T-Cell Cross-regulation are available in my CASCI lab.

Semi-metric and Complex Network Analysis

While most work on complex networks depends on modeling networks with regular or crisp graphs, many networks of interest are better modeled with weighted of fuzzy graphs. Therefore, I have been interested in the study of transitive properties of complex networks modeled as weighted graphs. In particular, we are studying how semi-metric distance functions inherent in such graphs lead to distinct transitive closures. We are identifying the impact of alternative distance measures on scale free and small-World behavior. We are also developing stochastic models of vertex aging in networks, to better predict network growth. We apply the methodology to various real problems: keyword co-occurrence networks for information retrieval, networks of neuronal activity, etc. Details of my research in Network Analysis of Weighted Graphs are available in my CASCI lab.

Biosemiotics: interplay between self-organization and selection

From my studies with Howard Pattee, I became preoccupied with the observation that while processes of a seemingly informational and indeed linguistic nature are fundamental to evolution in biology, computers which are based on the purely syntactic aspects of language were so non-adaptive. Therefore, I became interested in the linguistic/symbolic aspects of the living organization (the gene as a carrier of information, and DNA as memory) which play a large role in the seemingly open-ended evolution defined by natural selection. This lead me to study the interplay between self-organization and natural selection, introducing the concept of selected self-organization[Rocha ,1996a; Rocha ,1998a]. I became particularly interested on the problem of how information, symbols, representations and the like can arise from a purely dynamical system of many components. Details of my research in biosemiotics and Selected Self-organization in Genotype-Phenotype Maps are available in my CASCI lab.

Computational and Systems Biology

There are today a number of projects aiming at the understanding how the cell regulates collections of genes. Such projects are merging biology with informatics and other areas of computational science. Techniques in information retrieval, text mining, knowledge discovery, machine learning, computational learning theory, and information management present us with the opportunity to make new discoveries in biology. In addition to the research in this area described below, I have also been involved in educational endeavors in this area. I regularly teach an introductory course in Bioinformatics at the Instituto Gulbenkian da Ciencia, Portugal, where I am also in the direction of a PhD Program in Computational Biology and the director of the FLAD Computational Biology Collaboratorium. Additionally, I have advised various graduate students in this area. Likewise, I have long been involved in Artificial Life, having, for instance, recently chaired Alife X and organizing the next ECAL 07, the leading conferences in the field. I also taught what was apparently the first course taught on Artificial Life

Biomedical Literature and Text Mining

Our approach to literature mining is based on bottom-up, data-driven or bio-inspired methods, which we have applied to automatic discovery, classification and annotation of protein-protein and drug-drug interactions, pharmacokinetics numerical data, protein sequence family and structure prediction, functional annotation of transcription data, enzyme annotation publications, and so on. Details of all these projects are described in the bibliome informatics research thread in my CASCI lab.

Modeling of biochemical regulation dynamics

I have been working on the modeling of collective or emergent computation both in biochemical regulation and generic complex dynamical systems. Currently working with models of genetic regulation in yeast , flowering of Arabidopsis thaliana; body segmentation in Drosophila, intracellular signal transduction in fibroblasts, biochemical pathways in granular leukemic lymphocytes, an integrated genome-scale transcriptional and metabolic network for E-Coli, and others. Details of all these projects are described in the Collective Dynamics research thread in my CASCI lab.

Genomic Multivariate Analysis

I have been interested in clustering methods for microarray analysis which allow multiple membership of genes in clusters. In particular, with various collaborators, we became very interested in using spectral analysis, such as Singular Value Decomposition (SVD), as an automated method for Functional Genomics. Details of all these projects are described in the genomic multivariate analysis project in my CASCI lab.

Computational Intelligence

I am generally interested on the problem of how information, symbols, representations and the like can arise from a purely dynamical system of many components. Moreover, I am interested in using design principles from nature, particularly from biological systems dealing with information and memory, to improve information technology. These biologically-inspired design principles are typically distributed, that is, they are organized as complex networks, the characteristics of which I am interested in studying. For these reasons I have been very involved in Embodied Cognition, Bio-inspired computing, and Complex Systems by organizing meetings such as the International interdisciplinary seminar on new robotics, evolution and embodied cognition in Lisbon in 2002, editing a special issue of the Artificial Life Journal on embodied cognition, and being a member of projects such as the Principia Cybernetica Project, etc. I have also been involved in educational endeavors in this area, by teaching a course such as I-590 - Biologically-inspired Computing.

The Adaptive Web and Bio-inspired designs for Recommendation Systems

I have been working on producing intelligent information retrieval methods that can pro-actively cater to the changing demands of user communities. Classic information retrieval algorithms did not effectively use the implicit knowledge accumulated from usage patterns. In other words, information retrieval used to rely exclusively on users pulling the information they needed from a passive environment. More recently, much effort has been posited on developing a different paradigm for information retrieval which relies much more on computational environments that “push” relevant information to users according to previous patterns of information retrieval. This kind of information retrieval has been described as Active Collaborative Filtering, Knowledge Mining, and even Knowledge Self-Organization in distributed information systems (DIS).

I have been developing active recommendation systems for DIS based on adaptive environments which are both collaborative and content-based, as they integrate information from the patterns of usage of groups of users and also categorize database content or semantics in a manner relevant to those groups. One of these systems, TalkMine, further allows the (adaptive) transmission of information across databases, as users may search several databases at the same time. TalkMine entails an open-ended human-machine symbiosis, which can be used in the automatic, adaptive, organization of knowledge in DIS such as library databases or the Internet, facilitating the rapid dissemination of relevant information and the discovery of new knowledge. TalkMine is based on several bio-inspired mechanisms: cognitive categorization as modeled by Fuzzy Set Theory and Dempster-Shafer Theory of Evidence, a connectionist memory architecture, and Hebbian learning for adaptive memory organization.

The recommendation systems I have been investigating aim at the creation of adaptive knowledge networks that facilitate collaboration among individuals, each with various search personalities. Indeed, I have been particularly interested in collaborative systems that are also strong on personalization---preserving distinct search personalities for each user. The systems we have developed aim at the open-ended self-organization and adaptation of DIS to the particular, diverse needs of their users. We refer to these systems as Adaptive Webs. The TalkMine system, in particular, was originally developed for the library without walls project of the Los Alamos National Laboratory research library. A testbed for TalkMine was development under the Active Recommendation Project. Other recommendation systems based on network models have been developed and are currently implemented in the MyLibrary Web Service at Los Alamos. The figure above depicts an associative network of more than a thousand journal titles accessed by the users of MyLibrary, where the links denote strong co-occurrence associations. The figure below depicts the web interface of this service.

Relevant Publications and resources:

Agent-based modeling

The term agent is used today to mean anything between a mere subroutine to a conscious entity. There are "helper" agents for web retrieval and computer maintenance, robotic agents to venture into inhospitable environments, agents in an economy, etc. Intuitively, for an object to be referred to as an agent it must possess some degree of autonomy, that is, it must be in some sense distinguishable from its environment by some kind of spatial, temporal, or functional boundary. It must possess some kind of identity to be identifiable in its environment. To make the definition of agent useful, we often further require that agents must have some autonomy of action, that they can engage in tasks in an environment, independently or without external control. This is, in effect, a definition of agency directly related to the one put forward in the XIII century by Thomas Aquinas: an entity capable of election, or choice.

This is a very important definition indeed; for an entity to be referred to as an agent, it must be able to step out of the dynamics of an environment, and make a decision about what action to take next---a decision that may even go against the natural course of its environment. By this, simplistically, I mean that an agent (say in a steep incline) can opt to go uphill rather than roll with the force of gravity. Since choice is a term loaded with many connotations from theology, philosophy, cognitive science, and so forth, I prefer to discuss instead the ability of some agents to step out of the dynamics of its interaction with an environment and explore different behavior alternatives. In physics we refer to such a process as dynamical incoherence . In computer science, Von Neumann, based on the work of Turing on universal computing devices, referred to these systems as memory-based systems. That is, systems capable of engaging with their environments beyond concurrent state-determined interaction by using memory to store descriptions and representations of their environments. Such agents are dynamically incoherent in the sense that their next state or action is not solely dependent on the previous state, but also on some (random-access) stable memory that keeps the same value until it is accessed and does not change with the dynamics of the environment-agent interaction. In contrast, state-determined systems are dynamically coherent (or coupled) to their environments because they function by reaction to present input and state using some iterative mapping in a state space.

Let us then refer to the view of agency as a dynamically incoherent system-environment engagement or coupling as the strong sense of agency, and to the view of agency as some degree of identity and autonomy in dynamically coherent system-environment coupling as the weak sense of agency. The strong sense of agency is more precise because of its explicit requirement for memory and ability to effectively explore and select alternatives. Indeed, the weak sense of agency is much more subjective because the definition of autonomy, a boundary, or identity (in a loop) are largely arbitrary in dynamically coherent couplings.

I have been working on various types of agent models that are either based on the strong sense of agency detailed above, or attempt to study the emergence of such agency from dynamically coherent environments. The strong sense of agency has been further detailed in an overview of agent models for a previous project of the modeling of socio-technical systems as well as in an overview of research on complex systems modeling I wrote. Examples of agent-based models I have worked on are: the simulations of evolving agents with different kinds of reproduction strategies using Fuzzy Development Programs, the agent-based model of genotype editing, the evolving cellular automata experiments, the soft computing agents for recommendation systems, the immune-inspired spam detection algorithm, etc.

Relevant Publications and resources:

Uncertainty and Generalized Information Theory

I have been working in mathematical models of uncertainty such as Fuzzy Set Theory and the Dempster-Shafer Theory of Evidence (DST). In particular, I developed a set structure named Evidence Sets, which extended Fuzzy Sets with the DST. Evidence sets were developed to address the shortcomings of fuzzy sets as models of linguistic/cognitive categories previously discussed by George Lakoff by providing a set structure capable of dealing better with the contextual nature of cognitive categories while preserving their prototypical effects as observed by Eleanor Rosch. To make evidence sets useful, I developed new measures of uncertainty for continuous domains, since, in their membership degrees, they capture three distinct types of uncertainty: fuzziness, nonspecificity and conflict. I have also used evidence sets and their measures of uncertainty to develop soft computing agents for a digital library and web tool named TalkMine, which is capable of adapting to different user personalities and learning new terms for existing documents. More information about evidence sets is available in a separate page. The figure below depicts a non-consonant evidence set.

Relevant Publications and resources:

Research Team/Lab

Below is the roster of current and previous students and postdocs who work or have worked with me in all the projects above. Please see the site of the Complex Adaptive Systems and Computational Intelligence (CASCI) group I lead, for a more up to date list and additional information. We also have a group page at the Instituto Gulbenkian de Ciencia.

Current Students and Postdocs:

Previous Students and Postdocs (Alumni):

Collaborators and Relevant Research Groups

Below is a group of past and present collaborators, including graduate students, postdoctoral fellows and fellow researchers. I acknowledge all of them as contributing to different elements of the research described above. There is also a list of past and present research groups I am or have been involved with.

People with whom I share at least a publication:

People with whom I am currently working on yet unpublished work:

Present and Past affiliated Research Groups:

For more information contact Luis Rocha at rocha@indiana.edu. Check the Web Design Credits, for due credit.
Last Modified: April 30, 2012