Iowa State University

Iowa State UniversityIowa State University
Vasant Honavar

Department of Computer Science

Vasant Honavar: Research Interests

Honavar's research and teaching interests cut across Computer Science, Information Science, Cognitive Science, and Bioinformatics.

His research is driven by fundamental scientific questions or important practical problems such as the following:

  • What are the information requirements and algorithmic basis of learning in specific scenarios?
  • What are the information requirements and algorithmic basis of inter-agent communication, multi-agent interaction, coordination, and organization?
  • How can we develop sophisticated machine learning algorithms for knowledge acquisition from richly structured data (sequences, images, graphs, etc.)?
  • How is information encoded, stored, retrieved, decoded, and used in macromolecular, neural, and cognitive systems?
  • How can we discover the relationships between macromolecular sequence, structure, expression, interaction and macromolecular function?
  • How can we construct accurate predictive models of signaling networks involved in cellular development, differentiation, and biological function?
  • How can we query and use information from heterogeneous, distributed, autonomous data and knowledge sources?
  • How can we build useful predictive models from large, distributed, semantically heterogeneous, autonomous data sources?
  • How can we develop software environments for collaborative development, sharing, and use of large, complex, ontologies?
  • How can we support the design, assembly and execution of complex web services using autonomously developed components?
  • How can we represent and manipulate scientific knowledge in a form that lends itself to automated processing by the computer and at the same time, is comprehensible by, and communicable to humans?

Current Research Interests

  • Artificial Intelligence: Intelligent agent architectures, Multi-agent organizations, Inter-agent interaction, and Multi-agent coordination, Logical, probabilistic, and decision-theoretic knowledge representation and inference, Neural architectures for knowledge representation and inference, Computational models of perception and action
  • Bioinformatics, Computational Molecular Biology, and Computational Systems Biology: Data-driven discovery of macromolecular sequence-structure-function-interaction-expression relationships, identification of sequence and structural correlates of protein-protein , protein-RNA, and protein-DNA interactions, protein sub-cellular localization, automated protein structure and function annotation, modeling and inference of genetic regulatory networks from gene expression (micro-array, proteomics) data, modeling and inference of signal transduction and metabolic pathways.
  • Data Mining: Design, analysis, implementation, and evaluation of algorithms and software for data-driven knowledge acquisition, data and knowledge visualization, and collaborative scientific discovery from semantically heterogeneous, distributed data and knowledge sources, Applications to data-driven knowledge acquisition tasks in bioinformatics, medical informatics, geo-informatics, environmental informatics, chemo-informatics, security informatics, social informatics, critical national infrastructure (communication networks, energy networks) e-government, e-commerce, and e-science.
  • Machine Learning: Statistical, information theoretic, linguistic and structural approaches to machine learning, Learning and refinement of bayesian networks, causal networks, decision networks, neural networks, support vector machines, kernel classifiers,, multi-relational models, language models (n-grams, grammars, automata), Learning classifiers from attribute value taxonomies and partially specified data; Learning attribute value taxonomies from data; Learning classifiers from sequential and spatial data; Learning relationships from multi-modal data (e.g., text, images), Learning classifiers from distributed data, multi-relational data, and semantically heterogeneous data; Incremental learning, Ensemble methods, multi-agent learning, selected topics in computational learning theory.
  • Semantic Web: Ontology-based user and query-centric approaches to information integration and acquisition of sufficient statistics for learning from data under different access and resource constraints from heterogeneous, distributed, autonomous, ubiquitous information sources, sensor networks, peer-to peer networks; description logics, collaborative ontology design, ontology tools, ontology-extended information sources, ontology-extended workflow components, ontology-extended agents and services, semantic workflow composition.
  • Other Topics of Interest: Biological Computation . Evolutionary, Cellular and Neural Computation, Complex Adaptive Systems, Sensory systems and behavior evolution, Language evolution, Mimetic evolution; Computational Semiotics. Origins and use of signs, emergence of semantics; Computational organization theory; Computational Neuroscience; Computational models of creativity, Computational models of discovery.