Vasant Honavar
Location: (Ames, Iowa)
Personal Research Web Page: http://www.cs.iastate.edu/~honavar/
Keywords: Artificial intelligence, Machine Learning, Data Mining, Description Logics, Semantic Web, Ontologies, Knowledge Representation, Probabilistic Models, Bioinformatics, Computational biology, Data Mining, Service Composition, e-science, Systems Biology
Posted on: Thursday, May 20th, 2010
Broad Research Area: AI / Machine Learning / Robotics / Vision, Databases / Information Retrieval / Data Mining, Scientific/Medical Informatics, Social Computing / Social Informatics
Research Interests:
(a) Bioinformatics and Computational Molecular 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, prediction of functionally important sites in proteins, modeling and inference of genetic regulatory networks from gene expression (micro-array, proteomics) data, modeling and inference of signal transduction and metabolic pathways, comparative analysis of macromolecular interaction networks.
(b) 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 and health informatics, energy informatics, ecological informatics chemo-informatics, security informatics, social informatics, e-government, and e-science.
(c) Knowledge Representation and Inference: Logical, probabilistic, and decision-theoretic knowledge representation and inference; Representing and reasoning about preferences. Secrecy or privacy preserving query answering from knowledge bases; Federated knowledge bases and inference algorithms; Description logics; Neural architectures for knowledge representation and inference, Computational models of perception and action
(d) 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.
(e) 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; description logics, federated knowledge bases, modular ontology languages, web service composition.
(f) 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.
