Iowa State University

Iowa State UniversityIowa State University

College of Liberal Arts and Sciences

Department of Computer Science

Vasant Honavar
Professor

Office: 211 Atanasoff
Phone: (515) 294-1098
Fax: (515) 294-0258
Email: honavar@cs.iastate.edu
Homepage: http://www.cs.iastate.edu/~honavar/homepage.html

Current Affiliations

Biographical Sketch

Dr. Vasant Honavar received a B.E. in Electronics Engg. from Bangalore University, India, an M.S. in Electrical and Computer Engg. from Drexel University, and an M.S. and a Ph.D. in Computer Science from the University of Wisconsin, Madison. He founded (in 1990) and has been the director of the Artificial Intelligence Research Laboratory at Iowa State University (ISU) where he is currently a professor of Computer Science. He directs the Center for Computational Intelligence, Learning & Discovery which he founded in 2004. Honavar is on the faculty of interdepartmental graduate programs in Bioinformatics and Computational Biology, Human-Computer Interaction, Neuroscience, and Information Assurance. He has served as the associate chair (2001-2003) and chair (2003-2005) of the Bioinformatics and Computational Biology Graduate Program. Honavar's research and teaching interests include Artificial Intelligence: Machine Learning, Knowledge Representation, Machine Perception, Intelligent Agents and Multi-agent systems; Bioinformatics and Computational Biology; Data Mining, Information Integration, Neural Computation, Systems Biology; Semantic Web, Service-Oriented Computing, Environmental Informatics, Security Informatics, Social Informatics, Honavar has published over 200 research articles in refereed journals, conferences and books, and has co-edited 6 books. He has served as a co-editor-in-chief of the Journal of Cognitive Systems Research (1999-present) and a member of the Editorial Board of the Machine Learning Journal (2002-2005), the International Journal of Data Mining and Bioinformatics (2005-2008), the International Journal of Semantic Web and Information Systems (2007-present), the International Journal of Computational Biology and Drug Design (2007-present), the Journal of Bioinformatics and Biology Insights (2007-present), the International Journal of Functional Informatics and Personalized Medicine (2008-present), and the International Journal of Computer and Information Security (2004-present). Honavar has served as a chartered member of the National Institutes of Health study section on Biological Data Management and Analysis (2002-2007). Prof. Honavar has received several honors and awards including the Board of Regents Award for Faculty Excellence (2007), the College of Liberal Arts and Sciences Award for Outstanding Achievement in Research (2008). Prof. Honavar is a senior member of the Association for Computing Machinery (ACM), a senior member the Institute of Electrical and Electronic Engineers (IEEE), and a member of the Association for Advancement of Artificial Intelligence (AAAI), the American Medical Informatics Association (AMIA), the International Society for Computational Biology (ISCB) and the New York Academy of Sciences.

Research Interests

Professor Honavar's research interests include:

  • 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 and computational models of memory, learning, motivation and reward, knowledge representation and inference, perception and action.
  • Bioinformatics 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 and Information Integration: 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, ontology design, ontology tools, ontology-extended information sources, ontology-extended workflow components, ontology-extended agents and services, semantic workflow composition.
  • Selected Topics in: Biological Computation, Evolutionary, Cellular and Neural Computation, Complex Adaptive Systems, Sensory systems and behavior evolution, Language evolution, Mimetic evolution, Computational Semiotics including origins and use of signs, emergence of semantics; Computational organization theory, Computational Neuroscience, Computational models of creativity, Computational models of discovery.


The laboratory's research is funded in part by grants from the National Science Foundation, the National Institutes of Health, the US Department of Agriculture, and Iowa State University.


Additional information about current projects in the Artificial Intelligence Research Laboratory can be found on the projects and publications pages.

Research Areas

Artificial Intelligence, Intelligent Agents and Multiagent Systems, Bioinformatics and Computational Biology, Complex Adaptive Systems, Information Integration and Information Retrieval, Distributed Computing and Networks, Information Security, Machine Learning and Data Mining, Multi-Agent Systems, Neural Networks and Evolutionary Computation, Software Systems, Computational Learning Theory, Human Computer Interaction, Semantic Web

Research Statement

Honavar's research interests cut across Computer Science, Information Science, Statistics, Cognitive Science, and Biological Sciences. This research is driven by fundamental scientific questions or important practical problems such as the following:

  • What is the algorithmic basis of learning in specific scenarios?
  • What are the information requirements of inter-agent communication, multi-agent interaction, coordination, and organization?
  • How can we develop sophisticated machine learning algorithms for knowledge
  • 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,
  • 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?


Selected Research Accomplishments


Research Contributions in Machine learning, Data mining and Computational Learning Theory


  • Development of scalable algorithms for learning predictive models (e.g., decision trees, bayesian network classifiers, support vector machines) from autonomous, distributed data sources using statistical queries with proven performance guarantees relative to their centralized counterparts (with Ph.D. student Doina Caragea)

  • Development of algorithms for learning comprehensible predictive models from data and prior knowledge in the form of attribute value taxonomies (with Ph.D. students Jun Zhang, Adrian Silvescu, and Flavian Vasile

  • Development of algorithms for learning predictive models (e.g., decision tree and Bayesian network classifiers) from partially specified data, i.e., data at varying levels of abstraction (with Ph.D. student Jun Zhang)

  • Development of algorithms for learning a family of sequence classifiers with applications in computational biology and computer security (with Ph.D. students Carson Andorf, Adrian Silvescu, and Dae-Ki Kang)

  • Development of discriminatively trained probabilistic models for sequence classification (with Ph.D. student Oksana Yakhnenko

  • Development of generalized multiple instance learning algorithms with applications in bioinformatics, text and image analysis (with Ph.D. student Yasser El-Manzalawy)

  • Development of multi-relational learning algorithms(with MS student Anna Atramentov)

  • Development of independence-based Markov Network learning algorithms (with Facundo Bromberg and Dimitris Margaritis)

  • Theoretical characterization of independence and decomposability of functions that take values into an Abelian group including probability distributions, energy functions, value functions, fitness functions, and relations (with Ph.D. student Adrian Silvescu

  • Development and analysis of a machine learning algorithm for inference of temporal boolean network models from multivariate time series data, with applications to inference of genetic networks from gene expression data (with Ph.D. student Adrian Silvescu

  • Development of machine learning methods based on tensor decomposition for discovery of communities, topics, etc. from web data(with Ph.D. student Flavian Vasile)

  • Development of algorithms for learning predictive models from multi-relational, semantically heterogeneous data (with Ph.D. student Cornelia Caragea)

  • Development of polynomial algorithms for learning regular languages from examples and membership queries (with Ph.D. student Rajesh Parekh and collaborator Codrin Nichitiu)

  • Theoretical analysis of the relationship between various models of learning in helpful environments showing that a concept that is learnable under Gold’s model for learning from characteristic samples, Goldman and Mathias’ polynomial teachability model, and the model for learning from example based queries is also learnable under the PACS model(with Ph.D. student Rajesh Parekh)

  • Establishing that simple DFA (i.e., DFA whose canonical representations have logarithmic Kolmogorov complexity) are efficiently PAC learnable under the Solomonoff Levin universal distribution; and that if the examples are sampled at random according to the universal distribution by a teacher that is knowledgeable about the target concept, DFA are efficiently PAC learnable under the universal distribution, thereby answering the open problem posed by L. Pitt in 1989: Are DFA PAC-identifiable if examples are drawn from the uniform distribution, or some other known simple distribution? (with Ph.D. student Rajesh Parekh

  • Development of evolutionary algorithms for feature subset selection for classification problems (with Ph.D. student Jihoon Yang)

  • Development of evolutionary approaches to design of sensor systems for adaptive robots (with Ph.D. student Karthik Balakrishnan)

  • Development of incremental neural network learning algorithms with applications in nondestructive evaluation (with Ph.D. student Robi Polikar and collaborators Satish Udpa and Lalita Udpa)

  • Development of constructive neural network algorithms that take advantage of prior knowledge in the form of classification rules (with Ph.D. students Rajesh Parekh and Jihoon Yang)

  • Generalization (with convergence guarantees) of a large family of such algorithms designed for 2-class binary pattern classification problems to handle classification problems involving real-valued patterns and an arbitrary number of classes (with Ph.D. students Rajesh Parekh and Jihoon Yang)

  • Development of a simple, inter-pattern distance based provably convergent, polynomial time constructive neural network algorithm which compares very favorably with computationally far more expensive algorithms in terms of generalization accuracy (with Ph.D. students Jihoon Yang and Rajesh Parekh)



Research Contributions in Semantic Web, Information Integration, Knowledge Representation, Ontologies, Service-Oriented Computing


  • Development of a family of description-logics based modular ontology languages (P-DL) that support selective sharing of knowledge and establishment of a minimal set of restrictions on the use of imported concepts and roles to support localized semantics, transitive propagation of imported knowledge, and different interpretations from the point of view of different ontology modules (with Ph.D. student Jie Bao and collaborators Giora Slutzki and George Voutsadakis)

  • Development of a family of sound and complete tableau-based federated reasoning algorithms for distributed, autonomous, P-DL ontologies that support selective sharing of knowledge across ontology modules that avoid the need to integrate ontologies using message exchanges between modules as needed (with Ph.D. student Jie Bao and collaborators Giora Slutzki and George Voutsadakis)

  • Development of a privacy-preserving reasoning framework for answering queries against ontologies using private knowledge without compromising private knowledge (with Ph.D. student Jie Bao and collaborator Giora Slutzki)

  • Development and open-source implementation of an ontology-based system for querying multiple semantically disparate data sources from a user’s point of view (with Ph.D. students Doina Caragea, Jie Bao, and Neeraj Koul)

  • Development of algorithms for specification-driven interactive and verifiable composition of composite web services from autonomous component services (with Ph.D. student Jyotishman Pathak and collaborator Samik Basu)

  • Development of algorithms for context-specific substitution of one service by another within a composite service (with Ph.D.student Jyotishman Pathak and collaborator Samik Basu)



Research Contributions in bioinformatics, computational molecular biology, and systems biology


  • Application of classifiers trained using machine learning to discover a large set of incorrect Gene Ontology annotations an experimentally well-studied family of proteins - mouse kinases (with Ph.D. student Carson Andorf)

  • Development and applications of probabilistic graphical models and related methods for assigning protein sequences to functional families, predicting protein subcellular localization, etc. (with Ph.D. students Carson Andorf and Adrian Silvescu)

  • Construction and analysis of PPIDB, a comprehensive database of protein-protein interfaces (with Ph.D. students Feihong Wu, Raphael Osorio and collaborator Drena Dobbs)

  • Development of machine learning approaches to prediction of protein-protein interface residues from amino acid sequence, evolutionary and when available, structural information (with Ph.D. student Changhui Yan and collaborator Drena Dobbs and Robert Jernigan)

  • Demonstration of the pitfalls of commonly used windows-based cross-validation for sequence-based classification tasks (e.g., phosphorylation site prediction, DNA-binding site prediction) (with Ph.D. student Cornelia Caragea)

  • Development of machine learning approaches and implementation of online servers for prediction of protein-RNA interface residues from amino acid sequence and when available, structural information (with Ph.D. students Michael Terribilini, Cornelia Caragea, and collaborator Drena Dobbs)

  • Development of machine learning approaches and implementation of online servers for prediction of protein-DNS interface residues from amino acid sequence, and when available, structural information (with Ph.D. student Changhui Yan, Cornelia Caragea, and collaborator Drena Dobbs)

  • Structural characterization of protein-protein and protein-RNA interfaces (with Ph.D. students Feihong Wu and Fadi Towfic)

  • Development of machine learning methods and online servers for identification of posttranslational modification sites e.g., phosphorylation sites, glycosylation sites in amino-acid sequences (with Ph.D. students Cornelia Caragea, Yasser El-Manzalawy)

  • Development of kernel-based methods for predicting B-cell epitopes from amino acid sequences (with Ph.D. student Yasser El-Manzalawy and collaborator Drena Dobbs)

  • Demonstrations of the pitfalls of commonly used benchmark datasets for evaluating the performance of machine learning approaches to epitope prediction (with Ph.D. student Yasser El-Manzalawy)

  • Prediction of protein and RNA binding sites in recalcitrant (with regard to attempts at structure determination) proteins e.g., HIV-1 and EIAV and experimental confirmation of the predictions (with Ph.D. students Jae-Hyung Lee, Michael Terribilini and collaborators Drena Dobbs and Susan Carpenter)

  • Development and application of an approach to combining homology modeling and structure prediction methods with machine learning to predict sequence and structural correlates of RNA, DNA, and protein binding sites in telomerase (with Ph.D. students Michael Terribilini, Jae-Hyung Lee, Cornelia Caragea, and collaborator Drena Dobbs)

  • Characterization of gene expression changes during the onset of photosynthesis (with collaborator Steve Rodermel)

  • Characterization of gene expression changes during differentiation of retinal stem cells into rod photoreceptors (with Ph.D. students Tim Alcon, Alison Barnhill, Laura Hecker and collaborators Heather Greenlee and Don Sakaguchi)

  • Characterization differences in the proteome of murine retinal and brain derived progenitor cells (with Ph.D. students Tyra Dunn and collaborators Heather Greenlee and Drena Dobbs)

  • Development of a collaborative phenotype ontology development environment (with Ph.D. students Jie Bao, LaRon Hughes, and collaborator James Reecy)

  • Development of databases and software tools for capture, analysis, annotation, and integration of gene expression data with other types of ‘omics’ data (with Ph.D. students Neeraj Koul, Oliver Couture, and collaborator Chris Tuggle)

  • Development of a method for interactive querying and analysis of multiple gene expression datasets using an experimentally verified gene network to expand the network, and to prioritize experimental targets (with Ph.D. student Tim Alcon and collaborator Heather Greenlee)

  • Development of the Retina Workbench, a software tool for construction, analysis, and comparison of gene and protein networks (with MS student Oksana Kohutyuk, Ph.D. student Fadi Towfic, and collaborator Heather Greenlee)




Research Contributions in Neural and Cognitive Modeling


  • Development of algorithms for construction of robust, noise-tolerant neural networks for pattern storage and associative, content-based retrieval (with Ph.D. student Chun-Hsien Chen)

  • Development of algorithms for construction of highly parallel neural architectures for syntax analysis (parsing of regular, context-free, and context-sensitive languages) (with Ph.D. student Chun-Hsien Chen)

  • Development of a biologically inspired neural architecture and an extended Kalman filter algorithm for place learning and localization in a-priori unknown environments which successfully accounts for a large body of behavioral and neurobiological data from animal experiments and offers several testable predictions (with Ph.D. student Karthik Balakrishnan, and collaborator Olivier Bousquet)



Research Contributions in Distributed Data Driven Applications, Computing and Communication Networks, Critical Infrastructure Monitoring and Protection


  • Development of tools for formal specification of intrusions using colored Petri nets and software fault trees and methods for automated generation of multi-agent systems for coordinated intrusion detection in computer and communication networks (with Ph.D. student Guy Helmer, MS student Mark Slagell, and collaborators Johnny Wong, Robyn Lutz, and Les Miller)

  • Development of multi-agent system for detection of coordinated or concerted attacks on distributed computing systems in particular by monitoring different processes, resources, users, events, and extract and integrate relevant information from disparate sources over multiple space and time scales (with Ph.D. student Guy Helmer and collaborators Johnny Wong, Robyn Lutz, and Les Miller)

  • Development and application of machine learning approaches for learning predictive rules for anomaly and misuse detection (with Ph.D. students Dae-Ki Kang, Guy Helmer, and collaborator Johnny Wong)

  • Development of an electronic nose for detection and identification of odorants using machine learning (with Ph.D student Robi Polikar and collaborators R. Shinar, L. Udpa, and M. Porter).

  • Development and applications of machine learning methods for non-destructive inspection of nuclear power plant pipes using ultrasound (with Ph.D. student Robi Polikar and collaborators L. Udpa, and S. Udpa

  • Development of a service-oriented distributed software infrastructure for
    monitoring distributed power systems (with Ph.D. student Jyotishman Pathak and collaborator Jim McCalley)

  • Development of statistical methods and software for monitoring and condition assessment of critical components of distributed power systems (with collaborator Jim McCalley)

  • Development of distributed multi-agent systems for information integration and decision support in distributed power systems (with MS student Vijay Viswanathan and collaborator Jim McCalley)

  • Development of infrastructure for multi-agent negotiation for power systems, e-commerce, and related applications (with collaborator Mokdong Chung)

  • Development of a utility-theoretic approach to routing in communication networks that supports a flexible tradeoff between delay for a specific message and the overall network load (and hence expected delay for all routed messages) using a knowledge representation scheme that enables each node in a communication network to maintain and update a small constant-size knowledge base (independent of the network size)

  • Theoretical and experimental analysis of utility-theoretic routing that showed the efficacy of the approach in minimizing message delay and load imbalance over the entire network without access to accurate global network state information.


For information about current projects, please visit the web page of the AI Lab.

Education

Ph.D.   University of Wisconsin-Madison   1990
M.S.   University of Wisconsin-Madison   1989

Honors and Awards


Keynote Talk  Chicago Colloquium on Digital Humanities and Computer Science, 2009

Invited Plenary Talk  Biotechnology and Bioinformatics Symposium, 2009

Keynote Talk  International Congress on Pervasive Computing and Management, 2008

Plenary Speaker  Italian Association for Artificial Intelligence, 2008

Chair, NIH Study Section on Data Ontologies and Sharing Data and Tools  National Institutes of Health, 2008

Award for Outstanding Career Achievement in Research  College of Liberal Arts and Sciences , Iowa State University, 2008

Member of Editorial Board  International Journal of Functional Informatics and Personalized Medicine, 2008

Keynote speaker  Workshop on Computational Structural Bioinformatics, IEEE Conference on Bioinformatics and Biomedicine, 2007

Member of Advisory Board, Information Sciences; Artificial Intelligence and Robotics  Lifeboat Foundation, 2007

Member of Review Board  Applied Intelligence Journal, 2007

Member of Editorial Board  International Journal of Computational Biology and Drug Design, 2007

Member of Editorial Board  Journal of Bioinformatics and Biology Insights, 2007

Regents Award for Faculty Excellence  Board of Regents, The State of Iowa, 2007

Senior Member  Association for Computing Machinery (ACM), 2007

Keynote Speaker  Fourth International Conference on Intellingent Sensing and Information Processing (ICISIP 2006), 2006

CV Ramamoorthy Award for Best Paper  IEEE ICTAI 2006, 2006

Member of Editorial Board  International Journal of Semantic Web and Information Systems, 2006

Best Paper Award  Asian Semantic Web Conference, 2006

Invited speaker  Semantic Technologies Conference, 2006

Senior Member  IEEE, 2005

Member of Editorial Board  International Journal of Data Mining and Bioinformatics, 2005

Invited Plenary Speaker  Algorithmic Learning Theory and Discovery Science Conferences, 2005

Chartered Member of Biological Data Management and Analysis Study Section  National Institutes of Health, 2005-2008

Member of Editorial Board  International Journal of Information and Computer Security, 2004

Member of Editorial Board  Machine Learning Journal, 2002-2005

Co Editor in Chief  Cognitive Systems Research, 1999

Research Initiation Award: Constructive Neural Network Learning Algorithms for Pattern Classification  National Science Foundation, 1994-1999

McDonnell-Pew Cognitive Neuroscience Fellow  McDonnell-Pew Foundation, 1989-1989

Argonne Parallel Computing Fellowship  Argonne National Laboratory, 1988-1988

Connectionist Models Summer School Fellowship  Carnegie Mellon University, 1988-1988

Gold Medal for Academic Excellence  Bangalore University, India, 1982

National Science Talent Scholar  National Council of Education, Research, and Training, India, 1977-1982

National Merit Scholar  Department of Education, India, 1975-1982

Current Grants


Developing Predictive Models for Identifying Pigs with Superior Immune Response and Improved Food Safety. Chris Tuggle (PI), Bearson, S., Honavar, V., Nettleton, D. Wannemuehler, M., Lunney, J. (Co-PIs). US Department of Agriculture (2009-2012). $1,000,000.

Identifying porcine genes and gene networks involved in effective response to PRRS virus using functional genomics and systems biology. Joan Lunney (PI, USDA), Vasant Honavar (Co-PI, ISU),Roman Pogranichniy (Co-PI, Purdue), Juan Steibel (Co-PI, MSU), Chris Tuggle (Co-PI, ISU), and Zhihua Zhang (Co-PI, WSU). US Department of Agriculture (2009-2012). $749,975.

Development of bioinformatics resources to transfer biological information across species. Honavar, V. (Co-PI), Reecy, J. (PI), Kwitek, A.. United States Department of Agriculture (2008-2010). $1,000,000.

Collaborative Research: Learning Classifiers from Autonomous, Semantically Heterogeneous, Distributed Data. Vasant Honavar (PI), Doina Caragea (Co-PI). National Science Foundation (2007-2010). $449,999.

IGERT - Computational Biology Training Group. Dan Voytas, Vasant Honavar, Susan Carpenter. National Science Foundation (2005-2010). $2,968,976.

Interactive and Verifiable Composition of Web Services To Satisfy End User Goals. Samik Basu (PI), Robyn Lutz (co-PI). National Science Foundation (2007-2010). $335,002.

High-Accuracy Protein Models Derived from Lower Resolution Data. Andrzej Kloczkowski (PI), Andzej Kolinski, Vasant Honavar, Krzysztof Ginalski, Janusz Bujnicki, Robert Jernigan, Andrzej Joachimiak, Zhijun Wu, Mark Gordon (Co-PIs). National Institutes of Health (2007-2010). $744,725.

Representative Publications

Refereed Journal and Conference Publications

Barnhill, A.E., Hecker, L.A., Kohutyuk, O., Buss, J.E., Honavar, V. and Greenlee, H.W. Characterization of the Retinal Proteome During Rod Photoreceptor Genesis. BMC Research Notes, In press, Accepted, 2009.

Couture, O., Callenberg, K., Koul, N., Pandit, S., Younes, J., Hu, Z-L., Dekkers, J., Reecy, J., Honavar, V., and Tuggle, C. ANEXdb: An Integrated Animal ANnotation and Microarray EXpression Database. Mammalian Genome., In press, Accepted, 2009.

Bromberg, F., Margaritis, D., and Honavar, V. Efficient Markov Network Structure Discovery from Independence Tests. Journal of Artificial Intelligence Research. Vol. 35. pp. 449-485, 2009.

Yakhnenko, O. and Honavar, V. Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence. Proceedings of the SIAM Conference on Data Mining, SIAM Press, Accepted, 2009.

Cornelia Caragea, Jivko Sinapov, Drena Dobbs and Vasant Honavar. Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling. BMC Bioinformatics. Vol. 10 (Suppl 4). No. doi:10.1186/1471-210, 2009.

Towfic, F., Caragea, C., Dobbs, D., and Honavar, V. Struct-NB: Predicting protein-RNA binding sites using structural features. International Journal of Data Mining and Bioinformatics, In press, Accepted, 2009.

Towfic, F., Greenlee, H., and Honavar, V. Aligning Biomolecular Networks Using Modular Graph Kernels. In: Proceedings of the Workshop on Algorithms in Bioinformatics (WABI 2009), In press, Accepted, 2009.

El-Manzalawy, Y. and Honavar, V. MICCLLR: Multiple-Instance Learning using Class Conditional Log Likelihood Ratio. Discovery Science (DS 2009), In press, Accepted, 2009.

Santhanam, G.R., Basu, S., and Honavar, V. Web Service Substitution Based on Preferences Over Non-functional Attributes. Proceedings of the IEEE International Conference on Services Computing (SCC 2009), In press, Accepted, 2009.

Santhanam, G., Basu, S., and Honavar, V. TCP-Compose* - A TCP-net based Algorithm for Efficient Composition of Web Services Based on Qualitative Preferences. Proceedings of the 6th International Conference on Service Oriented Computing, Sydney, Australia, Springer-Verlag Lecture Notes in Computer Science. Vol. 5254. pp. 453-467, Accepted, 2008.

Voutsadakis, G., Bao, J., Slutzki, G., and Honavar, V. F-ALCI: A Fully Contextualized, Federated Logic for the Semantic Web. Proceedings of the ACM/IEEE/WIC Conference on Web Intelligence, Sydney, Australia. pp. 575-578, 2008.

Koul, N., Caragea, C., Bahirwani, V., Caragea, D., and Honavar, V. Using Sufficient Statistics to Learn Predictive Models from Massive Data Sets. Proceedings of the ACM/IEEE/WIC Conference on Web Intelligence, Sydney, Australia, IEEE Computer Society. pp. 923-926, 2008.

Peto M., Kloczkowski A., Honavar V., Jernigan R.L. Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable. BMC Bioinformatics. Vol. 9. pp. 487-, 2008.

Jo, H., Na, Y-C.,, Oh, B., Yang, J., and Honavar, V. Attribute Value Taxonomy Generation through Matrix Based Adaptive Genetic Algorithm. IEEE Conference on Tools with Artificial Intelligence, Dayton, OH, IEEE Press. pp. 393-400, 2008.

Caragea D., Cook, D., Wickham H. and Honavar, V. Visual Methods for Examining SVM Classifiers. Visual Data Mining - Theory, Techniques and Tools for Visual Analytics, Springer-Verlag. Vol. 4404. pp. 136-153, 2008.

Tu, K., and Honavar, V. Unsupervised Learning of Probabilistic Context-Free Grammar using Iterative Biclustering. 9th International Colloquium on Grammatical Inference (ICGI 2008), St . Malo, France, In press, Accepted, 2008.

El-Manzalawy, Y., Dobbs, D., and Honavar, V. On Evaluating MHC-II Binding Peptide Prediction Methods. PLoS One, http://dx.plos.org/10.1371/journal.pone.0003268. Vol. 3. No. 9. pp. e3268, 2008.

El-Manzalawy, Y., Dobbs, D., and Honavar, V. Predicting Flexible Length Linear B-cell Epitopes. 7th Annual International Conference on Computational Systems Bioinformatics, Stanford, CA, World Scientific, 2008.

Yakhnenko, O. and Honavar, V. Annotating Images and Image Objects using a Hierarchical Dirichlet Process Model. 9th International Workshop on Multimedia Data Mining (SIGKDD MDM 2008), Las Vegas, ACM, 2008.

Caragea, C., Sinapov, J., Dobbs, D., and Honavar, V. Using Global Sequence Similarity to Enhance Macromolecular Sequence Labeling. IEEE Conference on Bioinformatics and Biomedicine, In press, Accepted, 2008.

El-Manzalawy, Y., Dobbs, D., and Honavar, V. Predicting Protective Linear B-cell Epitopes using Evolutionary Information. IEEE Conference on Bioinformatics and Biomedicine, In press, Accepted, 2008.

Pathak, J., Basu, S., and Honavar, V. Composing Web Services through Automatic Reformulation of Service Specifications. IEEE International Conference on Services Computing, IEEE. Vol. In press., Accepted, 2008.

El-Manzalawy, Y., Dobbs, D., and Honavar, V. Predicting linear B-cell epitopes using string kernels. Journal of Molecular Recognition, 10.1002/jmr.893. Vol. 21. No. 4. pp. 243-255, 2008.

Hughes, LaRon, Bao, J., Honavar, V., and Reecy, J. Animal Trait Ontology (ATO): the importance and usefulness of a unified trait vocabulary for animal species. Journal of Animal Science. Vol. 86. pp. 1485-1491, 2008.

Bao, J., Voutsadakis, G., Slutzki, G., and Honavar, V. On the Decidability of Role Mappings between Modular Ontologies. Proceedings of the 23nd Conference on Artificial Intelligence (AAAI-2008), Chicago, USA, AAAI, Accepted, 2008.

Pathak, J., Basu, S., Lutz, R., and Honavar, V. MoSCoE: An Approach for Composing Web Services through Iterative Reformulation of Functional Specifications. International Journal on Artificial Intelligence Tools. Vol. 17. No. 1. pp. 109-138, 2008.

Dunn-Thomas, T., Dobbs, D.L., Sakaguchi, D. Young, M.J. Honavar, V. Greenlee, H. M. W. Proteomic Differentiation Between Murine Retinal and Brain Derived Progenitor Cells. Stem Cells and Development. Vol. 17. No. 1. pp. 119-131, 2008.

Yan, C., Dobbs, D., Jernigan, R., and Honavar, V. Characterization of Protein-Protein Interfaces. The Protein Journal. Vol. 27. No. 1. pp. 59-70, 2008.

Alcon, T., Hecker, L., Honavar, V., and Greenlee, H. Analysis and Interpretation of Large-Scale Gene Expression Data Sets Using a Seed Network. Journal of Bioinformatics and Biology Insights. Vol. 2. pp. 91-102, 2008.

Lee, J-H., Hamilton, M., Gleeson, C., Caragea, C., Zaback, P., Sander, J.D., Li, X., Wu, F., Terribilini, M., Honavar, V., and Dobbs, D. Striking Similarities in Diverse Telomerase Proteins Revealed by Combining Structure Prediction and Machine Learning Approaches. Pacific Symposium on Bioinformatics. Vol. 13. pp. 501-512, 2008.

Pathak, J., Basu, S., and Honavar, V. On Context-Specific Substitutability of Web Services. Proceedings of the IEEE International Conference on Web Services, IEEE. pp. 192-199, 2007.

Wu, F., Towfic, F., Dobbs, D. and Honavar, V. Analysis of Protein Protein Dimeric Interfaces. IEEE International Conference on Bioinformatics and Biomedicine, San Jose, IEEE, 2007.

Bao, J., Slutzki, G. and Honavar, V. Privacy-Preserving Reasoning on the Semantic Web. ACM/WIC/IEEE International Conference on Web Intelligence, San Jose, CA, IEEE. pp. 791-797, 2007.

Towfic, F., Gemperline, D., Caragea, C., Wu, F., Dobbs, D., and Honavar, V. Structural Characterization of RNA-Binding Sites of Proteins: Preliminary Results. IEEE BIBM Computational Structural Bioinformatics Workshop, 2007.

Caragea, C., Sinapov, J., Terribilini, M., Dobbs, D. and Honavar, V. Assessing the Performance of Macromolecular Sequence Classifiers. IEEE Conference on Bioinformatics and Bioengineering, Boston, MA, IEEE. pp. 320-326, 2007.

Andorf, C., Silvescu, A., Dobbs, D., and Honavar, V. Exploring Inconsistencies in Genome Wide Protein Function Annotations: A Machine Learning Approach. BMC Bioinformatics. Vol. 8. pp. doi:10.1186/1471-210, 2007.

Bao, J., Slutzki, G., and Honavar, V. A Semantic Importing Approach to Knowledge Reuse from Multiple Ontologies. In: Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-2007), Vancouver, Canada, AAAI, 2007.

Bao, J., Slutzki, G., and Honavar, V. Distributed Reasoning with Modular ALC Ontologies (ALCP). Web Intelligence and Agent Systems. Vol. In press., Accepted, 2007.

Terribilini, M., Sander, J.D., Lee, J-H., Zaback, P., Jernigan, R.L., Honavar, V. and Dobbs, D. RNABindR: A Server for Analyzing and Predicting RNA Binding Sites in Proteins. Nucleic Acids Research. Vol. 35. No. 9. pp. doi:10.1093/nar/gkm2, 2007.

Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., Wang, Y., Wang, X., and Stakhanova, N. Software Fault Tree and Colored Petri Net Based Specification, Design, and Implementation of Agent-Based Intrusion Detection Systems. International Journal of Information and Computer Security. Vol. 1. No. 1. pp. 09-142, 2007.

Pathak, J., Li, Y., Honavar, V., McCalley , J. A Service-Oriented Architecture for Electric Power Transmission System Asset Management. Second International Workshop on Engineering Service-Oriented Applications: Design and Composition, Lecture Notes in Computer Science, Berlin: Springer-Verlag, 2007.

J. McCalley, V. Honavar, S. Ryan, W. Meeker, D. Qiao, R. Roberts, Y. Li, J. Pathak, M. Ye, Y. Hong. Integrated Decision Algorithms for Auto-steered Electric Transmission System Asset Management. 7th Intl. Conference on Computational Science, Berlin: Springer-Verlag. Vol. 4487. pp. 1066-1073, 2007.

Caragea, C., Sinapov, J., Silvescu, A., Dobbs, D. and Honavar, V. Glycosylation Site Prediction Using Ensembles of Support Vector Machine Classifiers. BMC Bioinformatics, doi:10.1186/1471-2105-8-438. Vol. 8, 2007.

Bao, J., Caragea, D., and Honavar, V. A Distributed Tableau Algorithm for Package-based Description Logics. Proceedings of the Second International Workshop on Context Representation and Reasoning (CRR 2006), Riva del Garda, Italy, CEUR, 2006.

Bao, J., Caragea, D., and Honavar, V. A Tableau-based Federated Reasoning Algorithm for Modular Ontologies. ACM/IEEE/WIC Conference on Web Intelligence, Hong Kong, IEEE. pp. 404-410, 2006.

Pathak, J., Basu, S., and Honavar, V. Modeling Web Services by Iterative Reformulation of Functional and Non-Functional Requirements. Proceedings of the International Conference on Service-Oriented Computing, Lecture Notes in Computer Science, Chicago, Springer-Verlag Lecture Notes in Computer Science. Vol. 4294. pp. 314-326, 2006.

J. Pathak, S. Basu, R. Lutz, and V. Honavar. MoSCoE: A Framework for Modeling Web Service Composition and Execution. IEEE Conference on Data Engineering Ph.D. Workshop, Atlanta, GA, 2006.

Bao, J., Caragea, D., and Honavar, V. On the Semantics of Linking and Importing in Modular Ontologies. International Semantic Web Conference, Athens, Georgia, USA, Springer-Verlag Lecture Notes in Computer Science. Vol. 4273. pp. 72-86, 2006.

Pathak, J., Basu, S., Lutz, R., and Honavar, V. Applying Tabled-Logic Programming to Web Service Development using Abstraction, Composition and Refinement. Proceedings of the IEEE International Conference on Tools With Artificial Intelligence (ICTAI 2006), Washington, DC, IEEE Press. pp. 445-454, 2006.

Pathak, J., Basu, S., Lutz, R., and Honavar, V. Parallel Web Service Composition in MoSCoE: A Choreography Based Approach. Proceedings of the IEEE European Conference on Web Services (ECOWS 2006), Zurich, Switzerland, IEEE. pp. 3-12, 2006.

Bao, J., Hu, Z., Caragea, D., Reecy, J., and Honavar, V. A Tool for Collaborative Construction of Large Biological Ontologies. Fourth International Workshop on Biological Data Management (BIDM 2006), Krakov, Poland, IEEE Press, 2006.

Bao, J., Caragea, D., and Honavar, V. Modular Ontologies - A Formal Investigation of Semantics and Expressivity. In Proceedings of the First Asian Semantic Web Conference, Beijing, China, Springer-Verlag. pp. 616-631, 2006.

Caragea, D., Zhang, J., Pathak, J., and Honavar, V. Learning Classifiers from Distributed, Ontology-Extended Data Sources. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2006), Krakov, Poland, Lecture Notes in Computer Science. Berlin: Springer. Vol. 4081. pp. 363-373, 2006.

Terribilini, M., Lee, J.-H., Yan, C., Jernigan, R. L., Honavar, V. and Dobbs, D. Predicting RNA-binding Sites from Amino Acid Sequence. RNA Journal.. Vol. 12. pp. 1450-1462, 2006.

Pathak, J., Basu, S., and Honavar, V. Modeling Web Service Composition Using Symbolic Transition Systems. AAAI '06 Workshop on AI-Driven Technologies for Services-Oriented Computing (AI-SOC), Boston, MA, AAAI Press, 2006.

Yan, C., Terribilini, M., , Wu, F., Jernigan, R.L., Dobbs, D. and Honavar, V. Identifying amino acid residues involved in protein-DNA interactions from sequence. BMC Bioinformatics. Vol. 7. pp. 262-, 2006.

Kang, D-K., Silvescu, A. and Honavar, V. RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification. Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). Lecture Notes in Computer Science., Berlin: Springer-Verlag. pp. 45-54, 2006.

Bao, J., Caragea, D., and Honavar, V. Towards Collaborative Environments for Ontology Construction and Sharing. Proceedings of the International Symposium on Collaborative Technologies and Systems., Las Vegas, 2006.

Wang, Y., Behera, S., Wong, J., Helmer, G., Honavar, V., Miller, L., and Lutz, R. Towards Automatic Generation of Mobile Agents for Distributed Intrusion Detection Systems. Journal of Systems and Software. Vol. 79. pp. 1-14, 2006.

Zhang, J., Kang, D-K., Silvescu, A. and Honavar, V. Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data. Knowledge and Information Systems. Vol. 9. No. 2. pp. 157-179, 2006.

Bromberg, F., Margaritis, D., and Honavar, V. Efficient Markov Network Structure Discovery from Independence Tests. SIAM Conference on Data Mining (SDM 06), SIAM Press. pp. 141-152, 2006.

Pathak, J, Yong, J. Honavar, V., McCalley, J. Condition Data Aggregation for Failure Mode Estimation of Power Transformers. Hawaii International Conference on Systems Sciences, IEEE Computer Society. pp. 241a, 2006.

Terribilini, M., Lee. J-H., Yan, C., Carpenter, S., Jernigan, R., Honavar, V. and Dobbs, D. Identifying interaction sites in recalcitrant proteins: predicted protein and rna binding sites in HIV-1 and EIAV agree with experimental data. Pacific Symposium on Biocomputing, Hawaii, World Scientific. Vol. 11. pp. 415-426, 2006.

Silvescu, A. and Honavar, V. Independence, Decomposability and functions which take values into an Abelian Group. Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics, http://anytime.cs.umass.edu/aimath06/proceedings.html, 2006.

Vasile, F., Silvescu, A., Kang, D-K., and Honavar, V. TRIPPER: An Attribute Value Taxonomy Guided Rule Learner. Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Berlin: Springer-Verlag. pp. 55-59, 2006.

Wu, F., Olson, B., Dobbs, D., and Honavar, V. Using Kernel Methods to Predict Protein-Protein Interaction Sites from Sequence. IEEE Joint Conference on Neural Networks, Vancouver, Canada, IEEE Press, 2006.

Pathak, J,, Koul, N., Caragea, D., and Honavar, V. A Framework for Semantic Web Services Discovery. Proceedings of the 7th ACM International Workshop on Web Information and Data Management (WIDM 2005)., ACM Press. pp. 45-50, 2005.

Yakhnenko, O., Silvescu, A., and Honavar, V. Discriminatively Trained Markov Model for Sequence Classification. IEEE Conference on Data Mining (ICDM 2005), Houston, Texas, IEEE Press, 2005.

Caragea, D., Zhang, J., Bao, J., Pathak, J., and Honavar, V. Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous Information Sources (Invited paper). Proceedings of the 16th International Conference on Algorithmic Learning Theory. Lecture Notes in Computer Science, Singapore, Berlin: Springer-Verlag. Vol. 3734. pp. 13-44, 2005.

Zhang, J., Caragea, D. and Honavar, V. Learning Ontology-Aware Classifiers. Proceedings of the 8th International Conference on Discovery Science. Springer-Verlag Lecture Notes in Computer Science, Singapore, Berlin: Springer-Verlag. Vol. 3735. pp. 308-321, 2005.

Caragea, D., Bao, J., Pathak, J., Andorf, C,., Dobbs, D., and Honavar, V. Information Integration from Semantically Heterogeneous Biological Data Sources. Proceedings of the Sixteenth International Workshop on Databases and Expert Systems Applications (DEXA 05), Copenhagen, IEEE Computer Society. pp. 580-584, 2005.

Kang, D-K., Fuller, D., and Honavar, V. Learning Misuse and Anomaly Detectors from System Call Frequency Vector Representation. IEEE International Conference on Intelligence and Security Informatics. Springer-Verlag Lecture Notes in Computer Science, Springer-Verlag. Vol. 3495. pp. 511-516, 2005.

Kang, D-K., Zhang, J., Silvescu, A., and Honavar, V. Multinomial Event Model Based Abstraction for Sequence and Text Classification. Proceedings of the Symposium on Abstraction, Reformulation, and Approximation (SARA 2005), Edinburgh, UK, Berlin: Springer-Verlag. Vol. 3607. pp. 134-148, 2005.

Wu. F., Zhang, J., and Honavar, V. Learning Classifiers Using Hierarchically Structured Class Taxonomies. Proceedings of the Symposium on Abstraction, Reformulation, and Approximation (SARA 2005), Edinburgh, Berlin, Springer-Verlag. Vol. 3607. pp. 313-320, 2005.

Kang, D-K., Fuller, D., and Honavar, V. Learning Classifiers for Misuse and Anomaly Detection Using a Bag of System Calls Representation. Proceedings of the 6th IEEE Systems, Man, and Cybernetics Workshop (IAW 05), West Point, NY, IEEE. pp. 118-125, 2005.

Caragea, D., Silvescu, A., Pathak, J., Bao, J., Andorf, C., Dobbs, D., and Honavar, V. Information Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources. Data Integration in Life Sciences (DILS 2005) Springer-Verlag Lecture Notes in Computer Science, San Diego, Berlin: Springer-Verlag. Vol. 3615. pp. 175-190, 2005.

Sen, T.Z., Kloczkowski, A., Jernigan, R.L., Yan, C., Honavar, V., Ho, K-M., Wang, C-Z., Ihm, Y., Cao, H., Gu, X., and Dobbs, D. Predicting Binding Sites of Protease-Inhibitor Complexes by Combining Multiple Methods. BMC Bioinformatics. Vol. 5. pp. 205, 2004.

R. Polikar, L. Udpa, S. Udpa, and V. Honavar. An Incremental Learning Algorithm with Confidence Estimation for Automated Identification of NDE Signals. IEEE Transactions of Ultrasonics, Ferroelectrics, and Frequency Control. Vol. 51. pp. 990-1001, 2004.

Cook, D., Caragea, D., and Honavar, V. Visualization in Classification Problems. Proceedings in Computational Statistics (COMPSTAT 2004), Springer-Verlag. pp. 799-806, 2004.

Zhang, J. and Honavar, V. Learning Compact and Accurate Classifiers from Attribute Value Taxonomies and Partially Specified Data. IEEE International Conference on Data Mining, IEEE Press. pp. 289-298, 2004.

Bao, J. and Honavar, V. Collaborative Ontology Building With Wiki@nt. Third International Workshop on Evaluation of Ontology Building Tools, Hiroshima, 2004.

Yan, C., Dobbs, D., and Honavar, V. A Two-Stage Classifier for Identification of Protein-Protein Interface Residues. Bioinformatics. Vol. 20. pp. i371-378, 2004.

Bao, J., Cao, Y., Tavanapong, W., and Honavar, V. Integration of Domain-Specific and Domain-Independent Ontologies for Colonoscopy Video Database Annotation. International Conference on Information and Knowledge Engineeringl (IKE 04), Las Vegas, Nevada, USA, CSREA Press. pp. 82-88, 2004.

Lonosky, P., Zhang, X., Honavar, V., Dobbs, D., Fu, A., and Rodermel, S. A Proteomic Analysis of Chloroplast Biogenesis in Maize. Plant Physiology. Vol. 134. pp. 560-574, 2004.

Caragea, D., Silvescu, A., and Honavar, V. A Framework for Learning from Distributed Data Using Sufficient Statistics and its Application to Learning Decision Trees. International Journal of Hybrid Intelligent Systems. Vol. 1. No. 2. pp. 80-89, 2004.

Yan, C., Dobbs, D., and Honavar, V. Identifying Protein-Protein Interaction Sites from Surface Residues – A Support Vector Machine Approach. Neural Computing Applications. Vol. 13. pp. 123-129, 2004.

Kang, D-K., Silvescu, A., Zhang, J. and Honavar, V. Generation of Attribute Value Taxonomies from Data for Accurate and Compact Classifier Construction. IEEE International Conference on Data Mining, IEEE Press. pp. 130-137, 2004.

Andorf, C., Silvescu, A., Dobbs, D. and Honavar, V. Learning Classifiers for Assigning Protein Sequences to Gene Ontology Functional Families. Fifth International Conference on Knowledge Based Computer Systems (KBCS 2004), India, New Delhi, India: Allied Publishers. pp. 256-255, 2004.

Pathak, J., Caragea, D., and Honavar, V. Ontology-Extended Component-Based Workflows: A Framework for Constructing Complex Workflows from Semantically Heterogeneous Software Components. VLDB-04 Workshop on Semantic Web and Databases. Springer-Verlag Lecture Notes in Computer Science., Toronto, Springer-Verlag. Vol. 3372. pp. 41-56, 2004.

Caragea, D., Pathak, J. and Honavar, V. Learning Classifiers from Semantically Heterogeneous Data. International Conference on Ontologies, Databases, and Applications of Semantics (ODBASE 2004). Springer-Verlag Lecture Notes in Computer Science, Cyprus, Greece, Springer-Verlag. Vol. 3291. pp. 963-980, 2004.

Zhang, Z.; McCalley, J.D.; Vishwanathan, V.; Honavar, V. Multiagent system solutions for distributed computing, communications, and data integration needs in the power industry. Proceedings of the General Meeting of the IEEE Power Engineering Society, IEEE Press. pp. 45-49, 2004.

Caragea, D., Cook, D., and Honavar, V. Toward Simple, Easy-to-Understand Classifiers. Proceedings of the IEEE International Conference on Data Mining, IEEE Press. pp. 497-500, 2003.

Wang, X., Schroeder, D., Dobbs, D., and Honavar, V. Data-Driven Discovery of Rules for Protein Function Classification Based on Sequence Motifs. Information Sciences. Vol. 155. pp. 1-18, 2003.

Reinoso-Castillo, J., Silvescu, A., Caragea, D., Pathak, J. and Honavar, V. Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources: A Federated, Query-Centric Approach. Proceedings of the IEEE International Conference on Information Integration and Reuse., Las Vegas, IEEE Press. pp. 183-191, 2003.

Zhang, J. and Honavar, V. Learning from Attribute Value Taxonomies and Partially Specified Data. Proceedings of the International Conference on Machine Learning, Washington, DC, AAAI Press. pp. 880-887, 2003.

Caragea, D., Silvescu, A., and Honavar, V. Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources. Proceedings of the International Conference on Intelligent Systems Design and Applications (ISDA 2003), Springer Verlag, 2003.

Atramentov, A., Leiva, H., and Honavar, V. A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments. Proceedings of the Conference on Inductive Logic Programming, Springer-Verlag, 2003.

Helmer, G., Wong, J., Honavar, V., and Miller, L. Lightweight Agents for Intrusion Detection. Journal of Systems and Software. Vol. 67. No. 2. pp. 109-122, 2003.

Caragea, D., Reinoso-Castillo, J., Silvescu, A., and Honavar, V. Statistics Gathering for Learning from Heterogeneous, Distributed, Autonomous Data Sources. Proceedings of the Workshop on Information Integration on the Web. International Joint Conference on Artificial Intelligence, Acapulco, Mexico, 2003.

Z. Zhong, V. Vishwanathan, J. McCalley, V. Honavar. Multiagent System Solutions for Distributed Computing, Communications, and Data Integration Needs in the Power Industry. Proc. of the 2003 IEEE PES Summer Meeting, Toronto, 2003.

Caregea, D., Cook, D., & Honavar, V. Visualizing Ensemble of Hyperplane Classifiers. IEEE International Conference on Data Mining, Melbourne, Florida, Springer Verlag, 2003.

Helmer, G., Wong, J., Honavar, V., and Miller, L. Automated Discovery of Concise Predictive Rules for Intrusion Detection. Journal of Systems and Software. Vol. 60. No. 3. pp. 165-175, 2002.

Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., and Lutz, R. A Software Fault Tree Approach to Requirements Specification of an Intrusion Detection System. Requirements Engineering. Vol. 7. No. 4. pp. 207-220, 2002.

Zhang, J., Silvescu, A., and Honavar, V. Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. Proceedings of Symposium on Abstraction, Reformulation, and Approximation, Berlin, Springer-Verlag. pp. 316-323, 2002.

Andorf, C., Dobbs, D., and Honavar, V. Discovering Protein Function Classification Rules from Reduced Alphabet Representations of Protein Sequences. Proceedings of the Conference on Computational Biology and Genome Informatics, Durham, North Carolina, 2002.

Parekh, R. and Honavar, V. DFA Learning from Simple Examples. Machine Learning. Vol. 44. pp. 9-35, 2001.

Polikar, R., Udpa, L., Udpa, S., and Honavar, V. Learn++: An Incremental Learning Algorithm for Multi-Layer Perceptron Networks. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 31. No. 4. pp. 497-508, 2001.

Mikler, A., Honavar, V. and Wong, J. Autonomous Agents for Coordinated Distributed Parameterized Heuristic Routing in Large Dynamic Communication Networks. Journal of Systems and Software. Vol. 56. pp. 231-246, 2001.

Silvescu, A., and Honavar, V. Temporal Boolean Network Models of Genetic Networks and Their Inference from Gene Expression Time Series. Complex Systems. Vol. 13. No. 1. pp. 54-75, 2001.

Wong, J., Helmer, G., Naganathan, V. Polavarapu, S., Honavar, V., and Miller, L. SMART Mobile Agent Facility. Journal of Systems and Software. Vol. 56. No. 1. pp. 9-22, 2001.

Polikar, R., Shinar, R., Honavar, V., Udpa, L., and Porter, M. Detection and Identification of Odorants Using an Electronic Nose. The Sixth IEEE Conference on Acoustics, Speech, and Signal Processing., Salt Lake City, UT, USA. pp. 3137-3140, 2001.

Caragea, D., Cook, D., and Honavar, V. Gaining Insights into Support Vector Machine Classifiers Using Projection-Based Tour Methods. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, ACM. pp. 251-256, 2001.

Wang, D., Wang, X., Honavar, V., & Dobbs, D. Data-Driven Generation of Decision Trees for Motif-Based Assignment of Protein Sequences to Functional Families. Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology, 2001.

Vishwanathan, V., McCalley, J., and Honavar, V. A Multiagent System Infrastructure and Negotiation Framework for Electric Power Systems. Proceedings of the IEEE Power Technology Conference, Porto, Portugal, 2001.

Balakrishnan, K., Bousquet, O. and Honavar, V. Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots. Adaptive Behavior. Vol. 7. No. 2. pp. 173-216, 2000.

Parekh, R., Yang, J., and Honavar, V. Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification. IEEE Transactions on Neural Networks. Vol. 11. No. 2. pp. 436-451, 2000.

Chung, M and Honavar, V. A Negotiation Model in Agent-Mediated Electronic Commerce. Proceedings of the International Symposium on Multimedia Software Engineering. pp. 403-410, 2000.

Yang, J., Parekh, R. and Honavar, V. Comparison of Performance of Variants of Single-Layer Perceptron Algorithms on Non-Separable Data. Neural, Parallel, and Scientific Computations. Vol. 8. pp. 451-438, 2000.

Pai, P., Miller, L., Nilakanta, S., Honavar, V., and Wong, J. Challenges of Information Technology Management in the 21st centure. Proceedings of the Eleventh International Conference of the Information Resources Management Association, Anchorage, Alaska. pp. 325-329, 2000.

Parekh, R. and Honavar, V. On the Relationships between Models of Learning in Helpful Environments. Proceedings of the International Colloquium on Grammatical Inference (ICGI-2000), Lisbon, Portugal, Berlin: Springer-Verlag. pp. 207-220, 2000.

Polikar, R., Udpa, L., Udpa, S., and Honavar, V. Learn++: An incremental learning algorithm for multilayer perceptron networks. IEEE International Conference on Acoustics, Speech, and Signal Processing. pp. 3414-3417, 2000.

Yang, J., Parekh, R. and Honavar, V. DistAl: An Inter-Pattern Distance Based Constructive Neural Network Learning Algorithm. Intelligent Data Analysis. Vol. 3. pp. 55-73, 1999.

Chen, C. and Honavar, V. A Neural Network Architecture for Syntax Analysis. IEEE Transactions on Neural Networks, IEEE Press. Vol. 10. No. 1. pp. 94-114, 1999.

Yang, J., Parekh, R., Honavar, V., and Dobbs, D. Data-Driven Theory Refinement Using KBDistAl. Conference on Intelligent Data Analysis (IDA 99), Springer-Verlag. pp. 331-342, 1999.

Honavar, V., Miller, L. and Wong, J. Distributed Knowledge Networks. Proceedings of the IEEE Information Technology Conference, Syracuse, NY., IEEE Press, 1998.

Yang, J. and Honavar, V. Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems. Vol. 13. pp. 44-49, 1998.

Miker, A., Wong, J. and Honavar, V. An Object-Oriented Approach to Simulating Large Communication Networks. Journal of Systems and Software. Vol. 40. pp. 151-164, 1998.

Balakrishnan, K. & Honavar, V. Intelligent Diagnosis Systems. Intelligent Systems. Vol. 8. pp. 239-290, 1998.

Leavens, G., Baker, A., honavar, V., Lavalle, S., and Prabhu, G. Programming is Writing: Why Student Programs Must be Carefully Evaluated. Mathematics and Computer Education. Vol. 32. pp. 284-295, 1998.

Bousquet, O., Balakrishnan, K., and Honavar, V. Is the Hippocampus a Kalman Filter?. Pacific Symposium on Biocomputing, Singapore, World Scientific. pp. 655-666, 1998.

Parekh, R., Nichitiu, C., and Honavar, V. A Polynomial Time Incremental Algorithm for Regular Grammer Inference. Fourth International Colloquium on Grammatical Inference. Lecture Notes in Artificial Intelligence, Springer-Verlag. Vol. 1433. pp. 37-49, 1998.

Mikler, A., Wong, J., & Honavar, V. QuoVadis - A Framework for Intelligent Routing in Large High Speed Communication Networks. Journal of Systems and Software. Vol. 37. pp. 61-73, 1997.

Mikler, A., Honavar, V., and Wong, J. Utility-Theoretic Heuristics for Intelligent Adaptive Routing in Large Communication Networks. Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, OR, AAAI Press. pp. 96-102, 1996.

Chen, C. & Honavar, V. A Neural Memory Architecture for Content as Well as Address-Based Storage and Recall: Theory and Applications. Connection Science. Vol. 7. No. 293. pp. 312, 1995.

Balakrishnan, K., and Honavar, V. Properties of Genetic Representations of Neural Architectures. Proceedings of the World Congress on Neural Networks., Washington, DC, INNS. pp. 807-813, 1995.

Chen, C., Yang, J., Balakrishnan, K., Parekh, R., & Honavar, V. Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms. World Congress on Neural Networks, Washington, D.C.. pp. 628-635, 1995.

Books

Honavar, V., D. Caragea, J. Knowledge Acquisition from Semantically Heterogeneous Distributed Data. Springer-Verlag. In press. 2008.

Patel, M., Honavar, V. and Balakrishnan, K. (Ed). Advances in Evolutionary Synthesis of Intelligent Agents. MIT Press. 2001.

Banzaf, W., Daida, J., Eiben, A., Garzon, M., Honavar, V., Jakiela, M., & Smith R. Proceedings of the Genetic and Evolutionary Computing Conference. Morgan Kaufmann 1999.

Honavar, V. and Slutzki, G. Grammatical Inference. Springer-Verlag 1998.

Honavar, V. and Uhr, L. (Ed). Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. Academic Press 1994.

Book Chapters

Caragea, D. and Honavar, V. Learning Classifiers from Semantically Heterogeneous Data. In: Encyclopedia of Data Warehousing and Mining (Ed. Wang, J), IGI Global 2009.

Bao, J., Slutzki, G., and Honavar, V. Package-Based Description Logics. In: In: Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization (Ed. Parent, C., Spaccapietra, S., and Stuckenschmidt, H.), Berlin: Springer Lecture Notes in Computer Science Vol. 5445 pp. 349-371 2009.

Caragea, D. and Honavar, V. Learning Classifiers from Distributed Data. In: Encyclopedia of Database Technologies and Applications (Ed. Ferraggine, V.E., Doorn, J.H., and Rivero, L.C.), Idea Group. In press 2008.

Pathak, J., Basu, S., Honavar, V. Assembling Composite Web Services from Autonomous Components. In: Emerging Artificial Intelligence Applications in Computer Engineering (Ed. Maglogiannis, I., Karpouzis, K., and Soldatos, J.), IOS Press, In press 2008.

Honavar, V, and Caragea, D. Towards Semantics-Enabled Infrastructure for Knowledge Acquisition from Distributed Data. In: Next Generation Data Mining (Ed. Kargupta, H.), In press 2008.

Caragea, C. and Honavar, V. Machine Learning in Computational Biology. In: Encyclopedia of Database Systems, In press (Ed. Raschid, L.), Springer 2008.

Caragea, D., Cook, D., Wickham, H., and Honavar, V. Visual Methods for Examining SVM Classifiers. In: Visual Data Mining: Theory, Techniques, and Tools for Visual Analytics, Springer, In press 2008.

Honavar, V., Miller, L., and Wong, J. Distributed Knowledge Networks. In: Unifying Themes in Complex Systems (Ed. Bar-Yam, Y., and Minai, A.), Perseus Books 2004.

McCalley, J., Honavar, V., Zhang, Z., and Vishwanathan, V. Multiagent negotiation models for power system applications. In: Autonomous Systems and Intelligent Agents in Power System Control and Operation (Ed. Christian Rehtanz), Springer-Verlag 2003.

Pai, P., L.L. Miller, V. Honavar, J. Wong, and S. Nilakanta. Supporting Organizational Knowledge Management with Agents. In: Knowledge Mapping and Management (Ed. D. White), IRM Press 2002.

Balakrishnan, K. and Honavar, V. Evolving Neurocontrollers and Sensors for Artificial Agents. In: Evolutionary Synthesis of Intelligent Agents (Ed. Patel, M., Honavar, V., and Balakrishnan, K.), MIT Press 2001.

Honavar, V. and Balakrishnan, K. Evolutionary and Neural Synthesis of Agents. In: Evolutionary Systhesis of Intelligent Agents (Ed. Patel, M., Honavar, V., and Balakrishnan, K.), MIT Press 2001.

Caragea, D., Silvescu, A., and Honavar, V. Towards a Theoretical Framework for Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience (Ed. Wermter, S., Austin, J. & Willshaw, D.), Springer-Verlag 2001.

Balakrishnan, K., and Honavar, V. Some Experiments in the Evolution of Robot Sensors. In: Evolution of Engineering and Information Systems and Their Applications (Ed. Jain, L.), CRC Press 2000.

Parekh, R. and Honavar, V. Automata Induction, Grammar Inference, and Language Acquisition. In: Handbook of Natural Language Processing (Ed. Dale, R., Moisl, H. & Somers, H.), Marcel Dekker 2000.

Chen, C. and Honavar, V. A Neural Network Architecture for Information Retrieval and Query Processing. In: Handbook of Natural Language Processing (Ed. Dale, R., Moisl, H., and Somers, H.), Marcel Dekker 2000.

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