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    ARTIFICIAL INTELLIGENCE RESEARCH LABORATORY
    Center for Computational Intelligence, Learning, and Discovery
    Department of Computer Science


Vasant Honavar
Professor of Computer Science
Artificial Intelligence Research Laboratory
Center for Computational Intelligence, Learning, & Discovery
Bioinformatics and Computational Biology Program
Department of Computer Science
211 Atanasoff Hall
Iowa State University
Ames, Iowa 50011-1040
voice: (515) 294-1098
fax: (515) 294-0258
email: honavar@cs.iastate.edu

You can generally find me here.

Index

Professional

Personal

Biographical Sketch


Vasant Honavar received his Ph.D. in Computer Science and Cognitive Science from the University of Wisconsin (Madison) in 1990. He joined the Department of Computer Science at Iowa State University in 1990 where he is presently a full professor. He is also on the faculty of the Bioinformatics and Computational Biology Neuroscience, and Information Assurance graduate programs as well as the Complex Adaptive Systems graduate minor. He has served as the chair of the Bioinformatics and Computational Biology graduate program and the Bioinformatics project director of the Computational Molecular Biology training group funded by a National Science Foundation IGERT (Integrative Graduate Education and Research Training) award at Iowa State University.

Honavar's current research and teaching interests include: artificial intelligence, bioinformatics and computational biology, Computational systems biology, cognitive science, machine learning, neural networks, intelligent agents, and multi-agent systems, data mining and knowledge discovery, computational and cognitive neuroscience, evolutionary computation, machine perception, intelligent systems, knowledge-based systems, pattern recognition, medical informatics, and artificial intelligence applications in science and engineering.

Honavar founded and directs the Center for Computational Intelligence, Learning, and Discovery at Iowa State University. He is also the founder (and director) of the Artificial Intelligence Research Laboratory. Some of the research in this laboratory has been supported through research grants from National Science Foundation, the National Institutes of Health", the US Department of Agriculture, the Department of Defense, the Department of Energy, Electric Power Research Institute, the John Deere Foundation, the Carver Foundation, the Iowa State University Council on International Programs, and Iowa State University, as well as fellowships and research assistantships funded by the IBM Corporation, and the Iowa State University Graduate College.

Honavar has published over 150 refereed papers in journals and conferences and 10 invited book chapters on these topics. He has also edited several books including: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration (with Prof. Leonard Uhr), published by Academic Press in 1994; Grammatical Inference (Lecture Notes in Computer Science Vol. 1433) (with Giora Slutzki) published by Springer-Verlag in 1998; Evolutionary Synthesis of Intelligent Agents (with his former Ph.D. student Karthik Balakrishnan and Mukesh Patel) published by MIT Press in 2001.

Honavar is an editor-in-chief of the Cognitive Systems Research which is published by Elsevier. Honavar has served on the editorial board of Machine Learning journal. He guest edited (with Colin de la Higuera) a special issue of the Machine Learning on automata induction, grammar inference, and language acquisition. Honavar currently serves on the editorial boards of the International Journal of Data Mining and Bioinformatics and Internatuional Journal of Information and Computer Security.

Honavar is the program chair of the AAAI Fall Symposium on Collaborative Knowledge Acquisition from Autonomous, Distributed, Semantically Disparate Information Sources. Honavar served as the program chair of the Fourth Conference on Computational Biology and Genome Informatics (CBGI-2002) to be held at Durham, North Carolina in March 2002. Honavar was the program chair for the Fourth International Colloquium on Grammatical Inference which was held at Iowa State University in Ames in July 1998.

Honavar has organized several workshops, the most recent being the Workshop on Knowledge Discovery from Heterogeneous, Distributed, Dynamic, Autonomous Data Sources held in conjunction with International Joint Conference on Artificial Intelligence (IJCAI-2001), Workshop on Cognitive Agents and Multi-Agent Interactions held in conjuction with International Conference on Cognitive Science (ICCS 2001), Workshop on Learning from Sequential and Temporal Data, held in conjunction with the International Conference on Machine Learning (ICML 2000), the Workshop on Computation with Neural Systems, held in conjunction with the Sixteenth National Conference on Artificial Intelligence (AAAI 99), and the Workshop on Automata Induction, Grammatical Inference, and Language Acquisition held in conjunction with ICML 97.

Honavar serves as a referee for several journals including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Expert, Connection Science, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks, Neural Computation, Information Sciences, IEEE Transactions on Knowledge and Data Engineering, Applied Intelligence, Evolutionary Computation, Machine Vision and Applications, Connection Science, and Neural Networks.

Honavar has served on program committees of numerous conferences, the most recent ones being the International Conference on Machine Learning (ICML 2006), International Colloquium on Grammatical Inference (ICGI 2006), 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 06), IEEE International Conference on Data Mining (ICDM 2005), International Conference on Algorithmic Learning Theory (ALT 2005)IEEE Conference on Tools with Artificial Intelligence (ICTAI 2005), IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), IEEE International Conference on Data Mining (ICDM 2004), the International Conference on Machine Learning (ICML 2004), the the IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2004), the International Colloquium on Grammatical Inference (ICGI 2004), IEEE/WIC/ACM Conference on Intelligent Agent Technology,.

Honavar has designed and taught graduate and undergraduate courses in Artificial Intelligence, Bioinformatics, Computational Systems Biology, Machine Learning, Neural Networks, Intelligent Agents and Multi-Agent Systems, and seminars in Bioinformatics, Cognitive and Neural Modeling, Data Mining and Knowledge Discovery, and Ontologies, Semantic Web, Computational Systems Biology, Information Integration, and related topics. He has developed and presented tutorials on several topics in Intelligent Agents and Multiagent Systems, and Machine Learning.

Honavar is a senior member of Institution of Electrical and Electronics Engineers (IEEE), Association for Computing Machinery (ACM), International Society for Computational Biology (ISCB), American Association of Artificial Intelligence (AAAI), American Medical Informatics Association, International Machine Learning Society, Cognitive Science Society, Society for Neuroscience, Neural Network Society, New York Academy of Sciences, and Sigma Xi and an associate of Behavior and Brain Sciences. He regularly serves as a consultant on topics in Artificial Intelligence, Data Mining, Bioinformatics, Data Integration, Semantic Web, and related areas.

The Formative Years

Current Affiliations

Research and Teaching Interests

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

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.
Current and Recent Research and Training Grants

Current Grants

Past Grants

Other research support has come from a number of sources including the College of Liberal Arts and Sciences at Iowa State University.
Research Overview and Selected Publications


Following is a brief overview of current research projects in Honavar's laboratory along with selected publications which provide more detailed information. A more complete list of publications can be found here. This research has been partially supported through grants from the National Science Foundation, the National Institutes of Health, the Department of Defense, the Carver Foundation, the John Deere Foundation, Pioneer Hi-Bred, and Iowa State University as well as research fellowships from IBM Corporation, Pioneer Hi-Bred, Inc., and the Iowa State University Graduate College.


Algorithms and Software for Knowledge Acquisition from Heterogeneous, Distributed Data

Development of high throughput data acquisition technologies together with advances in computing, and communications have resulted in an explosive growth in the number, size, and diversity of potentially useful information sources. However, the massive size, heterogeneity, autonomy, and distributed nature of the data repositories present significant hurdles in extracting knowledge from this data. Honavar's research on this topic, supported in part by an Information Technology Research (ITR) grant from the National Science Foundation (0219699) and a graduate fellowship from IBM seeks to overcome these hurdles through the design, analysis, and implementation of:

The resulting algorithms are being applied to representative data-driven knowledge discovery problems drawn from computational molecular biology.


References

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

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

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

  4. Zhang, J., Kang, D-K., Silvescu, A. and Honavar, V. (2005). Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data. In: Knowledge and Information Systems.

  5. Zhang, J., Caragea, D. and Honavar, V. (2005). Learning Ontology-Aware Classifiers. In: Proceedings of the 8th International Conference on Discovery Science. Springer-Verlag Lecture Notes in Computer Science. Singapore. Vol. 3735. pp. 308-321. Berlin: Springer-Verlag.
  6. Caragea, D., Pathak, J., and Honavar, V. (2004). Learning Classifiers from Semantically Heterogeneous Data. In: Proceedings of the International Conference on Ontologies, Databases, and Applications of Semantics (ODBASE 2004), Agia Napa, Cyprus, 2004.

  7. Bao, J., Cao, Y., Tavanapong, W., and Honavar, V. (2004). Integration of Domain-Specific and Domain-Independent Ontologies for Colonoscopy Video Database Annotation. In: International Conference on Information and Knowledge Engineering (IKE 04).

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

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

  10. Caragea, D., Silvescu, A., and Honavar, V. (2004). 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. pp. 80-89.

  11. Kang, D-K., Silvescu, A., Zhang, J., and Honavar, V. (2004). Generation of Attribute Value Taxonomies from Data for Data-Driven Construction of Accurate and Compact Classifiers. In: Proceedings of the IEEE International Conference on Data Mining.

  12. Pathak, J., Caragea, D., and Honavar, V. (2004). Ontology-Extended Component-Based Workflows: A Framework for Constructing Complex Workflows from Semantically Heterogeneous Software Components. In: Proceedings of the Workshop on Semantic Web and Databases (SWDB-04). Springer-Verlag Lecture Notes in Computer Science. In press.

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

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

  15. Zhang, J. and Honavar, V. (2004). AVT-NBL - An Algorithm for Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data. In: Proceedings of the IEEE International Conference on Data Mining.

  16. Atramentov, A., Leiva, H., and Honavar, V. (2003). A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments.. In: Proceedings of the Thirteenth International Conference on Inductive Logic Programming. Berlin: Springer-Verlag.

  17. Caragea, D., Reinoso-Castillo, J., Silvescu, A. (2003). Statistics Gathering for Information Integration on the Web. In: Proceedings of the IJCAI-03 Workshop on Information Integration on the Web..

  18. Reinoso-Castillo, J., Silvescu, A., Caragea, D., Pathak, J. and Honavar, V. (2003). Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources: A Federated, Query-Centric Approach.. IEEE International Conference on Information Integration and Reuse.

  19. Zhang, J. and Honavar, V. (2003). Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data. In: Proceedings of the International Conference on Machine Learning (ICML-03). Washington, DC. In press.

  20. Reinoso-Castillo, J. (2002). Ontolgy-Driven Information Extraction and Integration from Autonomous, Heterogeneous, Distributed Data Sources -- A Federated Query-Centric Approach. Masters Thesis. Artificial Intelligence Research Laboratory. Department of Computer Science. Iowa State University.

  21. Zhang, J., Silvescu, A., and Honavar, V. (2002). Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction. In: Proceedings of Symposium on Abstraction, Reformulation, and Approximation. Berlin: Springer-Verlag.

  22. Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Towards a Theoretical Framework for Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.

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

  24. Caragea, D., Silvescu, A., and Honavar, V. (2000). Agents That Learn from Distributed Dynamic Data Sources. In: Proceedings of the ECML 2000/Agents 2000 Workshop on Learning Agents. Barcelona, Spain.

  25. Honavar, V., Miller, L. and Wong, J. (1998). Distributed Knowledge Networks. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.

Bioinformatics and Computational Molecular Biology


Research in Computational Biology seeks to develop algorithmic or information processing models of biological systems and processes such as genetic networks, protein folding, and protein-protein interaction. Research in bioinformatics is concerned with development of algorithms and software for organizing, processing, and analyzing experimental data and knowledge derived from the data to address specific questions in biological sciences. Honavar's current research in Bioinformatics and Computational Molecular Biology, is focused on development of computational tools for largescale collaborative data-driven knowledge discovery in biological sciences and the application of the resulting tools in data-driven exploration of macromolecular sequence-structure-expression-evolution-function relationships and inference of complex biological signalling networks and pathways. Much of this work is being carried out in collaboration with colleagues with expertise in molecular biology, biochemistry, genetics, and biophysics. This work is supported in part by an Information Technology Research (ITR) grant (0219699) from the National Science Foundation, an Integrative Graduate Education and Research Training (IGERT) award in Computational Molecular Biology (09972653) from the National Science Foundation, a Biological Information Science and Technology Initiative (BISTI) award (GM066387) from the National Institutes of Health and graduate fellowships from Pioneer Hi-Bred and IBM. Current research foci in this area include:


References

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

  2. Terribilini, M., Lee. J-H., Yan, C., Carpenter, S., Jernigan, R., Honavar, V. and Dobbs, D. (2006). Identifying interaction sites in recalcitrant proteins: predicted protein and rna binding sites in HIV-1 and EIAV agree with experimental data. In: Pacific Symposium on Biocomputing. Hawaii.

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

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

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

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

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

  8. Andorf, C., Silvescu, A., Dobbs, D., and Honavar, V. (2004). Probabilistic Graphical Models for Protein Function Classification. International Conference on Knowledge-Based Systems. Hyderabad, India.

  9. Bao, J., Cao, Y., Tavanapong, W., and Honavar, V. (2004). Integration of Domain-Specific and Domain-Independent Ontologies for Colonoscopy Video Database Annotation. In: International Conference on Information and Knowledge Engineering (IKE 04).

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

  11. Caragea, D., Silvescu, A., and Honavar, V. (2004). 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. pp. 80-89.

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

  13. 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. (2004). Predicting Binding Sites of Protease-Inhibitor Complexes by Combining Multiple Methods. In: BMC Bioinformatics. Vol. 5. pp. 205.

  14. Yan, C., Dobbs, D., and Honavar, V. A Two-Stage Classifier for Identification of Protein-Protein Interface Residues. Bioinformatics. In Press., 2004.

  15. Yan, C., Honavar, V. and Dobbs, D. (2004). Identifying Protein-Protein Interaction Sites from Surface Residues - A Support Vector Machine Approach.. Neural Computing Applications. In press.

  16. Zhang, J., and Honavar, V. (2004). Learning Naï Bayes Classifiers from Attribute Value Taxonomies and Partially Specified Data. In: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 2004).

  17. Atramentov, A., Leiva, H., and Honavar, V. (2003). A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments.. In: Proceedings of the Thirteenth International Conference on Inductive Logic Programming. Berlin: Springer-Verlag. In press.

  18. Caragea, D., Silvescu, A., and Honavar, V. (2003). Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources. In: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 03).

  19. Caragea, D., Reinoso-Castillo, J., Silvescu, A. (2003). Statistics Gathering for Information Integration on the Web. In: Proceedings of the IJCAI-03 Workshop on Information Integration on the Web..

  20. Reinoso-Castillo, J., Silvescu, A., Caragea, D., Pathak, J. and Honavar, V. (2003). Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources: A Federated, Query-Centric Approach.. IEEE International Conference on Information Integration and Reuse. To appear.

  21. Wang, X., Schroeder, D., Dobbs, D., and Honavar, V. (2003). Automated Data-Driven Discovery of Motif-Based Protein Function Classifiers. Information Sciences. In press.

  22. Yan, C., Dobbs, D., and Honavar, V. (2003). Identification of Surface Residues Involved in Protein-Protein Interaction -- A Support Vector Machine ApproachIn: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA-03). Tulsa, Oklahoma. 2003.

  23. Zhang, J. and Honavar, V. (2003). Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data. In: Proceedings of the International Conference on Machine Learning (ICML-03). Washington, DC.

  24. Yan, C., Honavar, V., and Dobbs, D. (2002). Predicting Protein-Protein Interaction Sites From Amino Acid Sequence. To appear.

  25. Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Towards a Theoretical Framework for Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.

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

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

  28. Yang, J., Parekh, R., Honavar, V., and Dobbs, D. (1999). Data-Driven Theory Refinement Algorithms for Bioinformatics. In: Proceedings of the International Joint Conference on Neural Networks. Washington, D.C.

Grammatical Inference and Language Acquisition


Automata induction, the task of infering an unknown grammar (or equivalently, the corresponding recognition device) from examples finds applications in several areas including structural pattern recognition, language learning, information retrieval and computational biology. Honavar's research on grammar inference explores the design and analysis of algorithms for induction of regular grammars within different models of interaction between the learner and the environment. Of particular interest are models of language learning from simple examples, induction of large regular grammars, as well acquisition of semantics along with syntax of natural as well as artificial languages.

More recent work on language modeling has focused on probabilistic generative models and their applications in sequence classification.

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

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

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

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

  5. Andorf, C., Silvescu, A., Dobbs, D., and Honavar, V. (2004). Probabilistic Graphical Models for Protein Function Classification. In: Proceedings of the Conferenc on Knowledge-Based Computer Systems, Hyderabad, India.

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

  7. Parekh, R. and Honavar, V. (2000). On the Relationships between Models of Learning in Helpful Environments.i. In: Proceedings of the Fifth International Conference on Grammatical Inference. Lecture Notes in Artificial Intelligence Vol. 1891. Berlin: Springer-Verlag. pp. 207-220.

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

  9. Parekh, R. and Honavar, V. (1999). Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples. In: Proceedings of the International Conference on Machine Learning. Bled, Slovenia.

  10. Parekh, R., Nichitiu, C., and Honavar, V. (1998). A Polynomial Time Incremental Algorithm for Learning DFA. In: Proceedings of the Fourth International Colloquium on Grammatical Inference (ICGI'98), Ames, IA. Lecture Notes in Computer Science vol. 1433 pp. 37-49. Berlin: Springer-Verlag.

Distributed Intelligent Multi-Agent Systems

Intelligent agents and multi-agent systems offer a particularly attractive paradigm for the design, analysis, and implementation of complex, flexible, and scalable information systems consisting of large numbers of autonomous entities (information sources, clients). Honavar's current research in distributed intelligent multi-agent systems supported in part by an Information Technology Research (ITR) grant (0219699) from the National Science Foundation an Integrative Graduate Education and Research Training (IGERT) award in Computational Molecular Biology (09972653) from the National Science Foundation, a Biological Information Science and Technology Initiative (BISTI) award (GM066387) from the National Institutes of Health and graduate fellowships from Pioneer Hi-Bred and IBM, is focused on the principles, design, and implementation of multiagent systems for for information extraction, data-driven knowledge discovery, data and knowledge organization and assimilation, collaborative distributed problemsolving, decision support. Some of this work is motivated by specific applications in collaborative scientific discovery in computational biology, organizational decision support, self-managing communication networks, distributed energy networks, distributed sensor networks, and open-ended information-rich environments (Semantic Web). Some topics of current emphasis include:


References

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

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

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

  4. Wang, Y., Behera, S., Wong, J., Helmer, G., Honavar, V., Miller, L. and Lutz, R. (2005). Towards the automatic generation of mobile agents for distributed intrusion detection systems. Journal of Systems & Software, In press.

  5. Zhang, J., Kang, D-K., Silvescu, A. and Honavar, V. (2005). Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data. In: Knowledge and Information Systems.

  6. Zhang, J., Caragea, D. and Honavar, V. (2005). Learning Ontology-Aware Classifiers. In: Proceedings of the 8th International Conference on Discovery Science. Springer-Verlag Lecture Notes in Computer Science. Singapore. Vol. 3735. pp. 308-321. Berlin: Springer-Verlag.
  7. Caragea, D., Pathak, J., and Honavar, V. (2004). Learning Classifiers from Semantically Heterogeneous Data. In: Proceedings of the International Conference on Ontologies, Databases, and Applications of Semantics (ODBASE 2004), Agia Napa, Cyprus, 2004.

  8. Caragea, D., Silvescu, A., and Honavar, V. (2004). 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. pp. 80-89.

  9. Caragea, D., Pathak, J. and Vasant Honavar. Learning Classifiers from Semantically Heterogeneous Data. In: VLDB 2004 Workshop on the Semantic Web and Databases (SWDB 2004). To appear.

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

  11. Caragea, D., Silvescu, A., and Honavar, V. (2003). Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources. In: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 03).

  12. Reinoso-Castillo, J., Silvescu, A., Caragea, D., Pathak, J. and Honavar, V. (2003). Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources: A Federated, Query-Centric Approach.. IEEE International Conference on Information Integration and Reuse. To appear.

  13. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2002). Lightweight Agents for Intrusion Detection. Journal of Systems and Software. In press.

  14. Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.

  15. Mikler, A., Honavar, V. and Wong, J. (2001). Autonomous Agents for Coordinated Distributed Parameterized Heuristic Routing in Large Dynamic Communication Networks.. Journal of Systems and Software. Vol. 56. pp. 231-246. A preliminary version appeared in [mikler96].

  16. Viswanathan, V., McCalley, J., and Honavar, V. (2001). A Multi-agent System Infrastructure and Negotiation Framework for Electric Power Systems. In: Proceedings of the IEEE Power Technology Conference, Porto, Portugal, 2001.

  17. Wong, J., Helmer, G., Naganathan, V. Polavarapu, S., Honavar, V., and Miller, L. (2001) SMART Mobile Agent Facility. Journal of Systems and Software. Vol. 56. pp. 9-22.

  18. Chung, M. and Honavar, V. A Negotiation Model for Electronic Commerce, In: Proceedings of the IEEE Symposium on Multimedia Software Engineering. 2000.

  19. Honavar, V., Miller, L. and Wong, J. (1998). Distributed Knowledge Networks. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.

  20. Yang, J., Pai, P., Honavar, V., and Miller, L. (1998). Mobile Intelligent Agents for Document Classification and Retrieval: A Machine Learning Approach. In: Proceedings of the European Symposium on Cybernetics and Systems Research.

  21. Yang, J., Havaldar, R., Honavar, V., Miller, L. and Wong, J. (1998). Coordination and Control of Distributed Knowledge Networks Using the Contract Net Protocol. In: Proceedings of the IEEE Information Technology Conference. Syracuse, NY.

Data Mining and Knowledge Discovery - Algorithms and Applications

Data Mining is concerned with the development and applications of algorithms for discovery of a priori unknown relationships - associations, groupings, classifiers from data. Honavar's current research on data mining, supported in part by an Information Technology Research (ITR) grant (0219699) from the National Science Foundation, an Integrative Graduate Education and Research Training (IGERT) award in Computational Molecular Biology (09972653) from the National Science Foundation, a Biological Information Science and Technology Initiative (BISTI) award (GM066387) from the National Institutes of Health and graduate fellowships from Pioneer Hi-Bred and IBM is focused on:


References

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

  2. Bromberg, F., Margaritis, D., and Honavar, V. (2006). Efficient Markov Network Structure Discovery from Independence Tests. In: SIAM Conference on Data Mining (SDM 06). SIAM Press. To appear.

  3. Pathak, J, Yong, J. Honavar, V., McCalley, J. (2006). Condition Data Aggregation for Failure Mode Estimation of Power Transformers. In: Hawaii International Conference on Systems Sciences.

  4. Terribilini, M., Lee. J-H., Yan, C., Carpenter, S., Jernigan, R., Honavar, V. and Dobbs, D. (2006). Identifying interaction sites in recalcitrant proteins: predicted protein and rna binding sites in HIV-1 and EIAV agree with experimental data. In: Pacific Symposium on Biocomputing. Hawaii. In press.

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

  6. Silvescu, A. and Honavar, V. (2005). Independence, Decomposability and functions which take values into an Abelian Group. In: Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics. http://anytime.cs.umass.edu/aimath06/proceedings.html.

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

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

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

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

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

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

  13. Zhang, J., Kang, D-K., Silvescu, A. and Honavar, V. (2005). Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data. In: Knowledge and Information Systems.

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

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

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

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

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

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

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

  21. 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. (2004). Predicting Binding Sites of Protease-Inhibitor Complexes by Combining Multiple Methods. In: BMC Bioinformatics. Vol. 5. pp. 205.

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

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

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

  25. Atramentov, A., Leiva, H., and Honavar, V. (2004). A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments.. In: Proceedings of the Thirteenth International Conference on Inductive Logic Programming. Berlin: Springer-Verlag. In press.

  26. Caragea, D., Silvescu, A., and Honavar, V. (2003). Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources. In: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 03). In press.

  27. Wang, X., Schroeder, D., Dobbs, D., and Honavar, V. (2003). Automated Data-Driven Discovery of Motif-Based Protein Function Classifiers. Information Sciences. In press.

  28. Yan, C., Dobbs, D. (2003). Identification of Surface Residues Involved in Protein-Protein Interaction -- A Support Vector Machine ApproachIn: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA-03). Tulsa, Oklahoma. 2003.

  29. Zhang, J. and Honavar, V. (2003). Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data. In: Proceedings of the International Conference on Machine Learning (ICML-03). Washington, DC.

  30. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2003). Lightweight Agents for Intrusion Detection. Journal of Systems and Software. Vol. 67. pp. 109-122.

  31. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2002). Automated Discovery of Concise Predictive Rules for Intrusion Detection. Journal of Systems and Software.60 (3) (2002) pp. 165-175

  32. Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Towards a Theoretical Framework for Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.

  33. Caragea, D., Cook, D., and Honavar, V. (2001). Gaining Insights into Support Vector Machine Classifiers Using Projection-Based Tour Methods. In: Proceedings of the Conference on Knowledge Discovery and Data Mining.

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

  35. Polikar, R., Shinar, R., Honavar, V., Udpa, L., and Porter, M. (2001). Detection and Identification of Odorants Using an Electronic Nose. In: Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing.

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

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

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

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

  40. Yang, J. and Honavar, V. (1998). Feature Subset Selection Using a Genetic Algorithm. In: Feature Extraction, Construction, and Subset Selection: A Data Mining Perspective. Motoda, H. and Liu, H. (Ed.) New York: Kluwer. 1998. A shorter version of this paper appears in IEEE Intelligent Systems (Special Issue on Feature Transformation and Subset Selection).

  41. Balakrishnan, K. and Honavar, V. (1998). Intelligent Diagnosis Systems. Journal of Intelligent Systems. Vol. 8. No.3/4. pp. 239-290.


Neural and Cognitive Modeling

Computational or information processing models offer an attractive approach to understanding memory, learning, and behavior in biological systems and provide a rich source of ideas for realizing similar capabilities in engineered systems. Honavar's research on cognitive and neural modeling is focused on:

References

  1. Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.

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

  3. Polikar, R., Shinar, R., Honavar, V., Udpa, L., and Porter, M. (2001). Detection and Identification of Odorants Using an Electronic Nose. In: Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing.

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

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

  6. Yang, J., Parekh, R. & Honavar, V. (2000). Comparison of Performance of Variants of Single-Layer Perceptron Algorithms on Non-Separable Data. Neural, Parallel, and Scientific Computation. Vol. 8. pp. 415-438.

  7. Chen, C-H. & Honavar, V. (1999). A Neural Architecture for Syntax Analysis. IEEE Transactions on Neural Networks. Vol. 10 pp. 94-114.

  8. Chen, C-H. & Honavar, V. (1999). A Neural Architecture for Information Retrieval and Query Processing. Invited chapter. In: Handbook of Natural Language Processing. Dale, Moisl & Somers (Ed). New York: Marcel Dekker.

  9. Chen, C-H. and Honavar, V. (1995). A Neural Memory Architecture for Content as well as Address-Based Storage and Recall: Theory and Applications Connection Science. vol. 7. pp. 293-312.

  10. Honavar, V. (1994). Symbolic Artificial Intelligenc e and Numeric Artificial Neural Networks: Toward a Resolution of the Dichotomy. Invited chapter. In: Computational Architectures Integrating Symbolic and Neural Processes. pp. 351-388. Sun, R. and Bookman, L. (Ed.) New York: Kluwer.

  11. Honavar, V. and Uhr, L. (1990). Coordination and Control Structures and Processes: Possibilities for Connectionist Networks. Journal of Experimental and Theoretical Artificial Intelligence 2: 277-302.

  12. Honavar, V. and Uhr, L. (1989). Brain-Structured Connectionist Networks that Perceive and Learn. Connection Science 1: 139-160.

Evolutionary Synthesis of Intelligent Agents

Evolutionary algorithms (genetic algorithms, evolution strategies, genetic programming, evolutionary programming) offer an attractive paradigm for automated synthesis of sensory, behavior, and control structures for robots and intelligent agents. Honavar's recent research in this area has focused on automated synthesis of sensor systems, and computational architectures for reactive and deliberative behavior in intelligent autonomous agents and robots under a variety of cost, performance, and environmental constraints. Related research explores evolution of communication, cooperation, and language in communities of intelligent agents.

References

  1. Balakrishnan, K. & Honavar, V. (2001). Experiments in Evolutionary Robotics. In: Advances in Evolutionary Synthesis of Neural Systems. Patel, M. & Honavar, V. (Ed). Cambridge, MA: MIT Press.

  2. Balakrishnan, K., and Honavar, V. (2001). Evolutionary Synthesis of Intelligent Agents. In: Advances In the Evolutionary Synthesis of Intelligent Agents. Patel, M. Honavar, V., and Balakrishnan, K. (ed). Cambridge, MA: MIT Press.

  3. Balakrishnan, K. & Honavar, V. (1999). Evolutionary Synthesis of Sensor Systems and Controllers. Invited chapter In: Evolutionary Computing Techniques in System Design Jain, L. (Ed.), New York: CRC Press.

  4. Yang, J. and Honavar, V. (1998). Feature Subset Selection Using a Genetic Algorithm. Invited chapter. In: Feature Extraction, Construction, and Subset Selection: A Data Mining Perspective. Motoda, H. and Liu, H. (Ed.) New York: Kluwer. 1998.

  5. Yang, J. and Honavar, V. (1998). Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems (Special Issue on Feature Transformation and Subset Selection). vol. 13. pp. 44-49.

Security Informatics

Ensuring the security of networked computing and information infrastructure poses,several research challenges that cut across different areas of computer science including distributed computing, formal specifications, software engineering, and artificial intelligence. Research in Honavar's laboratory on information assurance and computer security is focused on specification, design, and agent-based distributed implementation of adaptive systems for coordianated intrusion detection and counter-measures. This research has been supported in part by a grant from the Department of Defense.

References

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

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

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

  4. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2003). Lightweight Agents for Intrusion Detection. Journal of Systems and Software. Vol. 67. pp. 109-122.

  5. Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., and Lutz, R. (2002) A Software Fault Tree Approach to Requirements Specification of an Intrusion Detection System. Requirements Engineering. Vol 7 (4) (2002) pp. 207-220.

  6. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2002). Automated Discovery of Concise Predictive Rules for Intrusion Detection. Journal of Systems and Software.60 (3) (2002) pp. 165-175

  7. Helmer, G., Wong, J., Honavar, V., and Miller, L. (2002). Lightweight Agents for Intrusion Detection. Journal of Systems and Software. In press.

  8. Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., and Lutz, R. Colored Petri Net Based Specification, Design and Implementation of Agent-Based Intrusion Detection Systems. Draft Under revision.

  9. Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L. and Lutz, R. (2001). A Software Fault Tree Approach to Requirements Analysis of an Intrusion Detection System. In: Proceedings of the Symposium on Requirements Engineering for Information Security, Indianapolis, IN, USA.

  10. Helmer, J., Wong, J., Honavar, V., and Miller, L. (1998) Intelligent Agents for Intrusion Detection and Countermeasures. In: Proceedings of the IEEE Information Technology Conference. pp. 121-124.

Other Topics of Current Interest:

Other topics of interest include: Computational Models of Discovery, Computational Learning Theory, Computational Models of Creativity, Computational Semiotics, Computational Game Theory, Applications of information theory and complexity theory (in particular, Kolmogorov complexity, minimum description length, and related topics) in computational learning theory and biology, Knowledge Representation and Inference, Computational Organization Theory.

A more complete list of publications can be found here

Books

  1. Patel, M., Honavar, V., and Balakrishnan, K. (2001). Advances in the Evolutionary Synthesis of Intelligent Agents Cambridge, MA: MIT Press.

  2. Honavar, V. and Slutzki, G. (1998) (Ed.). Proceedings of the Fourth International Colloquium on Grammatical Inference. (LNCS Vol. 1433). Berlin: Springer-Verlag.

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

  4. Banzaf, W., Daida, J., Eiben, A. Garzon, M., Honavar, V., Jakiela, M., & Smith, R. (Ed.) (1999). Proceedings of the Genetic and Evolutionary Computation Conference. San Mateo, CA: Morgan Kaufmann.

  5. W. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. Potter, A. Schultz, J. Miller, E. Burke, N. Jonoska. (2002). (Ed). Proceedings of the Genetic and Evolutionary Computing Conference. Palo Alto, CA: Morgan Kaufmann.

  6. H. J. Caulfield, S.-H. Chen, H.-D. Cheng, R. Duro, V. Honavar, E. E. Kerre, M. Lu, M. G. Romay, T. K. Shih, D. Ventura, P. P. Wang, and Y. Yang, (2002) (Ed). Proceedings 6th Joint Conference on Information Sciences, JCIS / Association for Intelligent Machinery.

  7. Honavar, V. & Parekh, R., & de la Higuera, C. (Ed.) (2004). Advances in Automata Induction, Grammar Inference, and Language Acquisition. To appear.

  8. Honavar, V. Knowledge Acquisition from Heterogeneous, Distributed, Autonomous Data Sources. (2006). (with contributions from D. Caragea, J. Bao, and J. Zhang) To appear.
Current and Former Students

Current Ph.D. Students

  1. Tim Alcon (Honavar, Greenlee). Interests: Computational Systems Biology, Proteomics, Transcriptomics, Bioinformatics. Tim is supported by a Multi-disciplinary Graduate Education and Training (MGET) award funded by the USDA. Expected graduation: Summer 2008.
  2. Carson Andorf (Honavar). Interests: Data Mining, Bioinformatics, Machine Learning, Distributed Knowledge Networks, Intelligent Agents and Multi-agent systems, Gene Expression Analysis. Carson is supported by an IGERT fellowship funded by the National Science Foundation and a research assistantship funded by the National Institutes of Health. Expected Graduation: Summer 2006.
  3. Jie Bao (Honavar). Interests: Machine Learning, Multi-agent Learning, Multi-agent Interaction, Computational Learning Theory, Cooperative Learning, Ontology Learning. Data Mining. Jie Bao is supported by a Research Assistantship in Computer Science funded by the ISU Graduate college and the National Science Foundation. Expected Graduation: Summer 2007.
  4. Yasser El-Manzalawy (Honavar). Interests: Machine Learning, Data Mining, Probabilistic Relational Models, Semantic Web. Yasser is supported by a fellowship from the Egyptian Government. Expected graduation: Summer 2009.
  5. Dae-Ki Kang (Honavar). Interests: Ontology Learning, Learning from Relational Data, Probabilistic Relational Models, Security Informatics. Dae-Ki is funded by a Teaching assistantship from the Department of Computer Science and a Research assistantship funded by the National Science Foundation. Expected Graduation: Summer 2006.
  6. Jyotishman Pathak (Honavar). Interests: Information Integration from Semantically Heterogeneous Distributed Data Sources, Workflow Composition, Web Services, Data Mining. Jyotish is supported by a graduate research assistantship funded by PSERC (a National Science Foundation Power Systems Engineering Research Center). Expected Graduation: Summer 2007.
  7. Jaime Reinoso-Castillo (Honavar). Interests: Distributed Databases, Data Mining, Intelligent Agents and Multiagent Systems. Jaime is supported by a Fullbright Scholarship and a research assistantship funded by the National Science Foundation. Jaime completed his M.S. in 2002. He plans to be back for his Ph.D. in June 2006.
  8. Adrian Silvescu (Honavar). Interests: Machine Learning, Data Mining, Ontology Learning, Probabilistic Relational Models, Computational Biology and Bioinformatics. Adrian has been supported through a teaching assistantship from the Department of Computer Science and research assistantships funded by Pioneer Hi-Bred and the National Science Foundation. Expected Graduation: Fall 2006.
  9. Cornelia Caragea (Honavar). Interests: Machine Learning, Data Mining, Probabilistic Relational Models. Cornelia is being supported by a teaching assistantship in Computer Science. Expected Graduation: Spring 2008.
  10. Kent Vander Velden (Reilley and Honavar). Interests: Systems Biology, Metabolic Networks, Computational Biology. Kent is an NSF IGERT fellow. He is at present a research scientist at Pioneer Hi-Bred and his doctoral work is supported by Pioneer Hi-Bred. Expected Graduation: Fall 2006.
  11. Flavian Vasile (Honavar). Interests: Machine Learning, Human-Computer Interaction, and the Semantic Web. Flavian is being supported by a teaching assistantship in Computer Science. Expected Graduation: Spring 2008.
  12. Feihong Wu (Honavar and Jernigan). Feihong is interested in Bioinformatics and Computational Biology and Characterization of Protein Sequence-Structure-Function Relationships. Feihong has been funded by a research assistantship from the Bioinformatics and Computational Biology (BCB) graduate program.
  13. Oksana Yakhnenko (Honavar). Interests: Machine Learning, Multi-relational learning, Learning from Semantically Heterogeneous data, Probabilistic Relational Models, Data Visualization, Learning from Sequence Data. Oksana is funded by a graduate teaching assistantship in Computer Science and a research assistantship funded by the National Science Foundation. Expected Graduation: Spring 2008.

Current M.S. Students

  1. Mgavi Braithwaite (Honavar). Interests: Bioinformatics and Computational Biology, Data Mining, Systems Biology. Mgavi is supported by an IGERT fellowship funded by the National Science Foundation and a George Washington Carver Fellowship from Iowa State University. Expected Graduation: Summer 2006.
  2. Oksana Kohutyuk (Honavar). Interests: Machine Learning, Computational Molecular Biology, Probabilistic Graphical Models, Gene Expression Analysis. Oksana is supported by a research assistantship funded by the National Institutes of Health.

Rotation Students (Graduate)

None at present

Undergraduates and Summer Research Students

None at Present

Alumni

Ph.D. Graduates

  1. Changhui Yan (Honavar and Dobbs). Ph.D. 2005. Thesis: Analysis and Computational Prediction of Protein-Protein and Protein-DNA Interfaces. Changhui was supported by a Plant Sciences Fellowship and a research assistantships funded by the ISU Graduate College and a grant from the National Institutes of Health. Current Position: Assistant Professor of Computer Science, Utah State University.
  2. Jun Zhang (Honavar). Ph.D. 2005. Thesis: Learning Ontology Aware Classifiers. Jun was supported by a teaching assistantship in Computer Science and research assistantships funded by the Iowa State University Graduate College and the National Science Foundation. Current Position: Data Mining Research Scientist, Fair Isaac, San Diego.
  3. Doina Caragea (Honavar). Ph.D. 2004. Thesis: Learning Classifiers From Distributed, Semantically Heterogeneous, Autonomous Data Sources. Doina was supported through research assistantships funded by the ISU Graduate College and the National Science Foundation. Doina also received the IBM Research Fellowship during 2002-2003 and 2003-2004. Initial Employment: Research Fellow, Computational Intelligence, Learning, and Discovery Program, Iowa State University.
  4. Jihoon Yang (Honavar) Ph.D. 1999. Thesis: Intelligent Information Retrieval and Knowledge Discovery Agents.. Jihoon has been supported by a CS Teaching Assistantship and Research Assistantships funded by the ISU Graduate College and the John Deere Foundation. Initial Employment: Research Scientist, Information Sciences Laboratory, Hughes Research Laboratory, Malibu, CA. Current Position: Assistant Professor, Computer Science, Sogang University, Korea.
  5. Karthik Balakrishnan, Ph.D. 1998. Thesis: Biologically Inspired Information Processing Structures for Autonomous Agents and Robots. Karthik was supported through CS Teaching assistantship, Research assistantships funded by the National Science Foundation and the John Deere Foundation. Karthik received an IBM Research Fellowship during 1997-1998, and a Research Excellence Award from the Iowa State University Graduate College. Initial Employment: Research Scientist, Allstate Research and Planning Center, Menlo Park, CA. Current Position: Director of Analytics, Fireman's Fund Insurance, Novato, CA.
  6. Rajesh Parekh, 1998. Constructive Learning Algorithms: Inducing Grammars and Neural Networks. Rajesh was supported by a CS teaching assistantship, and a research assistantship funded by the National Science Foundation. . Rajesh received a Graduate Research Excellence award from the Iowa State University graduate college. Initial Employment: Research Scientist, Allstate Research and Planning Center, Menlo Park. Current Position: Senior Data Mining Engineer, Blue Martini Software, San Mateo, CA.
  7. Chun-Hsien Chen, 1997. Thesis: Neural Architectures for Associative Memory, Syntax Analysis, Knowledge Representation, and Inference. Chun-Hsien was funded by a project assistantship at the Division of Extended and Continuing Education. Initial Employment: Research Scientist, Computer and Communications Research Laboratories, Industrial Technology Research Institute, Taiwan. Current Position: Associate Professor, Department of Information Management, Chang Gung University, Taiwan.
  8. Armin Mikler, 1995. (Co-advised with Johnny Wong). Thesis: Quo-Vadis - A Framework for Intelligent Routing in Large Communication Networks. Armin was supported by a CS teaching assistantship and a project assistantship at the Center for Agricultural and Rural Development. Initial Employment: Postdoctoral Research Fellow, Scalable Computation Laboratory, DOE Ames Laboratory, Ames, IA. Current Position: Associate Professor, Department of Computer Science, University of North Texas, Denton.

M.S. Graduates

  1. Charles Gieseler (Honavar, Tesfatsion). M.S. 2005. Thesis: A Java Reinforcement Learning Module for the Recursive Porous Agent Simulation Toolkit. Current Position: Lawrence Livermore Labs.
  2. Anna Atramentov (Honavar). Thesis: Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments. Anna was supported by a Teaching Assistantship in Computer Science and Research Assistantship funded by the Graduate College. Current position: Ph.D. Student, Computer Science, University of Illinois at Urbana-Champaign.
  3. Zhong Gao (Honavar and Ho). Thesis: Genome wide recognition of tumor necrosis factor (TNF) like ligands in human and Arabidopsis genomes: A structural Threading Approach. Current position: Post-doctoral Associate, The Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University.
  4. Hector Leiva (Honavar). 2002. Thesis: Multi-Relational Decision Tree Learning. Hector was supported by a Fullbright Fellowship and a Graduate Teaching Assistantship in Computer Science. Current Position: Research Scientist, Universidad Nacional de San Luis. Argentina.
  5. Jaime Reinoso-Castillo (Honavar). Thesis: Ontolgy-Driven Information Extraction and Integration from Autonomous, Heterogeneous, Distributed Data Sources -- A Federated Query-Centric Approach. Thesis. Jaime was supported by a Fullbright Scholarship and a research assistantship funded by the National Science Foundation. Current Position: Universidad Javeriana, Colombia.
  6. Xiaosi Zhang. 2002. Interests: Bioinformatics and Computational Biology. Gene Expression Analysis. Xiaosi was supported by a graduate assistantship funded by a grant from the Carver Foundation. Current Position: Papajohn Center for Entreprenuership.
  7. Xiangyun Wang (Honavar). 2002. Interests: Bioinformatics and Computational Biology. Gene Expression Analysis. Macromolecular Structure-Function Prediction from Sequence Data. Xiangyun was supported by a fellowship from the ISU graduate college and a teaching assistantship in Computer Science. Current Position: Research Scientist, Astra Zeneca, Inc.
  8. Kent Vander Velden 2002. (Gavin Naylor's lab). Interests: Bioinformatics and Computational Biology, Characterization of Molecular Structure-Function relationships, Phylogenetics. Kent was supported by an NSF IGERT Fellowship in Computational Molecular Biology. Current Status: Member of Research Staff, Pioneer Hi-Bred, Inc.
  9. Neeraj Koul. 2001. Thesis: Clustering with Semi-Metrics. Neeraj was supported by a research assistantship in Computer Science funded by the Carver Foundation and a Teaching Assistantship in Computer Science. Current Position: Motorola.
  10. Rushi Bhatt. 2001. Thesis: Spatial Learning and Localization. Rushi was supported by the ISU Neuroscience Graduate Fellowship and a Teaching Assistantship in Computer Science. Current Status: Ph.D. Student, Boston University.
  11. Dake Wang. 2001. Dake was supported by a teaching assistantship in Computer Science and a research assistantship funded by the Carver Foundation. Initial Employment: Lumicyte Inc.
  12. Asok Tiyyagura. Project: Mutual Information Based Association Rule Mining. Asok has been supported through a research assistantship funded by the ISU Council on International Programs and EPRI and a teaching assistantship in Computer Science. Initial Employment: Cisco Systems.
  13. Fajun Chen. 2000. Thesis: Learning Information Extraction Patterns. Fajun has been supported through a research assistantship funded by the ISU Graduate College and a Teaching Assistantship in Computer Science. Initial Employment: Ericsson.
  14. Tarkeshwari (Taru) Trivedi. 2000. Thesis: An Agent Toolkit for Distributed Knowledge Networks. Taru was supported through a teaching assistantship in Computer Science and a research assistantship funded by the John Deere Foundation. Initial Employment: Motorola.
  15. Di Wang, 1998. Project: Mobile Agents for Information Retrieval. Initial Employment: Consultant, Canada.
  16. Shane Konsella, 1997. Thesis: Trie Compaction Using Genetic Algorithms. Shane was supported by a teaching assistantship in Computer Science. Initial Employment: Hewlett-Packard.
  17. Karthik Balakrishnan, 1993. Project: Faster Learning Approximations of Backpropagation by Handling Flat-Spots. Karthik was supported by a teaching assistantship in Computer Science and a research assistantship funded by the ISU graduate College. (Stayed on as a Ph.D. Student).
  18. Rajesh Parekh, 1993. Project: Efficient Learning of Regular Languages Using Teacher-Supplied Positive Examples and Learner-Generated Queries. Rajesh was suported by a teaching assistantship in Computer Science. (Stayed on as a Ph.D. Student).
  19. Jayathi Janakiraman, 1993. Project: Adaptive Learning Rate for Increasing Learning Speed in Backpropagation Networks. Jayathi was supported by a teaching assistantship in Computer Science. Initial Employment: Motorola.
  20. Priyamvadha Thambu, 1993. Thesis: Automated Knowledge-Base Consistency Maintenance in an Evolving Intelligent Advisory System. Priya was supported by a research assistantship funded by Iowa Department of Housing and Urban Development. Initial Employment: Inference Corporation.
  21. Richard Spartz, 1992. Project: Speeding Up Backpropagation Using Expected Source Values. Rich was supported by a teaching assistantship in Computer Science. Initial Employment: IBM Rochester.

Undergraduates and Summer Research Students

  1. Oksana Yakhnenko. 2004. Interests: Artificial Intelligence, Machine Learning, Data Mining, Data and Knowledge Visualization. Undergraduate Researcher, 2003-2004. Oksana is now pursuing a Ph.D. in Computer Science at Iowa State University.
  2. Amy Nienaber, Participant, Summer Institute in Bioinformatics and Computational Biology Summer 2003. Interests: Computational Biology.
  3. Matthew Beard, Participant, Summer Institute in Bioinformatics and Computational Biology Summer 2003. Interests: Compuational Biology.
  4. Diane Schroeder. 2002. Undergraduate research assistant 2000-2002. Interests: Bioinformatics and Computational Biology; Data Mining. Diane is now pursuing a Ph.D. at Stanford University.
  5. Carl Pecinovsky, 1997. Undergraduate Research Assistant 1996-1997. Employed at IBM Rochester, MN.
  6. Jeremy Ludwig, 1997. Undergraduate Research Assistant 1996-97. Graduate School: University of Pittsburgh.
  7. Todd Lindsey, 1996. Undergraduate Research Assistant, 1995-1996.
  8. Eric Barsness, 1993. Employed at IBM Rochester, MN.
Pre-College Students
  1. John Farragher. Summer program for gifted pre-college students 1992.
  2. Stephen Lee. Summer program for gifted pre-college students 1993.
  3. Anna Keyte, Angel Sheriff, Kellan Brumback, Sara Karbeling, 1997. Adventures in Supercomputing (AIS 1997) Team.
  4. Eric Solan, Nic Dayton, Luke Rolfes, and Julian Sheldahl. Adventures in Supercomputing (AIS 1998) Team.

Teaching

Catalog descriptions and information about scheduled offerings for the courses can be found here.

During Spring 1996, I cotaught (ComS 610 / CPRE 590B)- a seminar on Intelligent High-Speed Communication Networks with Professors Wong (CS) and Tridandapani (CprE) and Dr. Mikler (Ames Lab).

In Fall 1996, I ran the Graduate Orientation Seminar (ComS 591).

In Fall 1997, I co-taught (with over a half-dozen other faculty), a graduate seminar in Computational Molecular Biology.

Professional Activities