Kang, D-K., Silvescu, A. and Honavar, V. (2006) 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.

Silvescu, A. and Honavar, V. (2006) Independence, Decomposability and functions which take values into an Abelian Group. Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics, 2006.

Terribilini, M., Lee, J.H., Yan, C., Jernigan, R., Honavar, V., and Dobbs, D. (2006). Computational Prediction of Protein-RNA Interfaces. RNA Journal.. Vol. 12. No. 1450. pp. 1462, 2006.

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

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

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

Yakhnenko, O., Silvescu, A., and Honavar, V. (2005). Discriminatively Trained Markov Model for Sequence Classification. In: Proceedings of the IEEE Conference on Data Mining (ICDM 2005). IEEE Press. pp. 498-505.

R. Polikar, L. Udpa, S. Udpa, and V. Honavar (2004). 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.

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. Washington, DC. AAAI Press. pp. 880-887.

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

XIAOQIU HUANG, Associate Professor of Computer Science

Ph.D. 1990, Computer Science, Pennsylvania State University

Major Interests:

Bioinformatics and computational biology: design and applications of computational methods for understanding the structure and evolution of genomes.

Current Research:

The long term goal of Dr. Huang's research is to develop computational methods for finding functional elements in genomes. He has worked on two major problems: genome assembly and alignment.

Genome Assembly

Dr. Huang and collaborators at Genome Sequencing Center (GSC) of Washington University in St. Louis have developed a packaged named PCAP for reconstructing long genome sequences from short DNA sequences generated in whole-genome shotgun sequencing projects. The PCAP package has been used at GSC in a number of large-scale genome sequencing projects including the chimpanzee and chicken genome projects. Dr. Huang has also developed a sequence assembly program named CAP3.The CAP3 program has been used in thousands of academic labs for assembly of sequences from gene coding regions of the genome. PCAP and CAP3 are freely available for academic use at http://seq.cs.iastate.edu.

Genome assembly is a challenging computational problem. An input data set from a mammalian genome consists of up to 40 millions of sequences with a total size of 30 GB. A genome assembly program takes several days to produce a draft assembly from the input data set on hundreds of processors. Dr. Huang and collaborators continue to make accuracy and efficiency improvements to the PCAP package and to develop computational methods for sequence data from new genome sequencing technology.

Genome Alignment

Once the DNA sequence of a genome is determined, computational methods are used to find genes and regulatory elements in the genome. A very effective computational method for finding genes and regulatory elements in the genome is to compare the new genome sequence with existing genome sequences from other species, where similar regions between the new genome and existing genomes are located and aligned. The genome alignment method is based on the evolutionary law that genes and regulatory elements in the genomes of related species are much more conserved than the other regions of the genomes.

Dr. Huang and collaborators have designed a number of alignment algorithms and implemented the algorithms in computer programs available to biologists for analysis of DNA and protein sequences. A program named SIM computes a number of optimal local alignments between two sequences in linear space. The SIM program has been used in thousands of academic labs for analysis of protein sequences. A program named GAP3 is based on a dynamic programming algorithm specially designed to handle introns in genomic DNA sequences. Fast comparison programs based on a lookup table or a superword array (an efficient variation of a suffix array) have been developed by Dr. Huang and collaborators.

A package named AAT of alignment and comparison programs has been used for many years at The Institute for Genomic Research for finding genes in a genome. Many of the programs can be used at http://deepc2.psi.iastate.edu/aat/sas.html. Dr. Huang and collaborators continue to design better alignment models and algorithms for producing genome alignments.

See http://www.cs.iastate.edu/~xqhuang for details.

Representative Publications:

Huang, X., Yang, S.-P., Chinwalla, A., Hillier, L., Minx, P., Mardis, E. and Wilson, R.(2006). Application of a Superword Array in Genome Assembly. Nucleic Acids Research, 34:201-205.

Wang, J. and Huang, X.(2005). A Method for Finding Single-Nucleotide Polymorphisms with Allele Frequencies in Sequences of Deep Coverage. BMC Bioinformatics, 6:220.

Ye, L. and Huang, X.(2005). MAP2: Multiple Alignment of Syntenic Genomic Sequences. Nucleic Acids Research, 33:162-170.

Huang, X., Ye, L., Chou, H.-H., Yang, I-H. and Chao, K.-M.(2004). Efficient Combination of Multiple Word Models for Improved Sequence Comparison. Bioinformatics, 20:2529-2533.

Huang, X., Wang, J., Aluru, S., Yang, S.-P. and Hillier, L. (2003). PCAP: A Whole-Genome Assembly Program. Genome Research, 13:2164-2170.

Huang, X. and Chao, K.-M. (2003). A Generalized Global Alignment Algorithm. Bioinformatics, 19:228-233.

Huang, X, and Madan, A. (1999). CAP3: A DNA Sequence Assembly Program. Genome Research, 9:868-877.

Huang, X, Adams, M.D., Zhou, H., and Kerlavage, A.R. (1997). A Tool for Analyzing and Annotating Genomic Sequences. Genomics, 46:37-45.

Huang, X., and Zhang, J. (1996). Methods for Comparing a DNA Sequence with a Protein Sequence. Computer Applications in the Biosciences. 12:497-506.

Huang, X. and Miller, W. (1991) A Time-Efficient, Linear-Space Local Similarity Algorithm. Advances in Applied Mathematics, 12:337-357.

YAN-BIN JIA, Associate Professor of Computer Science

Ph.D. 1997, Robotics (and Computer Science), Carnegie Mellon University

Major Interests:

Robotics and artificial intelligence, geometric algorithms for curves and surfaces, tactile shape recognition and reconstruction, sensor implementation, localization and robot sensing, computational geometry and modeling, planning and control for robot dexterity, robot interfaces, optimization, nonlinear control and observation, kinematics and dynamics of manipulation.

Current Research:

Dr. Jia's main research objective is to develop algorithms that would enable arobot to effectively execute real world tasks that involve sensing, localizing, grasping, and manipulating of physical objects. His investigation into robot dexterity has focused on understanding computational and control issues in manipulation tasks. A major of this study is the development of algorithms for dynamic retrieval of geometric information such as shape and pose, and of mechanical information such as motion and force, and the effective use of such information in executing specific tasks.

Dr. Jia has been studying the localization, recognition, and reconstruction of curved shapes using minimum tactile information. This study is aimed at understanding the basic computational principles and limitations of touch sensing. From a more general perspective, he is interested in the design of geometric and control algorithms for dexterous manipulation and strategies for robot sensing that are efficient, reliable, and easily integrable into manipulation operations.

Details of Dr. Jia's research can be found at http://www.cs.iastate.edu/~jia

Representative Publications:

Ibrayev, R.and Jia, Y-B.(2006). Surface recognition by registering data curves from touch.Accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, PRC, Oct 9-15, 2006.

Jia, Y-B,Mi, L. and Tian, J. (2006). Surface patch reconstruction via curve sampling. In Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1371-1377, Orlando, FL, May 16-18, 2006.

Ibrayev, R.and Jia, Y-B.(2005). Semi-differential invariants for tactile recognition of algebraic curves. International Journal of Robotics Research, vol. 24, no. 11, pp. 951-969.

Jia, Y-B. (2005). Localization of curved parts through continual touch. IEEE Transactions on Robotics, vol. 21, no. 4, pp. 726-733.

Jia, Y-B. (2004). Computation on parametric curves with an application in grasping. International Journal of Robotics Research, vol. 23, no. 7-8, pp. 825-855.

Jia, Y-B. andMi, L. (2004). High precision contour tracking with a joystick sensor. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 804-809, Sendai, Japan, Sep 28 - Oct 2, 2004.

Jia, Y-B. (2003). Contact sensing for parts localization: sensor design and experiments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 516-522, Las Vegas, NV, Oct 27-31, 2003.

Jia, Y-B. and Erdmann, M. (1999).Pose and Motion from Contact. International Journal of Robotics Research, 18(5):466-490.

Jia, Y-B. and Erdmann, M. (1998).Local Observability of Rolling. In Robotics: The Algorithmic Perspective, P.K. Agarwal et al. (eds.), pp. 251-263, A. K. Peters, Boston.

Jia, Y-B. and Erdmann, M. (1996). Geometric Sensing of Known Planar Shapes. International Journal of Robotics Research, 15(4):365-392.

JACK H. LUTZ, Professor of Computer Science

Ph.D. 1987, Mathematics, California Institute of Technology

Major Interests:

Computational Complexity: structure of complexity classes, resource-bounded measure and dimension, and complexity in analysis. Algorithmic Information and Randomness: computational randomness, constructive dimension, Kolmogorov complexity, prediction, finite-state dimension, and algorithmic fractal geometry. Nanoscale Self-Assembly: information flow, universal computation, zeta-dimension, and randomness in self-assembling fractal structures.

Current Research:

Dr. Lutz is currently focused on computational complexity, algorithmic information theory, and nanoscale self-assembly. In all three of these areas, I collaborate extensively with graduate students, postdoctoral researchers, and faculty, both at ISU and elsewhere.In computational complexity he is investigating the structure of complexity classes (deterministic, non-deterministic, and probabilistic) using resource-bounded measure and dimension, complexity-theoretic generalizations of Lebesgue measure and fractal dimension that he has developed.

Current work focuses on derandomization, weak completeness, circuit-size complexity, strong hypotheses, and the type-2 foundations of resource-bounded measure and dimension.We are also investigating computability and complexity in geometric measure theory, which is the part of mathematical analysis that deals with the fine-scale properties of fractals, particle trajectories, and other dynamical processes in real Euclidean space.

In algorithmic information theory Dr. Lutz is studying randomness,Kolmogorov complexity, and constructive dimension, with applications in algorithmic prediction, geometric measure theory, and games. His research group isalso investigating finite-state dimension, especially in connection with Borel normal numbers and data compression. Their newest research area is nanoscale self-assembly.Here they are investigating the algorithmic self-assembly of fractal structures in the DNA tile assembly model of Seeman, Winfree, and Rothemund.They are especially interested in zeta-dimension, which is the appropriate measure of the fractal dimensions of such structures; bandwidth as a physical complexity measure; the apparent tradeoff between zeta-dimension and efficiency of computation in self-assembly; and randomized self-assembly.

Representative Publications:

Gu, X., Lutz, J. and Mayordomo, E. (2006). "Points on Computable Curves", Proceedings of the the Forty-Seventh Annual IEEE Symposium on Foundations of Computer Science (Berkeley, CA, October 22-24, 2006), IEEE Computer Society Press, pp. 469-474.

Doty, D., Lutz, J., and Nandakumar, S. (2006). "Finite-State Dimension and Real Arithmetic", Proceedings of the Thirty-Third InternationalColloquium on Automata, Languages, and Programming} (Venice, Italy, July 10-14, 2006), Springer-Verlag, pp. 537-547.

Gu, X.and Lutz, J."Dimension Characterizations of Complexity Classes", Computational Complexity, to appear.

Gu, X., Lutz, J. and Moser, P."Dimensions of Copeland-Erdos Sequences", Information and Computation, In press.

Athreya, K., Hitchcock,J., Lutz, J. and Mayordomo, E. "Effective Strong Dimension in Algorithmic Information and Computational Complexity", SIAM Journal on Computing, In press.

Hitchcock, J., Lutz, J. and Terwijn, S. (2007). "The Arithmetical Complexity of Dimension and Randomness", ACM Transactions on Computational Logic, In press.

Hitchcock, J. and Lutz, J. (2006). "Why Computational Complexity Requires Stricter Martingales", Theory of Computing Systems 39, pp. 277-296.

Fortnow, L.and Lutz, J. (2005). "Prediction and Dimension", Journal of Computer and System Sciences 70, pp. 570-589.

Hitchcock, J., Lutz, J. and Mayordomo, E. (2004). "Scaled Dimension andNonuniform Complexity", Journal of Computer and System Sciences 69, pp. 97-122.

Dai, J., Lathrop, J., Lutz, J.and Mayordomo, E. (2004). "Finite-State Dimension", Theoretical Computer Science 310, pp. 1-33.

Lutz, J. (2003)."The Dimensions of Individual Strings and Sequences", Information and Computation 187, pp. 49-79.

Lutz, J. (2003)."Dimension in Complexity Classes", SIAM Journal on Computing 32, pp. 1236-1259.

ROBYN LUTZ, Professor of Computer Science

Ph.D. 1980, Spanish, University of Kansas

M.S. 1990, Computer Science, Iowa State University

Major Interests:

Safety-critical product lines, requirements engineering, software safety, formal methods for specification and analysis, fault monitoring and recovery, software engineering.

Current Research:

Dr. Lutz's research is in software engineering, the part of computer science that studies how to develop better software systems. Her research contributions have been in two overlapping areas of software engineering:(1) how to build safe systems and (2) how to specify and analyze requirements for those systems.

Safety-critical software is software that can cause, or prevent, a hazardous situation or damage to life, property, or mission. Examples of safety-critical software include the software in medical imaging systems, in autonomous vehicles, and in spacecraft control systems. Dr. Lutz's research in the area of software safety has focused on safety-critical software product lines.A product line is a set of highly similar systems with a few key variations. Some examples of safety-critical software product lines that she has worked with are: flight-instrumentation displays (such as pilots use in the cockpit), interferometers (spaceborne, astronomical telescopes), medical-imaging systems (used by physicians to create computer images of patients' organs), and implantable medical devices (such as pacemakers and defibrillators).Dr. Lutz's key contributions in this area have been to extend software-safety techniques that were traditionally applied only to single systems to handle the new product lines of systems. The purpose of these extensions is to allow safety analyses to become reusable assets within a product line. By expanding software-safety techniques to product lines, and by formalizing the caveats on such reuse, Dr. Lutz and her research group hope to reduce the cost of doing safety analysis on product lines and speed up time-to-market of new products in the line. More importantly for safety-critical product lines, these techniques enhance the thoroughness and rigor of the safety analyses performed.

Dr. Lutz's work on requirements analysis for safety critical systems has focused on methods that enable developers to more accurately describe and rigorously analyze the requirements and design for safety-critical software. Historically, many failures of software have been due to an inadequate understanding of the software requirements by the developers. The specifications of what the software had to do were incomplete or inconsistent in some way that hindered safe, correct software from being built.One of Dr. Lutz's key contributions in this area has been to create formal models for the fault-protection software that detects and isolates system failures, and reconfigures the system to recover from them. More recently, she and collaborators at Jet Propulsion Laboratory and NASA Ames have developed a tool-supported, development approach for diagnosing and responding to contingencies (operational situations with mission risk) in a formal model of an unpiloted aerial vehicle.

A second key contribution has been the development by Dr. Lutz of Bi-Directional Safety Analysis, which systematically combines two distinct analysis methods to search for missing or incomplete requirements in software systems. Many of the results in this area have been applied to NASA spacecraft. With support from the NSF, Dr. Lutz and her students have extended this technique to product lines. She has also investigated a product-line approach to the development of distributed multi-agent systems (such as fleets of autonomous vehicles). Other collaborative applications have been to automatic construction of monitors for intrusion detection and to guiding user goal-refinement when a desired web-service composition fails.

A third key contribution has been to empirically confirm what was long-suspected:that misunderstood requirements and interfaces cause failures during testing and operations. Dr. Lutz and Carmen Mikulsi did this by identifying and describing the patterns of requirements omissions and requirements misunderstandings that escaped testing to manifest themselves during flight. These empirical investigations enabled the pinpointing of some specific, common weaknesses in the software requirements specification process. Dr. Lutz, in collaboration with her students, has developed solutions that target these specific areas. Supported by NSF, Dr. Lutz's research group and Dr. John Knight's research group at the University of Virginia have experimentally applied automated analysis results from software problem reports (generated during testing) to iteratively improve the linguistic domain modeling of requirements specifications.

Additional details of Dr. Lutz's research can be found at www.cs.iastate.edu/~rlutz

Representative Publications:

Lutz, R., Patterson-Hine, A., Nelson, S., Frost, C., Tal, D., and Harris, R."Using Obstacle Analysis to Identify Contingency Requirements on an Unpiloted Aerial Vehicle", Requirements Engineering Journal, to appear.

Lutz, R., Patterson-Hine, A., and Bajwa, A. "Tool-Supported Verification of Contingency Software Design in Evolving, Autonomous Systems", Proc. 7th IEEE International Symposium on Software Reliability Engineering (ISSRE 2006). Nov. 7-10, 2006, Raleigh, NC.

Dehlinger, J. and Lutz, R. (2006). "PLFaultCat:A Product-Line Software Fault Tree Analysis Tool", Automated Software Engineering, 13(1): pp. 169-193.

Dehlinger, J. and Lutz, R. (2005). "A Product-Line Approach to Promote Asset Reuse in Multi-Agent Systems", SELMAS 2005, LNCS vol. 3914, Ed. R. Choren.

Feng, Q. and Lutz, R.. "Bi-Directional Safety Analysis of Product Lines", Journal of Systems and Software, 78(2), Nov. 2005, pp. 111-127.

Padmanabhan, P. and Lutz, R.. "Tool-Supported Verification of Product Line Requirements, Automated Software Engineering, 12(4), Oct., 2005, pp. 447-485.

Schwanke, R. and Lutz, R.. "Experience with Architectural Design of a Modest Product Family," Software Practice and Experience, 34(13), Nov. 2004, pp. 1273-1296.

Lutz, R. and Mikulski, C. "Empirical Analysis of Safety-Critical Anomalies during Operations," IEEE Transactions on Software Engineering," vol. 30, no., 3, March, 2004, pp. 172-180.

Lutz,R. (2000). "Software Engineering for Safety:A Roadmap,'' in The Future of Software Engineering, 2000, ed. A. Finkelstein, ACM,Invited Chapter, pp. 213-224.

Easterbrook, S., Lutz, R., Covington, 'R., Kelly, J.,Ampo, Y. and Hamilton, D. "Experiences Using Lightweight Formal Methods for Requirements Modeling," IEEE Transactions on Software Engineering, Vol. 24, 1, Jan., 1998, pp. 4-14.

Lutz, R. and Wong, J. "Detecting Unsafe Error Recovery Schedules,'' IEEE Transactions on Software Engineering, Vol. 18, 8, Aug., 1992, pp. 749-760.

DIMITRIS MARGARITIS, Assistant Professor of Computer Science

Ph.D. 2003, Computer Science, Carnegie Mellon University

Major Interests:

Probabilistic modeling, methods for managing and reasoning with uncertainty/indeterminacy in Artificial Intelligence and other applications, Bayesian networks, Markov networks, data mining, machine learning, cross-disciplinary applications of these in bioinformatics, econometrics, very large databases and other domains.

Current Research:

Dr. Margaritis's current research is on algorithms for learning the structure of Bayesian networks and applying them to real-world application domains where modeling uncertainty might be beneficial.For example, it turns out that approximate query answering in very large databases can be done quickly using a sufficiently complex model of the database---in this case a Bayesian network. He would also like to explore capturing and modeling information about influences by statistical means in bioinformatics domains such as protein structure prediction. Dr. Margaritis's long-term vision is to develop algorithms and representations that help researchers gain insights into their research domains by automatically constructing probabilistic models of them from data.

Representative Publications:

Bromberg, F., Margaritis, D. and Honavar, V. (2006). Efficient Markov Network Structure Discovery from Independence Tests.SIAM International Conference on Data Mining (SDM).

Margaritis, D. (2005). Distribution-Free Learning of Bayesian Network Structure in Continuous Domains.National Conference on Artificial Intelligence (AAAI).

Yaramakala, S. and Margaritis, D. (2005). Speculative Markov Blanket Discovery for Optimal Feature Selection.IEEE International Conference on Data Mining (IEEE ICDM).

Cheng, H., Sen, T. Z. , Kloczkowski, A., Margaritis, D.and Jernigan, R. L.. Prediction of Protein Secondary Structure by Mining Fragments Database, Polymer.in press.

Margaritis, D. and Thrun, S. (2001). A Bayesian Multiresolution Independence Test for Continuous Variable. Uncertainty in Artificial Intelligence (UAI).

Margaritis, D., Faloutsos, C. and Thrun, S. (2001). NetCube: A Scalable Tool for Fast Data Mining and Compression. International Conference on Very Large Databases (VLDB).

Margaritis, D. and Thrun, S. (1999). Bayesian Network Induction via Local Neighborhoods. Neural Information Processing Systems (NIPS).

Moghaddam, D., Biermann, H. and Margaritis, D. (2001). Regions-of-Interest and Spatial Layout for Content-Based Image Retrieval. Journal of Multimedia Tools and Applications, Kluwer Academic Publishers, Vol. 14, No. 3, 2001.

Margaritis, D. and Thrun, S. (1998). Learning to Locate and Object in 3D Space from a Sequence of Camera Images. International Conference in Machine Learning (ICML).

Waldherr, S., Thrun, S.,Romero, R. and Margaritis, D. (1998). Template-Based Recognition of Pose and Motion Gestures on a Mobile Robot. National Conference on Artificial Intelligence (AAAI).

LES MILLER, Professor of Computer Science

Ph.D. 1980, Computer Science, Southern Methodist University

Research Areas

Bioinformatics and Computational Biology, e-Government, Human Computer Interaction, Data Warehouses, Information Integration and Information Retrieval, Database Systems, Information Security, Software Systems

Research Statement

Dr. Miller's current research is focused on interface design issues for handheld computers used in spatial data collection within Bureau of Census's decennial census. Other current work in human computer interaction aims to enhance the use of the Internet by the elderly. His workin computational biology is focused onthe development of graph models for storing and analyzing biological networks.His on knowledge Management looks at the development of distributed knowledge management systems that use business process-based topic maps as knowledge hubs. Work on organizational memory is focused on the capture of organizational semantics and the integration of memory artifacts into the organization's knowledge base.

Selected Publications

Miller, L., Nilakanta, S., Song, Y., Zhu, L. and Hua, M. (2007). Knowledge Management: Using Topic Maps in Organizational Memory. Hawaiian International Conference on System Sciences.pp. 204b-

Miller, L., Ding, J. and Nusser, S. (2007). An Accuracy Model for Relational Databases. Accepted to the ISCA 22nd International Conference on Computers and Their Applications. In press.

Nilakanta, S., Miller, L., Song, Y. and Zhu, L. (2007). Supporting Topic Maps in an Organizational Memory Web Server. Information Resources Management Association International Conference.In press.

Helmer, G., Wong, J., Slagell, M., Honavar, V., Miller, L., Wang, Y., Wang, X. and Stakhanova, N. (2007). Software Fault Tree and Colored Petri Net Based Specification, Design and Implementation of Agent-Based Intrusion Detection Systems. To appear in the International Journal of Information and Computer Security.

Wang, Y., Behera, S., Wong, J., Helmer, G., Honavar, V., Miller, L., and Lutz, R. (2006). Towards the automatic generation of mobile agents for distributed intrusion detection systems. Journal of Systems and Software79 (1): 1-14.

Nilakanta, S., Miller, L. and Zhu, D. (2006). Organization Memory Management: Technological and Research Issues. Journal of Database Management. Vol. 17 No. 1 pp. 85-94.

Tsai, H-J., Miller, L., Ming, H. and Nusser, S. (2006). Combining Spatial Data from Multiple Data Sources. ISCA 19th International Conference on Computer Applications in Industry and Engineering. Pages 89-94.

Zhang, J.,Cook, D. and Miller, L. (2005). Limn Matrix: A Tool for Visualizing Large, Distributed, High-Dimension Data Sets. 17th International Conference of Computer Applications in Industry and Engineering. Honolulu, HI. pp. 263-268

Miller, L., Zou, B., Hua, M. and Nusser, S. (2004). Supporting a Virtual Office Environment for Federal Agency Field Operations. The Third International Conference on Politics and Information Systems: Technologies and Applications (PISTA '04). Orlando, FL. Pages 84-88.

ANDREW S. MINER, Associate Professor of Computer Science

Ph.D. 2000, Computer Science, College of William and Mary

Major Interests:

Performance and reliability analysis of systems;model checking and formal methods;binary decision diagrams (and variants);analysis algorithms; Petri nets and stochastic modeling; tool development.

Current Research:

Dr. Miner's primary research goal is to develop techniques for analyzingcomplex discrete-event systems (including computer communication networks, multi-threaded software, network protocols, and others). He has worked in the areas of model checking and performance evaluation, and is particularly interested in techniques that are applicable to both of these areas. Dr. Miner also works on developing software tools that incorporate these techniques. See http://www.cs.iastate.edu/~asminer for details.

Model checking is the process of determining if a system, described as a (usually nondeterministic) model, satisfies some properties. These properties are expressed using an appropriate logic, such as the temporal logics LTL or CTL. If the system model describes a finite state machine, then the model checking problem is decidable. In practice, however, the finite state machines can be extremely large. Researchers use a variety of techniques to overcome this; Dr. Miner has focused past efforts on techniques that utilize a data structure called decision diagrams. He has made several contributions in this area developing improved reachability set generation algorithms (a core problem in model checking) for decision diagrams, especially for models of asynchronous systems. Dr. Miner is currently working with Dr. Basu to apply these techniques to multi-threaded software.

If a system is described using a stochastic model, rather than a nondeterministic model, then the model ultimately generates a stochasticprocess. Properties of the system can then either be queried (e.g., "What is the probability that a packet is lost") or expressed using a stochastic logic such as pCTL or CSL (e.g., "The probability of a lost packet is less that 10^-6"). In either case, these properties are determined by analyzing the underlying stochastic process, which is typically a Markov chain. Dr. Miner has contributed to this area by adapting decision diagram algorithms used for model checking for use with Markov chains.

Dr. Miner is a co-designer and developer of the software tool SMART (Stochastic Model-checking Analyzer for Reliability and Timing), which contains implementations of traditional as well as state-of-the-art techniques in model checking and performance evaluation. The tool was designed for instructional, research, and industrial use.

Representative Publications:

Miner, A. Saturation for a general class of models. IEEE Transactions on Software Engineering. In press.

Ciardo, G., Jones, R.,Miner, A. and Siminiceanu, R. (2006). Logical and stochastic modeling with SMART. Performance Evaluation, 63 (6): 578-608.

Miner, A. and Cheng, S. (2004). Improving efficiency of implicit Markov chain state classification. In Proceedings of QEST 2004, pp 262-271.

Miner, A.and Parker, D. (2004). Symbolic representations and analysis of large state spaces. In "Validation of Stochastic Systems" (Springer Verlag, 2004),LNCS 2925, pp 296-338.

Miner, A (2004). Implicit GSPN reachability set generation using decision diagrams. Performance Evaluation, 56 (1-4): 145-165.

Ciardo, G.,Forno, M., Grieco, P. and Miner, A. (2003). Comparing implicit representations of large CTMCs. In Proceedings of NSMC 2003, pp 323-327.

Miner, A., Ciardo, G. and Donatelli, S. (2000).Using the exact state space of a large structured Markov model to compute approximate stationary measures.In Proceedings of ACM SIGMETRICS 2000, pp 207-216.

Ciardo, G. and Miner, A. (1999).A data structure for the efficient Kronecker solution of GSPNs. In Proceedings of PNPM'99, pp 22-3.

Miner, A. and Ciardo, G. (1999). Efficient reachability set generation and storage using decision diagrams. In Proceedings of ICATPN 1999 (LNCS 1639), pp 6-25.

GURPUR M. PRABHU, Associate Professor of Computer Science

Ph.D., Computer Science, 1983, Washington State University

Major Interests:

Parallel processing, information integration and information retrieval, intelligent computing, and other interesting scientific problems.

Current Research:

Dr. Prabhu's work on generating random numbers for parallel computers has led to an efficient parallel random number generator (O (1) parallel time per generation of one number on each processor).

Dr. Prabhu's work, in collaboration with former Ph.D. student Srinivas Aluru, focused on analysis of the fastest known algorithm, namely, Greengard's algorithm, for the N-body problem - the problem of simulating the motion of N particles under the influence of mutual force fields based on an inverse square law. This problem has applications in several domains including radiosity methods in computer graphics, astrophysics, molecular dynamics, fluid dynamics, and numerical complex analysis. Greengard, whose Ph.D. thesis won the ACM distinguished dissertation award in 1987, claimed to compute the pairwise interactions in O(N) time per iteration. Dr. Prabhu and Aluru's analysis of Greengard's algorithm showed that the Greengard's algorithm is not O(N) as claimed.

Dr. Prabhu's work with two doctoral students, Naresh Nayar and Milton Wikstrom focused on the applications of high-performance techniques in the areas of weather modeling and computational chemistry. Another student that Dr. Prabhu mentored, Diane Rover, worked at the Scalable Computing Laboratory and her work on visualization of program performance and the SLALOM benchmark have received national research awards. A recent doctoral student, Rajat Todi, used the HINT benchmark to evaluate file access patterns for realistic I/O workloads in cluster environments. Along with doctoral student Babak Forouraghi, Dr. Prabhu studied the design of learning algorithms for flaw classification of materials used in nondestructive evaluation, drawing on approaches from multiobjective optimization, fuzzy logic, machine learning, and multivariate statistics.

Dr. Prabhu's current interests center around semantic-based search techniques for information retrieval and processing. Dr. Prabhu is also exploring some long-standing open problems in the area of inertial frames and paradoxes in special relativity.

Representative Publications:

Prabhu, G., Aluru, S. and Gustafson, J. (1992). "A random number generator for parallel computers,"Parallel Computing, 18, pp. 839-847.

Prabhu, G., Forouraghi, B. and Schmerr, L. (1994). "Fuzzy multiobjective optimization with multivariate regression trees," Third International Conference on Fuzzy Systems, pp. 1400-1405.

Prabhu, G., Forouraghi, B. and Schmerr, L.(1994). "Induction of multivariate regression trees for design optimization," Twelfth AAAI National Conference on Artificial Intelligence, pp. 607-612.

Prabhu, G., Aluru, S., Gustafson, J.and Sevilgen, F. (1998). "Distribution-independent hierarchical algorithms for the N-body problem," Journal of Supercomputing, 12, pp. 303-323.

Prabhu, G., Wang, H., Takle, E., and Shen, J. M. (2000). "Performance evaluation of climate simulation on a cluster of networked workstations," International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 2007-2013, CSREA Press.

Prabhu, G. and Balakrishnan, P. (2003). "Programmable access to distributed data: Design of Semantic Bridge," 2^nd IASTED International Conference on Communications, Internet, and Information Technology, Scottsdale, Arizona, pp. 589-594.

Prabhu, G. and Iyer, C. (2006). "Reversal in time order of interactive events: collision of inclined rods," Eur. J. Phys. 27,pp. 819-824.

Prabhu, G. and Iyer, C.(2006). "Differing perceptions on the landing of the rod into the slot," (with Chandru Iyer), accepted in Am. J. Phys., 74 (11),pp. 998-1001.

Prabhu, G. and Iyer, C. (2007). "Lorentz transformations with arbitrary line of motion," (with Chandru Iyer), accepted in Eur. J. Phys. 28 (2), pp. 183-190, To appear.

HRIDESH RAJAN, Assistant Professor of Computer Science

Ph.D. 2005, Computer Science, University of Virginia

Major Interests:

Software engineering, modularity in software design, science of design, component integration, service-oriented architectures, programming language design and implementation, aspect-oriented languages, specification languages, static and dynamic analysis, testing and verification, program comprehension

Current Research:

Dr. Rajan's research on programming language design and implementation has focused on the design and implementation of aspect-oriented programming (AOP) languages. AOP has shown the potential to improve the ability of software architects to devise effective modularizations for complex systems, leading to savings in cost, schedule, and quality. In the Nu project (See http://www.cs.iastate.edu/~nu) Dr. Rajan and his students Robert Dyer and Harish Narayanappa are exploring intermediate language designs and corresponding virtual machine extensions to bring design modularity at the object code level for aspect-oriented languages. The potential impacts of this project include better compatibility with the existing tool chain, better run-time performance, cross AO language compatibility, improved pointcut expressivity, and efficient run-time weaving support. In collaboration with William G. Griswold from University of California, San Diego and Kevin J. Sullivan from University of Virginia, Dr. Rajan developed the notion of crosscut programming interfaces (XPI) to decouple aspects that use aspect-oriented advising from the details of advised code. XPI's provide a better encapsulation for changeable implementation details. The abstract design concerns that were hidden earlier in the program structures and syntactic pointcut elements were revealed in a principled way. In collaboration with Kevin J. Sullian, Dr. Rajan's Eos project (See http://www.cs.iastate.edu/~eos) has advanced aspect-oriented languages by developing the notion of general-purpose aspect instantiation and selective advising for aspects, and a significantly simplifying unification of aspect and object-oriented programming. These advances led to improved modularity of component integration concerns.

Dr. Rajan's work on specification and verification, in collaboration with Ph.D. student Youssef Hanna and his colleague Wensheng Zhang, is focused onspecifying and verifying cryptographic protocols for sensor networks in his Slede project (See http://www.cs.iastate.edu/~slede). Applications of sensor networks are numerous from military to environmental research. By providing mechanisms to find cryptographic errors in the security protocols for sensor networks this research program is improving the reliability of these networks, making a direct impact on all areas where these networks are utilized.

Program Comprehension

Dr. Rajan's work on program comprehension, In collaboration with his Ph.D. Student Juri Memmert, is focused on approaches and tools for automatic or semi-automatic generation of concern models from source code in his Osiris project (See http://www.cs.iastate.edu/~design/osiris/). Concern models are abstract representation of the system that eases better comprehension of large software systems designed to help enhance the comprehensibility of large software systems.

Representative Publications:

Rajan, H., Dyer, R., Hanna, Y. andNarayanappa, H.(2006). Preserving Separation of Concerns through Compilation.Software Engineering Properties of Languages and Aspect Technologies (SPLAT 06), A workshop affiliated with AOSD 2006, Bonn, Germany.

Rajan, H. and Sullivan, K. (2005). Classpects: Unifying Aspect- and Object-Oriented Language Design. In: Proceedings of the 27th International Conference on Software Engineering (ICSE 2005), 15-21 May 2005, St. Louis, Missouri, USA.

Rajan R. and Sullivan, K.(2003). Eos: Instance-Level Aspects for Integrated System Design.In: Proceedings of the 2003 Joint European Software Engineering Conference and ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2003), Helsinki, Finland.

Rajan, H. and Sullivan, K.(2005). Aspect Language Features for Concern Coverage Profiling. In: Proceedings of the Fourth International Conference on Aspect-Oriented Software Development (AOSD 2005), 14-18 March, 2005, Chicago, IL, USA.

Griswold, W., Sullivan, K., Song, Y., Shonle, M., Tewari, N., Cai, Y. and Rajan, H. (2006). Modular Software Design with Crosscutting Interfaces. IEEE Software, Special Issue on Aspect-Oriented Programming, Jan/Feb 2006.

Sullivan, K., Griswold, W., Song, Y., Shonle, M., Tewari, N., Cai, Y. and Rajan, H. (2005). Information Hiding Interfaces for Aspect-Oriented Design.In: Proceedings of the Joint 10th European Software Engineering Conference and 13th ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2005), 5-9 Sept 2005, Lisbon, Portugal.

LU RUAN, Assistant Professor of Computer Science

Ph.D. 2001, Computer Science, University of Minnesota - Twin Cities

Major Interests:

Computer Networks, Optical Networks.

Current Research:

IP over WDM is being envisioned as the architecture for the next generation Internet. In this architecture, high-speed IP routers are interconnected by intelligent optical core networks. Survivability in these networks is essential since the networks carry a high volume of traffic and a single link/node failure will cause tremendous service loss. Dr. Ruan's current research focuses on the design of fast and capacity efficient protection/restoration schemes in both the IP layer and the WDM layer to recover from link/node failures. In addition, she is interested in developing techniques to integrate the recovery schemes in the two layers seamlessly and efficiently.

Representative Publications:

Ruan, L. and F. Tang, F. (2006). Survivable IP Network Realization in IP-over-WDM Networks under Overlay Model. Computer Communications, vol. 29, no 10, pages 1772-1779, June 2006.

Liu, C. and Ruan, L. (2006).p-Cycle Design in Survivable WDM Networks with Shared Risk Link Groups (SRLGs). Photonic Network Communications, vol. 11, no. 3, pages 301-311, May 2006.

Ruan, L., Luo, H., and Liu, C. (2004) A Dynamic Routing Algorithm with Load Balancing Heuristics for Restorable Connections in WDM Networks. IEEE Journal on Selected Areas in Communications, vol.22, no. 9, pages 1823-1829, November 2004.

Liu, C. and Ruan, L. (2006). Dynamic Provisioning of Survivable Services Using Path-Segment Protecting p-Cycles in WDM Networks. In: Proceedings of Int'l Conf. on Computer Communications and Networks, Arlington, Virginia, USA, October 2006. (Best paper candidate.)

Liu, C. and Ruan, L. (2005). Logical Topology Augmentation for Survivable Mapping in IP-over-WDM Networks. In:Proceedings of IEEE Globecom, volume 4, pages 1885 - 1889, St. Louis, MO, USA, November/December 2005.

Liu, Z. and Ruan, L. (2005). Reducing Restoration Blocking in WDM Optical Networks. In: Proceedings of ICCCN 2005, pages 323-330, San Diego, CA, October 2005.

Ruan, L. and Tang, F. (2005). Dynamic Establishment of Restorable Connections using p-Cycle Protection in WDM Networks. In: Proceedings of Broadnets 2005, pages 147-154, Boston, MA, October 2005.

Ruan, L. and Liu, Z. (2005). Upstream Node Initiated Fast Restoration in MPLS Networks. In: Proceedings of IEEE ICC 2005, pages 959-964, Seoul Korea, May 2005.

Liu, C. and Ruan, L. (2004). Finding Good Candidate Cycles for Efficient p-Cycle Network Design. In: Proceedings of the Thirteenth International Conference on Computer Communications and Networks (ICCCN), pages 321-326, Chicago, IL, Oct. 2004.

Ruan, L. and Du, D-Z. (eds.), Optical Networks-Recent Advances, Kluwer Academic Publishers, 2001.

GIORA SLUTZKI, Professor of Computer Science

Ph.D. 1977, Computer Science, Tel-Aviv University, Tel-Aviv, Israel

Major Interests:

Algorithms and Complexity, Game Theory, Logic, Automata Theory.

Current Research:

Dr. Slutzki is currently working on complexity of some problems in abstract algebra and graph theory. He also works on bioinformatics algorithms, ranking of alternatives,problems in pursuit-evasion in polygons, and algorithmic problems in game theory.

Representative Publications:

Volij, O and Slutzki, G. (2006). Scoring of Web Pages and Tournaments - Axiomatizations. Journal of Social Choice and Welfare, Vol. 26, No. 1, pp.75-92.

Volij, O and Slutzki, G. (2005).Ranking Participants in Generalized Tournaments. International Journal of Game Theory, 33(2)(2005), pp. 255-270.

Slutzki, G., Simov, B. and LaValle, S.(2002). A Complete Pursuit-Evasion Algorithm for Two Pursuers Using Beam Detection. In the proceedings of IEEE International Conference on Robotics and Automation (ICRA '02), May 2002, Washington, DC, USA.

Slutzki, G. and Bergman, C. (2002). Computational Complexity of Some Problems Involving Congruences on Algebras. Theoretical Computer Science, 270, pp. 591-608.

Slutzki, G., Fernandez-Baca, D. and Seppalainen, T.(2002). Lower Bounds for Parametric Sequence Comparison" .Special Issue of Discrete Applied Mathematics (DAM), 118(3), pp. 181-199. Extended Abstract appeared in SPIRE '99.

Slutzki, G. and Bergman, C. Computational Complexity of Generators and Nongenerators in Algebra. To appear in International Journal of Algebra and Computation.

Slutzki, G. Mobasher, B., Pigozzi, D. and Vautsadakis, G. (2000).A Duality Theory for Bilattices. Algebra Universalis, 43, pp. 109-125.

Slutzki, G., Simov, B. and LaValle, S.(2000). An Algorithm for Searching a Polygonal Region with a Flashlight.. 16th ACM Symposium on Computational Geometry (SoCG '00), June 2000, Hong Kong. Proceedings, pp. 260-269. To appear in a Special Issue of the International Journal of Computational Geometry and Applications (IJCGA).

Slutzki, G., Fernandez-Baca, D. and Seppalainen, T.(2000).Parametric multiple sequence alignment and phylogeny construction. 11th Annual Symposium on Combinatorial Pattern Matching (CPM '00), June 2000, Montreal, Canada. Springer Verlag, LNCS 1848, pp. 69-83. To appear in Journal of Discrete Algorithms.

Slutzki, G. and Bergman, C. (2000). Complexity of Some Problems Concerning Varieties and Quasi-Varieties of Algebras. SIAM Journal on Computing, 30 (2), pp. 359-382. Extended Abstract appeared in 16th STACS (1999).

Slutzki, G., Juedes, D. and Bergman, C. (1999).Computational Complexity of Term-Equivalence. International Journal of Algebra and Computation 9 (1), pp. 113-128.

GUANG SONG, Assistant Professor of Computer Science

Ph.D. 2003,Computer Science, Texas A&M University

Major Interests:

Bioinformatics and Computational Biology, Robotics, Computational Geometry, Algorithms

Current Research:

Dr. Song's research interests spread over several disciplines: physics, robotics, computer science, and biology. One goal of his research is to gain a deeper understanding of life and its mechanisms, which orchestrate various components to function together, at different levels. His current research focuses on understanding the mechanisms of protein functions, for example, how does the structure of a protein facilitate the realization of its functions? How does the binding a ligand open up an ion channel at a remote site? How does a protein fold? His work thus involves physically and structurally based modelings and simulations.His research interests also include motion planning, robotics, quantum computing, virtual reality, and haptic input.

Representative Publications:

Lei Yang, Guang Song, Alicia Carriquiry and Robert L. Jernigan. Close Correspondence between the Essential Protein Motions from Principal Component analysis of Multiple HIV-1 Protease Structures and Elastic Network Modes. Structure, Cell Press. Vol. 16. No. 2. pp. 321-330, 2008.

Guang Song and Robert L. Jernigan. vGNM: a Better Model for Understanding the Dynamics of Proteins in Crystals. Journal of Molecular Biology. Vol. 369. No.3. pp. 880-93, 2007.

Lei Yang, Guang Song, and Robert L. Jernigan. How Well Can We Understand Large-Scale Protein Motions Using Normal Modes of Elastic Network Models?. Biophysical Journal. Vol. 93. No. 3. pp. 920-9, 2007.

Song, G. and Jernigan, R. (2006). An Enhanced Elastic Network Model to Represent the Motions of Domain-Swapped Proteins. Proteins. Vol. 63. No. 1. pp. 197-209.

Thomas, S., Song. G., and Amato, N. (2005). Protein folding by motion planning. Physical Biology. Vol. 2. No. 4. pp. S148-55.

Song, G.and Klappenecker, A. (2004). Optimal Realizations of Simplified Toffoli Gates. Journal of Quantum Information and Computation. Vol. 4. No. 5. pp. 361-372.

Song. G. and Amato, N.(2004). A Motion Planning Approach to Folding: From Paper Craft to Protein Folding. IEEE Transactions on Robotics and Automation. Vol. 20. No. 1. pp. 60-71.

Amato, N., Dill, K. and Song, G.(2003). Using Motion Planning to Map Protein Folding Landscapes and Analyze Folding Kinetics of Known Native Structures. Journal of Computational Biology. Vol. 10. No. 3-4. pp. 239-256.

Song, G.and Klappenecker, A. (2003). Optimal Realizations of Controlled Unitary Gates. Journal of Quantum Information and Computation. Vol. 3. No. 2. pp. 139-155.

Song. G. and Amato, N.(2001). Using Motion Planning to Study Protein Folding Pathways. the 5th ACM International Conference on Computational Molecular Biology (RECOMB), Montreal, Canada. pp. 287-296.

Bayazit, O. B., Song. G. and Amato, N.(2001). Enhancing Randomized Motion Planners: Exploring with Haptic Hints. Autonomous Robots. Vol. 10. No. 2. pp. 163-174.

WALLAPAK TAVANAPONG, Associate Professor of Computer Science

Ph.D. 1999, Computer Science, University of Central Florida

Major Interests:

Content analysis for medical images and videos, multimedia databases, distributed caching systems for multimedia data, multimedia communications and operating systems, peer-to-peer systems, quality of service support, mobile data management.

Current Research:

Dr. Tavanapong investigates database management systems for videos captured from medical procedures, especially from endoscopic procedures such as colonoscopy, upper endoscopy, just to name a few. Such a system will be useful for improving endoscopic research, training, and education and has a great potential to improve patient care as millions of these procedures are performed yearly in the US alone. The key challenges from computer science perspectives are 1) how to design an algorithm that effectively and efficiently recognizes important contents such as different types of abnormality and instruments used in medical procedures, 2) how to represent and organize these contents such that they are quickly accessible via a keyword query or a query by image or video example, and 3) how to present the contents to users in a user-friendly manner. Several software packages have also been developed and used in a hospital. This research is a joint work with Professor Johnny Wong at Iowa State University, Professor Piet de Groen at Mayo Clinic Rochester, and Professor JungHwan Oh at the University of North Texas.

Dr. Tavanapong's research in multimedia and communications has investigated distributed caching systems for multimedia data, a bi-directional collaborative framework for a content distribution network and a peer-to-peer network to benefit from one another, a peer-to-peer caching technique, a periodic broadcast scheme that offers low response time and is capable of serving a large number of requests, a generic periodic broadcast server capable of implementing many of the existing periodic broadcast schemes in the literature with few changes in coding, Quality of Service (QoS) core-based routing using a set of core nodes to deliver multimedia data from multiple senders to multiple receivers, taking the desired QoS into account, and techniques to detect malicious peer nodes in peer-to-peer streaming. Dr. Tavanapong has published a number of conference and journal publications on these works. Some of these research works have been partially funded by the National Science Foundation.

Representative Publications:

Tran, M. and Tavanapong, W.On the Design, Analysis, and Implementation of a Generalized Periodic Broadcast Server. To appear in IEEE Transactions on Broadcasting.

Sheu, S., Tavanapong, W. and Hua K. A. (2006). A Scalable Cost-effective Video Broadcasting System for On-demand Video Services. Multimedia Tools and Applications, 28(3): 321-345.

Shetty, S., Galdames, P.,Tavanapong, W. and Cai Y.(2006). Detecting Malicious Peers in Overlay Multicast Streaming. To appear in Proc. of IEEE Conf. on Local Area Networks (LCN), Florida.

Cao, Y., Liu, D., Tavanapong, W., Wong, J., Oh, J. and de Groen P. C. (2006). Automatic Classification of Images with Appendiceal Orifice in Colonoscopy Videos. Proc. of IEEE Engineering in Medicine and Biology Conference. New York City, New York.

Tran M. and Tavanapong, W. (2005). Peer-assisted Content Distribution Networks. Proc. of IEEE Conf. on Local Computer Networks (LCN), pages 123-131, Sydney, Australia.

Hwang, S.,Oh, J., Lee, J.,de Groen, P. C., Cao, Y., Tavanapong, W., Liu, D.and. Wong. J.(2005). Automatic Measurement of Quality Metrics for Colonoscopy Videos. Proc. of ACM Multimedia 2005, pages 912-921, Singapore.

Y. Cao, D. Li, W. Tavanapong, J. Wong, J. Oh, and P. C. de Groen. Parsing and Browsing Tools for Colonoscopy Videos. Proc. of ACM Multimedia 2004, pages 844-851, New York, NY, USA.

Tavanapong, W. and Zhou, J. (2004). Shot Clustering Techniques for Story Browsing. IEEE Transactions on Multimedia, 6(4): 517-527.

Cao, Y., Li, D., Tavanapong, W., Wong, J., Oh, J. and de Groen, P. C. (2004). A Visual Model Approach for Parsing Colonoscopy Videos. Proc. of Int'l Conf. on Image and Video Retrieval (LNCS 3115), pages 160-169, Dublin, Ireland.

JIN TIAN, Assistant Professor of Computer Science

Ph.D. 2002, Computer Science, University of California, Los Angeles

Major Interests:

Artificial Intelligence: Bayesian networks, probabilistic reasoning, causal reasoning and learning.

Current Research:

The long term goal of Dr. Tian's research is to provide theoretical foundations that will facilitate building intelligent systems that can learn about and reason with causes and effects. Dr. Tian's recent research is focused on causal reasoning and learning in the framework of causal Bayesian networks. Some topics that Dr. Tian has been working on include: learning causal structures from data, inferring causal effects from a combination of data and qualitative causal assumptions, identifying linear causal relationships in structural equation models.

Representative Publications:

Kang, C. and Tian, J. (2006). Inequality Constraints in Causal Models with Hidden Variables. Conference on Uncertainty in Artificial Intelligence (UAI), Cambridge, Massachusetts, AUAI Press. pp. 233-240.

Tian, J., Kang, C., and Pearl, J. (2006). A Characterization of Interventional Distributions in Semi-Markovian Causal Models. Proceedings of the National Conference on Artificial Intelligence (AAAI), Boston, Massachusetts. AAAI Press, pp. 1239-1244.

Tian, J. (2005). Identifying Direct Causal Effects in Linear Models. the National Conference on Artificial Intelligence (AAAI), Pittsburgh, Pennsylvania, AAAI Press. pp. 346-352.

Tian, J. (2004). Identifying Linear Causal Effects. Proceedings of the National Conference on Artificial Intelligence (AAAI), San Jose, California, AAAI Press. pp. 104-110, 2004.

Tian, J. (2004). Identifying Conditional Causal Effects. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Banff, Canada. AUAI Press. pp. 561-568.

Tian, J. and Pearl, J.(2002). A general identification condition for causal effects, in Proceedings of the National Conference on Artificial Intelligence (AAAI).

Tian, J. and Pearl, J.(2002)., On the Testable Implications of Causal Models with Hidden Variables, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

Tian, J. and Pearl, J.(2001)., Causal Discovery from Changes, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

Tian, J. and Pearl, J.(2000)., Probabilities of causation: Bounds and identification, in Annals of Mathematics and Artificial Intelligence 28: 287-313.

Tian, J. (2000). A branch-and-bound algorithm for MDL learning Bayesian networks, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

JOHNNY S. K. WONG, Professor of Computer Science

Ph.D. 1987, Computer Science, The University of Sydney, NSW, Australia

Research Interests:

Operating Systems, Distributed Systems, Communication Networks, Multimedia and Medical Information Systems, Intelligent Multi-Agents Systems, Data Mining, Intrusion Detection and Response, Wireless Ad-hoc Networks and Peer-to-Peer Systems.

Current Research:

Dr. Wong's current research interests include design and implementation issues in operating systems, distributed systems, multimedia communications and medical information systems. Current activities focus on systems and network security, intrusion detections and response using intelligent mobile agents, endoscopic medical information systems (EMIS).

Representative Publications:

Cai, Y., Natarajan, A.and Wong, J. (2007). On Scheduling of Peer-to-Peer Video Services, IEEE Journal on Selected Areas in Communications, Vol. 25 (1), pp. 140-145.

Zhang, M., Wong, J., Tavanapong, W., Oh, J. and de Groen. P.C. (2006). Design and Analysis of a Media Uploading System. Journal of Multimedia Tools and Applications, Accepted.

Stakhnova, N., Basu, S., Lutz, R. and Wong, J. (2006).Automatic caching of behavioral patterns for efficient run-time monitoring. The 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC'06), Indianapolis, USA, IEEE. pp. 333-340.

Hwang, S., Oh, J., Lee, J., Tavanapong, W., de Groen, P.C. and Wong J.Informative Frame Classification for Endoscopy Video Medical Image Analysis. Medical Image Analysis Journal, In press.

Yang, C., Zhou, J., Zhang, W. and Wong, J. (2006). Pairwise Key Establishment for Large-Scale Sensor Networks: from Identifier-based to Location-Based. Infoscale 2006: First International Conference on Scalable Information Systems, Hong Kong, ACM. pp. 1-4.

Stakhanova, N., Basu, S. and Wong, J. (2006). A Taxonomy of Intrusion Response Systems. The International Journal of Information and Computer Security, Inderscience, Accepted.

Babbitt, R., Wong, J., Chang, C. and Mitra, S. (2006). Privacy Management In Smart Homes: Design And Analysis. International Conference on Aging, Disability and Independence, ST. Petersburg, Florida. pp. 255.

Putthividhya, W., Tavanapong W. and Wong J. (2006). Core-based Routing with QoS Support for Distributed Interactive Multimedia Applications. International Journal of Computer Science and Network Security. Vol. 6. No. 1B. pp. 47-57.

Wang, Y., Behera, S., Wong, J., Helmer, G., Honavar, V., Miller, L., Slagell, M. and Lutz, R.(2006). Towards the automatic generation of mobile agents for distributed intrusion detection systems. Journal of Systems & Software, Elsevier. Vol. 79. No. 1. pp. 1-14.

Babbitt, R., Lu, D., Chang. C. and Wong, J. (2005). Requirements Engineering for Smart Homes to Support Successful Aging, Disability and Independence. Annals of The European Academy of Sciences, EAS Publishing House. pp. 107-127.

WENSHENG ZHANG, Assistant Professor of Computer Science

Ph.D. 2005, Computer Science, The Pennsylvania State University

Major Interests:

Computer networks, network security, and applied cryptography

Current Research:

Dr. Zhang's current research is to explore the technologies and theories for designing more efficient and secure wireless networks (with a focus on wireless sensor networks). To purse this goal, he is working in three aspects: (1) designing new or improving existing protocols for wireless networks to enable more applications with higher efficiency and reliability; (ii) investigating new cryptographic and non-cryptographic methodologies, and applying them in designing more efficient strategies and protocols to protect wireless networks as well as the applications lying on top of them; (3) identifying the limitations of existing theories and techniques in specifying and verifying wireless network protocols, and developing exploring new approaches.

Representative Publications:

Zhang, W., Cao, G. and La Porta, T. Dynamic Proxy Tree-Based Data dissemination Scheme for Wireless Sensor Networks.ACM/Springer Wireless Networks, to appear.

Zhang, W., Song, H., Zhu, S. and Cao, G. (2005). Least Privilege and Privilege Deprivation: Towards Tolerating Mobile Sink Compromises in Wireless Sensor Networks, ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC).

Zhang, W. and Cao, G. (2005).Defend Against Cache Consistency Attacks in Wireless Ad Hoc Networks, Annual International Conference on Mobile and Ubiquitous Systems: Networks and Services (Mobiquitous).

Zhang, W. and Cao, G. (2005).Group Rekeying for Filtering False Data in Sensor Networks: A Predistribution and Local Collaboration-Based Approach,IEEE Conference on Communications (INFOCOM).

Wang, G., Cao, G., La Porta, T. and Zhang, W. (2005). Sensor Relocation in Mobile Sensor Networks, IEEE Conference on Communications (INFOCOM).

Zhang, W. and Cao, G. (2004). DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks,IEEE Transactions on Wireless Communication, Vol. 3, No. 5, pp. 1689-1701.