9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

October 20–23, 2013 | Austin, Texas, United States

Day one - October 21st, 2013

Lorenzo Alvisi (University of Texas at Austin)

Reasoning With MAD Distributed Systems


Decentralized approaches spanning multiple administrative domains (MAD) are an increasingly effective way to deploy services. Popular examples include peer-to-peer (p2p) applications, content distribution networks, and mesh routing protocols. Cooperation lies at the heart of these services. Yet, when working together is crucial, a natural question is: "What if users stop cooperating?" After all, users in a MAD system are vulnerable not only to the failure of some of the equipment in the system, or to the possibility that some users may behave maliciously, but also to the possibility that users may selfishly refrain from sharing their resources as the protocol would require.

In this setting, it is hard to put a bound on the number of components in the systems that deviate from their correct specification. is it still possible under such circumstances to build systems that not only provide provable guarantees in terms of their safety and liveness properties but also yield practical performance?


Lorenzo Alvisi is a Professor in the Department of Computer Sciences at the University of Texas at Austin and a Visiting Chair Professor at Shanghai Jiao Tong University. Lorenzo holds a Ph.D. (1996) and M.S. (1994) in Computer Science from Cornell University, and a Laurea summa cum laude in Physics from the University of Bologna, Italy. His research interests are in dependable distributed computing. He is a Fellow of the ACM and the recipient of a Humboldt Research Award, an Alfred P. Sloan Fellowship, and an NSF CAREER Award, as well as of several teaching awards. He serves on the editorial boards of the ACM Transactions on Computer Systems (TOCS), ACM Computing Surveys, and Springer's Distributed Computing. In addition to distributed systems, Lorenzo is passionate about classical music and red Italian motorcycles.

Day 2 - October 22nd, 2013

Vasant Hanovar (Pennsylvania State University)

From Data Analytics to Computational Discovery and Discovery Informatics


Much recent attention has focused on the opportunities and challenges offered by the emergence of “big data” – data that are far more voluminous, diverse, and inter-related than we know how to cope with. However, data, big or small, although necessary ingredients for scientific discovery, are not sufficient. The increased ability to acquire and process data offers unprecedented opportunities for a shift in focus of informatics to assist simply the recording, organizing, and routine analysis of observations to using them to steer experiments, construct, test, and validate predictive models, harmonize theories and explanations at different levels of abstraction, and across different disciplines. Developing information processing accounts of, and effective algorithms for discovery is a long-standing challenge in artificial intelligence and cognitive science. Information processing systems provide powerful abstractions for understanding complex systems ranging from cells to societies on the one hand and a powerful exploratory apparatus for science, on the other. Eliciting causal relations and predictive models from observations and experiments, which is central to scientific discovery, is also a hallmark of intelligence. In this talk, I will highlight, drawing on post-genomic biology as a representative example, some research challenges that must be addressed in order to realize the full promise of computational discovery; and the opportunities for foundational advances in artificial Intelligence and cognitive Systems that a serious effort in that direction entail.


Dr. Vasant Honavar received his Ph.D. in Computer Science and Cognitive Science in 1990 from the University of Wisconsin Madison, specializing in Artificial Intelligence. From 1990 to 2013, he served on the faculty of Computer Science and of Bioinformatics and Computational Biology at Iowa State University (ISU). At ISU, he directed the Artificial Intelligence Research Laboratory (which he founded in 1990) and the Center for Computational Intelligence, Learning & Discovery (which he founded in 2005) and served as the associate chair (2001-2003) and chair (2003-2005) of the ISU Bioinformatics and Computational Biology Graduate Program, which he helped establish in 1999 with support from an Integrative Graduate Education and Research Training (IGERT) award.

Honavar served as a program director in the Information and Intelligent Systems Division of the Computer and Information Sciences and Engineering directorate of the National Science Foundation (NSF) during 2010-2013 while maintaining his research program at ISU. He led the Big Data Science and Engineering Program, established the NSF-OFR collaboration in Computational and Information Processing Approaches to and Infrastructure in support of, Financial Research and Analysis and Management, contributed to Smart and Connected Health, Information Integration and Informatics, Expeditions in Computing, Science of Learning Centers, Integrative Graduate Education and Research Training, Computing Research Infrastructure Programs.

In September 2013, Honavar joined the faculty of Penn State University where he will serve as the Frymoyer Chair Professor of Information Science and Technology and lead new research and educational initiatives in Data Sciences and contribute to initiatives in Life Sciences. Honavar's current research and teaching interests include Artificial Intelligence, Machine Learning, Bioinformatics, Big Data Analytics, Computational Molecular Biology, Data Mining, Discovery Informatics Information Integration, Knowledge Representation and Inference, Semantic Technologies, Social Informatics, Security Informatics, and Health Informatics. Honavar has served on, or currently serves on the editorial boards of several journals and program committees of several major research conferences in these areas.

Honavar has led research projects funded by NSF, NIH, and USDA that have resulted in foundational research contributions (documented in over 250 peer-reviewed publications) in algorithms for constructing predictive models from sequence, image, text, multi-relational, graph-structured data; Scalable algorithms for building predictive models from large, distributed, semantically disparate data (big data); Knowledge base and service federation; Representing and reasoning about preferences; and applications in bioinformatics, computational biology, immunoinformatics, energy informatics, health informatics and social informatics.

Honavar received the Iowa Board of Regents Award for Faculty Excellence in 2007, the Iowa State University College of Liberal Arts and Sciences Award for Research Excellence in 2008, and the Iowa State University Margaret Ellen White Graduate Faculty Award in 2011. Honavar received the NSF Director’s Award for Superior Accomplishment in 2013 for his leadership of the NSF Big Data Program. However, he considers the 28 Ph.D. students that he has mentored and trained during his academic career his proudest accomplishments.

Day 3 - October 23rd, 2013

Murat Kantarcioglu (University of Texas at Dallas)

Incentive-compatible Assured Information Sharing


In many cases, lack of proper incentives and an easy to use secure infrastructure may hinder assured information sharing. In this talk, we discuss how game theoretic ideas can be used to incentivize information sharing under strict security guarantees. We explore the guarantees that we can make about the truthfulness and the quality of the shared data when we have no way of verifying individual actions due to security constraints. We discuss our previous work that tackle this problem by using a monetary mechanism to encourage players to be truthful about their data without being able to verify the truthfulness of the data that players provide. Finally, we present experimental results that show how humans share data under different incentive structures.


Murat Kantarcioglu, Ph.D. is an Associate Professor in the Computer Science Department and Director of the Data Security and Privacy Lab at the University of Texas at Dallas. He is also a visiting scholar at the Data Privacy Lab at Harvard University.

Dr. Kantarcioglu's research focuses on creating technologies that can efficiently extract useful information from any data without sacrificing privacy or security. He has published over 100 papers in peer reviewed journals and conferences. His research has been supported by grants from NSF, AFOSR, ONR, NSA, and NIH and has received two best paper awards. He is a recipient of the NSF CAREER award and his research has been reported on in the media, including the Boston Globe and ABC News. He holds a B.S. in Computer Engineering from Middle East Technical University, and M.S. and Ph.D degrees in Computer Science from Purdue University. He is a senior member of IEEE and ACM.