Panos Chrysanthis


Office: 6421 Sennott Square
Mailbox: 353

Dr. Chrysanthis received the BS degree (Physics with concentration in Computer Science, 1982) from the University of Athens, Greece. He earned the MS and PhD degrees (Computer and Information Sciences, 1986 and 1991) from the University of Massachusetts at Amherst.

He joined the Department of Computer Science at the University of Pittsburgh in 1991. He is also a Faculty Member of the RODS Laboratory at Pitt's Center for Biomedical Informatics and an Adjunct Associate Professor of Computer Science Department at Carnegie-Mellon University. He has held visiting positions at the University of Massachusetts, at the University of Rome, Italy and at the Carnegie Mellon University.

His research interests lie within the areas of mobile and pervasive data management, database systems, operating systems and real-time systems. In particular, his research focuses on advanced data and information management technologies.

In 1995, he was a recipient of the U.S. National Science Foundation CAREER Award for his investigation on the management of data for mobile and wireless computing. He is an editor of the VLDB Journal and has served as guest editor for a number of journals and on program committees of several Database and Distributed Computing Conferences. He is a member of the ACM (Sigmod, Sigmobile, Sigops), the IEEE Computer Society, and the USENIX Association.

Five Most Recent Publications

A. Zheng, A. Labrinidis, and P. K. Chrysanthis, "Planar: Parallel Lightweight Architecture-Aware Adaptive Graph Repartitioning," 32nd IEEE International Conference on Data Engineering , pp. 121-132, Helsinki, Finland, 2016.

H. Ehsan, M. A. Sharaf, and P. K. Chrysanthis, "MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration," 32nd IEEE International Conference on Data Engineering, pp. 1-12, Helsinki, Finland, 2016.

A. Zheng, A. Labrinidis, P. H. Pisciuneri, P. K. Chrysanthis, and P. Givi, "PARAGON: Parallel Architecture-Aware Graph Partition Refinement Algorithm," 19th International Conference on Extending Database Technology, Bordeaux, France, March 2016.

A. U. Shein, P. K. Chrysanthis, and A. Labrinidis, "F1: Accelerating the Optimization of Aggregate Continuous Queries," 24th ACM International Conference on Information and Knowledge Management, pp. 1151-1160, Melbourne, Australia, October 2015.

A. Konstantinidis, G. Nikolaides, G. Chatzimilioudis, G. Evagorou, D. Zeinalipour-Yazti and P. Chrysanthis, " Radiomap Prefetching for Indoor Navigation in Intermittently Connected Wi-Fi Networks," 16th IEEE International Conference on Mobile Data Management, pp. 34-43, Pittsburgh, PA, June 2015.

Research Interests

  • Big data
  • Database management
  • Data stream management
  • Workflow management
  • User-centric optimizations
  • Power-aware data processing
  • Mobile and pervasive management
  • Distributed and cooperating systems

Research Projects


The growing onslaught of astronomical data available presents a great challenge. Astronomy lacks an easy-to-use and scalable way to collect and distribute expert information about objects from datasets of tens of thousands to billions of individual events and objects. Over the next decade, the amount of information available to the typical astronomer will grow by two orders of magnitude both in raw data size and in the number of objects. This project pursues three research directions, each of which has the potential to transform how astronomers interface with large datasets: (1) a scalable annotation framework to enable linking of observations to specific experiments, models, or other observations; (2) a continuous workflow enactment system that would support automated reasoning in the presence of uncertainty; and (3) a computational framework for interactively analyzing astronomical datasets, allowing the construction and testing of hypotheses. Project plans include the design and development of a prototype system (AstroShelf) and its evaluation using two timely science programs: (a) Using multi-wavelength data from the DEEP3 and AEGIS surveys, develop methods to incorporate images and catalogs from disparate datasets, allowing us to study how the demographics of galaxies have changed over the last 8 billion years. (b) Using properties of time-variable events found by the Pan-STARRS survey, develop techniques for rapid classification of transient phenomena, dissemination of their properties, and incorporation of feedback from follow-up observations. AstroShelf will include a publicly accessible, flexible annotation system for public datasets, which will also lend itself to outreach efforts involving annotations by the general public. This project's significant impact is the ability to share information and expert opinions quickly and widely, about each new observation or event, fundamentally changing our ability to learn about the Universe; such functionality can also be applied in support of other scientific domains. Results of this research will be made publicly available at