Luca, Pegah and Shiyuan Received Campus Wide Research Awards

Congratulations Luca Foschini, Pegah Kamousi, and Shiyuan Wang on receiving three coveted Central Continuing Student Fellowship! Luca received the Dean’s Fellowship, while both Pegah and Shiyuan received Graduate Division Dissertation Fellowships. These are very competitive campus wide awards.

Luca Foschini’s research is mainly focused on efficient algorithms for computing statistics on streams of data, with applications to networking. During his Ph.D., Luca also worked on the theoretical characterization of the complexity of shortest path computation in time-dependent networks, graph partitioning problems, and geometric algorithms to dynamically maintain the volume of a set of hyper-boxes. Before joining the PhD program he worked on intrusion detection for high speed networks, string algorithms with applications to data compression, and graph clustering.

Pegah Kamous investigates the complexity of solving and approximating some classical computational geometry and graph-theoretic problems over a basic model of uncertainty in the input data. A number of classical algorithms, which are fundamental both in theory and application, are re-visited in this framework. Most of these problems are well studied and have efficient algorithms in the deterministic model. Surprisingly, some of them turn out to admit efficient algorithms even in the stochastic model, while some become computationally hard. For the latter case, Pegah is working on efficient approximation algorithms.

Shiyuan Wang studies research problems related to security and privacy of data in the cloud. In most cases and especially with Platform-as-a-Service (PaaS) and software-as-a-Service (SaaS), users cannot control and audit their own data stored in the cloud by themselves. She is doing research in the following aspects: 1. Protect individual users’ query privacy in the cloud by enabling privately querying on public data. 2. Protect privacy of user location and path information in location based services. Without adequate privacy protection, these systems can be easily misused, e.g., to track users or target them for home invasion. 3. Secure relational data access and management in the cloud that satisfies data confidentiality, data reliability and query efficiency.