My research spans security, privacy, and optimization of data collection particularly as applied to the Smart Grid, an augmented and enhanced paradigm for the conventional power grid. I am particularly interested in optimization approaches that take a notion of security and/or privacy into the modeling explicitly. At the intersection of the Intelligent Transportation Systems, Smart Grid, and Smart Cities, I am interested in data privacy and energy usage in smart parking lots. Protection of data and availability, especially under assault through a Denial-of-Service attacks, represents another dimension of my area of research interests. I am working on developing data privacy-aware bidding applications for the Smart Grid Demand Response systems without relying on trusted third parties. Finally, I am interested in educational and pedagogical research about teaching computer science, Smart Grid, cyber security, and data privacy.
Professor Balzano and her students investigate problems in statistical signal processing and optimization, particularly dealing with large and messy data. Her applications typically have missing, corrupted, and uncalibrated data as well as heterogeneous data in terms of sensors, sensor quality, and scale in both time and space. Her theoretical interests involve classes of non-convex problems that include Principal Components Analysis (or the Singular Value Decomposition) and many interesting variants such as PCA with sparse or structured principal components, orthogonality and non-negativity constraints, and even categorical data or human preference data. She concentrates on fast gradient methods and related optimization methods that are scalable to real-time operation and massive data. Her work provides algorithmic and statistical guarantees for these algorithms on the aforementioned non-convex problems, and she focuses carefully on assumptions that are realistic for the relevant applications. She has worked in the areas of online algorithms, real-time computer vision, compressed sensing and matrix completion, network inference, and sensor networks.
Stilian Stoev’s research is in the area of applied probability and statistics for stochastic processes with emphasis on extremes, heavy tails, self-similarity, and long-range dependence. His recent theoretical contributions are in the area of max-stable processes, which is the class of processes emerging as a canonical model for the dependence in the extremes. This includes the representation, characterization, ergodicity, mixing, and prediction for this class of processes. Dr. Stoev is also working on applied problems in the area of computer network traffic monitoring, analysis and modeling. A recent joint project focuses on developing efficient statistical methods and algorithms for the visualization and analysis of fast multi-gigabit network traffic streams, which can help unveil the structure of traffic flows, detect anomalies and cyber attacks in real-time. This involves advanced low-level packet capture, efficient computation and rapid communication of summary statistics using non-relational data bases. More broadly, Dr. Stoev’s research is motivated by large-scale and data intensive applied problems arising in the areas of:
- environmental, weather and climate extremes.
- insurance and finance.
- Internet traffic monitoring, modeling and prediction.