Pascal Van Hentenryck, Phd, is the Seth Bonder Collegiate Professor of Industrial and Operations Engineering, Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.
His research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. He applies evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.
The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.
Laura Balzano, PhD, is Assistant Professor in the Electrical Engineering and Computer Science department at the University of Michigan, Ann Arbor.
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.
Real-time dynamic background tracking and foreground separation. At time t = 101, the virtual camera slightly pans to right 20 pixels. We show how GRASTA quickly adapts to the new subspace by t = 125. The first row is the original video frame; the middle row is the tracked background; the bottom row is the separated foreground.
Jerome P. Lynch, PhD, is Professor and Donald Malloure Department Chair of the Civil and Environmental Engineering Department in the College of Engineering in the University of Michigan, Ann Arbor.
Prof. Lynch’s group works at the forefront of deploying large-scale sensor networks to the built environment for monitoring and control of civil infrastructure systems including bridges, roads, rail networks, and pipelines; this research portfolio falls within the broader class of cyber-physical systems (CPS). To maximize the benefit of the massive data sets, they collect from operational infrastructure systems, and undertake research in the area of relational and NoSQL database systems, cloud-based analytics, and data visualization technologies. In addition, their algorithmic work is focused on the use of statistical signal processing, pattern classification, machine learning, and model inversion/updating techniques to automate the interrogation sensor data collected. The ultimate aim of Prof. Lynch’s work is to harness the full potential of data science to provide system users with real-time, actionable information obtained from the raw sensor data collected.
A permanent wireless monitoring system was installed in 2011 on the New Carquinez Suspension Bridge (Vallejo, CA). The system continuously collects data pertaining to the bridge environment and the behavior of the bridge to load; our data science research is instrumental in unlocking the value of structural monitoring data through data-driven interrogation.