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    Edo Airoldi "Estimating Latent Processes on a Graph From Indirect Measurements"

    Structured measurements and populations/samples with interfering units are ubiquitous in science and have become a focal point for discussion in the past few years. Formal statistical models for the analysis of this type of data have emerged as a major topic of interest in diverse areas of study. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online social networking websites such as Facebook and LinkedIn, and a host of more specialized professional networking communities has intensified interest in the study of graphs, structured measurements and interference. In this talk, I will review a few ideas and open areas of research that are central to this burgeoning literature, placing emphasis on the statistical and data analysis perspectives. I will then focus on the the problem of making inference on latent processes on a graph, with an application to the estimating point-to-point traffic volumes in a communication network from indirect measurements. Inference in this setting requires solving a sequence of ill-posed inverse problems, y(t)= A x(t). We develop a multilevel state-space model for mixing times series and an efficient approach to inference; a simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. Our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series.


    Bio: Edoardo Airoldi is an assistant professor in the department of statistics at Harvard University. In December 2006, he received a Ph.D. from Carnegie Mellon, working on statistical machine learning and the analysis of complex systems with Stephen Fienberg and Kathleen Carley. His dissertation introduced statistical and computational elements of graph theory that support data analysis of complex systems and their evolution. Until December 2008, he was a postdoctoral fellow in the Lewis-Sigler Institute for Integrative Genomics of Princeton University working with Olga Troyanskaya, David Botstein, and James Broach where he developed mechanistic models to gain computational insights into aspects of the molecular and cellular biology that are not directly observable with experimental probes. Since that time, he has been working closely with biologists and in the areas of cellular differentiation, cellular development and cancer.

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