Probability and Statistics Seminar
Abstract: Stochastic queueing networks have a variety of industrial applications including services, data and telephony networks, manufacturing and more recently business processes.
We will begin with some motivating examples of business workflow processes and the underlying performance analysis issues. Then we will continue by introducing stochastic single class and multiclass queueing networks. The principal question is whether the probability distribution of the queue lengths has exponentially fast decaying tails in steady-state. We establish that for single class queueing networks this is indeed the case. Moreover, we establish that the stationary distribution of the associated reflected diffusion process provides a valid heavy-traffic approximation of the underlying queueing network in steady-state, thus resolving an open problem.
For multiclass networks, the picture is rather different. We present an example of a network where the queue length distribution is subexponential, even though the interarrival and service times have light tails and some other natural conditions are satisfied. Thus, the slow decay of the tails can be a purely network effect.
Joint work with Sean Meyn and Assaf Zeevi.
Brown University -
Division of Biology and Medicine, Center for Statistical Sciences
Spring 2005 Lecture Series
United Kingdom Department of Epidemiology, Mathematics & Statistics, Wolfson Institute of Preventive Medicine, Queen Mary University of London | |
Abstract: In this presentation, I summarize the major roles of biomarkers in cancer research and treatment. Examples are shown of different kinds of markers from serology, pathology and radiology. I show how they are used in practice in therapy and diagnosis, notably in cancers of the breast and prostate. Considerations for evaluation of such markers as surrogate endpoints in randomized trials are discussed.
**Sponsored by the Charles P. Sisson II Memorial Lectureship
***Co Sponsored by The Marshall Woods Lectureships Foundation of
Fine Arts
Brown University --
Joint Materials/Solid Mechanics Seminar Series
Department of Materials Science and Engineering, Division of Biological Engineering Harvard-MIT Division of Health Sciences and Technology | |
Abstract: We explore coupling among mechanics, biology and medicine at the cell and subcellular levels by investigating: the molecular changes induced by invasion of parasites or from exposure to chemicals occurring naturally in the human body, the consequent changes in the mechanical response of the cell, and possible implications for disease progression. The two cases considered are: human red blood cells invaded by the maleria parasite Plasmodium falciparum and Panc-1 pancreatic cancer cells. In the former case, it is shown by recourse to large deformation optical tweezers stretching at the picoNewton force level that parasitization leads to significant stiffening of the red blood cell. Possible consequences for biological and physiological responses are probed. These in vitro studies are also accompanied by in vivo experiments performed using a mouse model. Experiments specifically designed to explore the contributions to cell mechanical response from specific proteins transferred from the parasite to the cell cytoskeleton are also undertaken using studies of cloned parasites with protein knock-outs. The presentation will conclude by demonstrating very different chemomechanical pathways associated with the mechanical response of the Panc-1 cell and their implications for tumor metastasis.
Center for Fluid Mechanics Seminar
Department of Biological Statistics and Computational Biology | |
Candidate for Assistant Professor (tenure track) in the |
Abstract: High-throughput biotechnologies, such as microarray and mass spectrometry, simultaneously monitor the activities of thousands of genes at the RNA and protein level. Statistically, we are challenged by efficiently estimating high-dimensional parameters with noisy data. Furthermore, the signals in these large-scale analyses in genomics and proteomics are sparse and asymmetric. Here we propose a generalized shrinkage estimator based on empirical Bayesian thresholding, which is adaptive to the sparseness and possible asymmetry of the signals. The properties of this estimator have been investigated. Simulation study and application to microarray data demonstrate the performance of our approach.
Identifying polygenic effects on complex traits and profiling molecular features for clinical outcomes post another challenging statistical issue as selecting variables with large p small n data. Likewise, the sparseness and possible asymmetry of the signals are the most important characteristics of the large p small n data, which should be exploited because of the limited sample size and/or the biological implication. We develop a Bayesian model selection approach to incorporate this a priori information. A heat-map is proposed to help researchers make informed decisions and control false discovery rate. This approach has been successfully applied in revealing sex-specific QTL underlying differences in glucose-6-phosphate dehydrogenase enzyme activity between two Drosophila species.
Probability and Statistics Seminar
Abstract: We observe n points in the unit d-dimensional hypercube. We want to know whether these points are uniformly distributed or whether a small fraction of them are actually concentrated near an object, such as a curve or sheet, which is only known to belong to some regularity class.
We argue that this hypothesis testing problem is relevant for the task of detecting structures in galaxy distributions.
We consider classes of Holder immersions and study the asymptotic power of the Generalized Likelihood Ratio Test (GLRT), or Scan Statistic, in this setting.
We also address computational issues. It turns out that some exact calculations are feasible in some situations, via Dynamic Programming.
However, in general, exact computations are known to be NP-hard. Approximations are nevertheless possible, at least in theory. Via custom-built graphical structures, it is possible to translate this computational task into some variation of "The Traveling Salesman Problem", famous in Comuter Science and Operations Research.
We extend this study to higher order contact, which models recent experiments in Perceptual Psychophysics.
Collaborators: David Donoho (Stanford), Xiaoming Huo (Georgia Tech) and Craig Tovey (Georgia Tech).
Brown Analysis Seminar
Scientific Computing Seminar
Jeffrey Yepez, Air Force Research Laboratory |
Probability and Statistics Seminar
Abstract: Statistical ensemble simulations play an important role in predicting the behavior of chaotic/noise-driven processes in nature. It is therefore imperative to understand and quantify useful information in a forecast ensemble. Modern methods of estimating predictive skill in an ensemble are often based on its mean state and variance, thus not taking into account the shape of its distribution. Introduced is a novel information theory-based predictability approach via relative entropy which captures extra information in higher order statistical moments of a forecast ensemble.
Department of Mathematics Colloquium
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