Systems | Information | Learning | Optimization
 

location: Orchard View Room

Speeding up Permutation Testing in Neuroimaging and An Asynchronous Parallel Stochastic Coordinate Descent Algorithm

Speaker 1: Chris Hinrichs Abstract: Multiple hypothesis testing is a significant problem in nearly all neuroimaging studies. In order to correct for this phenomena, we require a reliable estimate of the Family-Wise Error Rate (FWER). The well known Bonferroni correction method, while being simple to implement, is quite conservative, and …

The Science of Mind Reading and Beamspace MIMO Transceivers for Low-Complexity and Near-Optimal Communication at mm-wave Frequencies and TBD

Prof. Rob Nowak Mind Reading involves a subtle interplay of statistics and physical modeling, and often the latter is not considered as critically as it should be. For example, parapsychological experiments have demonstrated mind reading effects that are statistically significant, but physically implausible. But we don’t have to look at …

Dynamic optimization of fractionation schedules in radiation therapy and On Finding the Largest Mean Among Many

Jagdish Ramakrishnan In radiation therapy, the fractionation schedule, i.e. the total number of treatment days and the dose delivered per day, plays an important role in treatment outcome. In the first part of the talk, we analyze the effect of tumor repopulation on the optimal fractionation scheme. We find that …

Split Cuts for Two-Stage Stochastic Integer Programs and Tracking Influence in Dynamic Social Networks

Merve Bodur Stochastic programming is a way of dealing with uncertainty in the optimization problems. We consider two-stage stochastic programs with integer first stage and continuous second stage. It means that the decision maker must take some integer decisions before the uncertainty is revealed, then can observe the realizations and …

Gobble Gobble: Random Graph Models for Large Empirical Networks and Blind Source Separation Techniques for Multiply Labeled Fluorescence Images

Sarah Rich Sometimes in life things are complicated, and we just wish they were simpler! (Am I right, ladies?) A standard approach of theoreticians is to just pretend that they *are* simpler and keep going! We’ll consider this approach in the context of models for large empirical networks, like social …

Estimation of local dependence graphs via Hawkes processes and link with functional connectivity in neurosciences

Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select coefficients, we propose an adaptive $\ell_1$-penalization methodology, where …

Criticality and information flow in an adaptive system and Semi-algebraic geometry of common lines

David Dynerman Cryo-electron microscopy (cryo-EM) is a technique for discovering the 3D structures of small molecules. To perform this 3D reconstruction a large number of 2D images taken from unknown microscope positions must be correctly positioned back in 3D space. Although these microscope positions are unknown, the common lines of …

How much is lost in pairwise correlations: the case of phylogenetics

Among the many popular techniques for reconstructing evolutionary trees from molecular sequences, so-called distance-matrix methods are typically the fastest. This speed stems from a straightforward, intuitive approach: repeated merging of the closest clusters of sequences. However, unlike more elaborate techniques such as maximum likelihood, distance-matrix methods only exploit empirical correlations …