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Thursday, February 1, 2018Seminar 217, Risk Management: Interpretable proximate factors for large dimensionsSeminar: Risk Seminar  February 1  12:302 p.m.  1011 Evans Hall Speaker: Markus Pelger, Stanford Center for Risk Management Research This papers deals with the approximation of latent statistical factors with sparse and easytointerpret proximate factors. Latent factors in a largedimensional factor model can be estimated by principal component analysis, but are usually hard to interpret. By shrinking the factor weights, we obtain proximate factors that are easier to interpret. We show that proximate factors consisting of... More > Wednesday, February 7, 2018Large deviations for two timescale jumpdiffusions and Markov chain modelsSeminar: Probability Seminar  February 7  3:104 p.m.  1011 Evans Hall Lea Popovic, Concordia University For a number of processes in biology the appropriate stochastic modelling is done in Applied Statistics at TeslaSeminar: Neyman Seminar  February 7  45 p.m.  1011 Evans Hall Swupnil Sahai, Tesla; Andrej Karpathy, Tesla From estimating the time to failure of battery modules for Reliability Engineering to predicting lane lines from images for Autopilot, statistics plays a vital role in building all of Tesla’s products. In this talk, we present the ways in which Tesla is changing the future of sustainable energy and discuss how statisticians will help us get there. Wednesday, February 14, 2018Scaling limits for percolated random planar mapsSeminar: Probability Seminar  February 14  3:104 p.m.  1011 Evans Hall Nina Holden, Concordia University The SchrammLoewner evolution (SLE) is a family of random fractal curves, which is the proven or conjectured scaling limit of a variety of twodimensional lattice models in statistical mechanics. Liouville quantum gravity (LQG) is a model for a random surface which is the proven or conjectured scaling limit of discrete surfaces known as random planar maps (RPM). We prove scaling limit results for... More > Thursday, February 15, 2018Seminar 217, Risk Management: Digitallydriven change in the insurance industry—disruption or transformation?Seminar: Risk Seminar  February 15  12:302 p.m.  1011 Evans Hall Speaker: Jeffrey Bohn, Swiss Re Center for Risk Management Research As technology continues to insinuate itself into all facets of financial services, the insurance industry faces a slowmotion parade of promise, possibilities, prematurity, and pareddown expectations. Digitization, the birth of InsurTech, machine intelligence, and the collection & curation of (orders of magnitude) more structured & unstructured data are changing (and will continue to change) the... More > Testing for twostage experiments in the presence of interferenceSeminar: Neyman Seminar  February 15  45 p.m.  1011 Evans Hall Guillaume Basse, Harvard University Many important causal questions concern interactions between units, also known as interference. Examples include interactions between individuals in households, students in schools, and firms in markets. Standard analyses that ignore interference can often break down in this setting: estimators can be badly biased, while classical randomization tests can be invalid. In this talk, I present recent... More > Wednesday, February 21, 2018Lowtemperature localization of directed polymersSeminar: Probability Seminar  February 21  3:104 p.m.  1011 Evans Hall Erik Bates, Stanford University On the ddimensional integer lattice, directed polymers are paths of a random walk that have been reweighted according to a random environment that refreshes at each time step. The qualitative behavior of the system is governed by a temperature parameter; if this parameter is small, the environment has little effect, meaning all possible paths are close to equally likely. If the parameter is made... More > Lowtemperature localization of directed polymersSeminar: Probability Seminar  February 21  3:104 p.m.  1011 Evans Hall Erik Bates, Stanford University On the ddimensional integer lattice, directed polymers are paths of a random walk that have been reweighted according to a random environment that refreshes at each time step. The qualitative behavior of the system is governed by a temperature parameter; if this parameter is small, the environment has little effect, meaning all possible paths are close to equally likely. If the parameter is made... More > Weina Wang Delay Bounds And Asymptotics In Cloud Computing SystemsSeminar: Other Related Seminars  February 21  3:305 p.m.  3110 Etcheverry Hall Weina Wang, Illinois UrbanaCampaign Industrial Engineering & Operations Research With the emergence of bigdata technologies, cloud computing systems are growing rapidly in size and becoming more and more complex, making it costly to conduct experiments and simulations. Therefore, modeling computing systems and characterizing their performance analytically are more critical than ever in identifying bottlenecks, informing system design, and facilitating provisioning. Recent Advances in Algorithmic HighDimensional Robust StatisticsSeminar: Neyman Seminar  February 21  45 p.m.  1011 Evans Hall Ilias Diakonikolas, USC Fitting a model to a collection of observations is one of the quintessential problems in machine learning. Since any model is only approximately valid, an estimator that is useful in practice must also be robust in the presence of model misspecification. It turns out that there is a striking tension between robustness and computational efficiency. Even for the most basic highdimensional tasks,... More > Thursday, February 22, 2018Seminar 217, Risk Management: Solving the “curse of dimensionality” problem in multiassetclass risk modelsSeminar: Risk Seminar  February 22  12:302 p.m.  1011 Evans Hall Speaker: Jose Menchero, Bloomberg Center for Risk Management Research Estimating a robust risk model risk for a portfolio that spans multiple asset classes is a challenging task due to the “curse of dimensionality” (i.e., the problem of estimating too many relationships from too few observations). While the sample covariance matrix is easily computed, it is susceptible to capturing spurious relationships that make it unsuitable for portfolio construction purposes.... More > Dr. Aaron McKenna, Department of Genome Sciences, University of Washington: Resolving whole organism cell fate with CRISPR/Cas9Seminar: Other Related Seminars  February 22  45 p.m.  Soda Hall, HP Auditorium 306 Center for Computational Biology, Electrical Engineering and Computer Sciences (EECS) Abstract: Multicellular organisms develop by way of a lineage tree, a series of cell divisions that give rise to cell types, tissues, and organs. However, our knowledge of the cell lineage and its determinants remains extremely fragmentary for nearly all species. This includes all vertebrates and arthropods such as Drosophila, wherein cell lineage varies between individuals; embryos and organs. Monday, February 26, 2018Dr. Mingfu Shao, Department of Computational Biology, Carnegie Mellon University: Efficient algorithms for largescale transcriptomics and genomicsSeminar: Other Related Seminars  February 26  45 p.m.  Soda Hall, HP Auditorium 306 Center for Computational Biology, Electrical Engineering and Computer Sciences (EECS) Title: GraphXD Seminar: Vector Representations of Graphs and the Maximum Cut ProblemSeminar: Other Related Seminars  February 26  45:30 p.m.  1011 Evans Hall David P. Williamson, Operations Research and Information Engineering, Cornell University Berkeley Institute for Data Science In this talk, I will look at a classical problem from graph theory of finding a large cut in a graph. We’ll start with a 1967 result of Erdős that showed that picking a random partition of the graph finds a cut that is at least half the largest possible cut. We’ll then describe a result due to Goemans and myself from 1995 that shows that by representing the graph as a set of vectors, one per... More > Wednesday, February 28, 2018Markovian Solutions to Scalar Conservation LawSeminar: Probability Seminar  February 28  3:104 p.m.  1011 Evans Hall Fraydoun Rezakhanlou, U C Berkeley According to a classical result of Bertoin (1998), if the initial data for Burgers equation is a Levy Process with no positive jump, then the same is true at later times and there is an explicit equation for the evolution of the associated Levy measures. In 2010, Menon and Srinivasan published a conjecture for the statistical structure of solutions to scalar conservation laws with certain Markov... More > Algorithmic Regularization in Overparameterized Matrix Recovery and Neural Networks with Quadratic ActivationsSeminar: Neyman Seminar  February 28  45 p.m.  1011 Evans Hall Tengyu Ma, Facebook AI Research Overparameterized models are widely and successfully used in deep learning, but their workings are far from understood. In many practical scenarios, the learned model generalizes to the test data, even though the hypothesis class contains a model that completely overfits the training data and no regularization is applied. 

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