Brownian motion and anomalous diffusion revisited via a fractional Langevin equation

Learning (from) Networks: Fundamental Limits, Algorithms, and Applications Read more about SEAS EE: Soheil Feizi

Analyzing Tobacco Use and Family History Documentation in the Electronic Health Record Read more about BrownCSSem: Elizabeth S. Chen

Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity Read more about BUStatPrSem: Veronika Rockova

Spatial and Adaptive Models for Neuroimaging Data

Cosmology with Weak Lensing: Bayesian Hierarchical Modelling with 100,000 Parameters Read more about IACS Sem: Alan Heavens

Network Inference from High Dimensional Data

ePCA: Exponential family PCA

Many applications, such as photon-limited imaging and genomics, involve large datasets with entries from exponential family distributions. It is of interest to estimate the covariance structure and principal components of the noiseless distribution. Principal Component Analysis (PCA), the standard method for this setting, can be inefficient for non-Gaussian noise. In this talk we present ePCA, a methodology for PCA on exponential family distributions. ePCA involves the eigendecomposition of a new covariance matrix estimator, constructed in a...

Read more about Seminar: Edgar Dobriban

Hilbert Transform and Options Valuation

Causality and Statistical Learning

Data-mining for development

Data Centers, Energy, and Online Optimization

(cancelled)

Statistics application in industry: financial institutions and tech companies