This set of lectures is an introduction to the stochastic analysis of Markov jump processes with applications in biology. The goal is of using stochastic calculus in this context, the analogue of Itô’s calculus for Brownian motion, as an efficient … Read more
Each semester the RTG program will host a luncheon for women trainees with a faculty role model, from within or outside UNC, to share and discuss unique challenges, experiences, and opportunities for women in STEM. This semester the luncheon will … Read more
Turning the data-integration dial: efficient inference from different data sources A fundamental aspect of statistics is the integration of data from different sources. Classically, Fisher and others were focused on how to integrate homogeneous sets of data. More recently, the … Read more
Modern Nonconvex Optimization: Parametric and Stochastic Programming Extensions Many modern applications in data science and operations research can be casted as nonconvex and nonsmooth optimization problems, which usually cannot be rigorously handled by applying heuristics or imposing some naive simplications … Read more
Principal Component adjustments for High Dimensional Association Studies In this talk, I will consider the effect of Principal Component (PC) adjustments while inferring the effects of variables on outcomes. This problem is particularly motivated by applications in genetic association studies … Read more
Multivariate Power Laws and Preferential Attachment Modeling In one-dimension, heavy tails or power-laws are easily understood to represent Pareto like behavior where data plotted on a log-log scale looks roughly linear. The generalization to higher dimensions is not always obvious … Read more
Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization First-order minimization algorithms, which rely only on blackbox (sub)gradient oracles, have always played a central role in the field of optimization, and are now even more prominent due to … Read more
Multivariate Power Laws and Preferential Attachment Modeling In one-dimension, heavy tails or power-laws are easily understood to represent Pareto like behavior where data plotted on a log-log scale looks roughly linear. The generalization to higher dimensions is not always obvious … Read more
From HeartSteps to HeartBeats: Personalized Decision-making Ever-increasing access to data and computational power allows us to make decisions that are personalized to users by taking their behaviors and contexts into account. These developments are especially useful in domains like mobile … Read more
On the Computational Complexity of Semidefinite and Polynomial Optimization: A real Algebraic Geometry Approach Semidefinite and polynomial optimization (SDO and PO) are topics of great theoretical and practical interest, with numerous applications in theoretical computer science, control theory, and statistics. … Read more
Multivariate Power Laws and Preferential Attachment Modeling In one-dimension, heavy tails or power-laws are easily understood to represent Pareto like behavior where data plotted on a log-log scale looks roughly linear. The generalization to higher dimensions is not always obvious … Read more
Statistical Thresholds for Tensor PCA Recent work in theoretical physics has produced numerous conjectures linking problems in high- dimensional statistics to the behavior of disordered magnetic materials known as spin glasses. In this talk, I will discuss results from joint … Read more
