Convergence of Gaussian kernelized graph Laplacian: eigen-convergence and bi-stochastic normalization Consider kernelized graph affinity matrix constructed from $N$ data points i.i.d. sampled from a general unknown $d$-dimensional manifold embedded in a possibly high-dimensional space. The setting is generic in graph-based … Read more
Group advising session with undergrad advisors Will Lassiter and Remi Abayomi will be held on Monday October 17th from 5-6pm in Hanes 130.
Title: Graph representation learning for single-cell genomics Abstract: Single-cell RNA sequencing measures gene expression at an unprecedented resolution and scale and allows the analysis of cellular phenotypes which was not possible before. In this context, graphs occur as a natural … Read more
Yiyun Luo Online Decision Making in Dynamic Pricing and Assortment Selection Under the direction of Yufeng Liu Online decision making is an interdisciplinary research field which lies at the interface of Statistics, Machine Learning, and Operations Research. … Read more
Reinforcement Learning under Unmeasured Confounding In practical reinforcement learning (RL), a representation of the full state which makes the system Markovian and therefore amenable to most existing RL algorithms is not known a priori. Decision makers are often facing so-called … Read more
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
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
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
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
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
