$$ \newcommand{\R}{\mathbb{R}} \newcommand{\SSS}{\mathbb{S}} \newcommand{\E}{\mathbb{E}} \newcommand{\C}{\mathbb{C}} \newcommand{\N}{\mathbb{N}} \newcommand{\Z}{\mathbb{Z}} \newcommand{\P}{\mathbb{P}} \newcommand{\Q}{\mathbb{Q}} \newcommand{\frakX}{\mathfrak{X}} \newcommand{\frakB}{\mathfrak{B}} \newcommand{\frakW}{\mathfrak{W}} \newcommand{\Id}{\mathrm{Id}} \newcommand{\dist}{\mathrm{dist}} \newcommand{\span}{\mathrm{Span}} \newcommand{\diag}[1]{\mathrm{diag}(#1)} \newcommand{\dim}[1]{\mathrm{dim} \, #1} \newcommand{\tr}[1]{\mathrm{tr}(#1)} \newcommand{\partder}[2]{\frac{\partial #1}{\partial #2}} \newcommand{\rank}[1]{\mathrm{rank} \, #1} \newcommand{\inner}[1]{\langle #1 \rangle} \newcommand{\innerbig}[1]{\left \langle #1 \right \rangle} \newcommand{\abs}[1]{\lvert #1 \rvert} \newcommand{\norm}[1]{\lVert #1 \rVert} \newcommand{\ker}[1]{\text{ker}(#1)} \newcommand{\supp}[1]{\text{supp}(#1)} \newcommand{\calA}{\mathcal{A}} \newcommand{\calB}{\mathcal{B}} \newcommand{\calC}{\mathcal{C}} \newcommand{\calD}{\mathcal{D}} \newcommand{\calE}{\mathcal{E}} \newcommand{\calF}{\mathcal{F}} \newcommand{\calG}{\mathcal{G}} \newcommand{\calH}{\mathcal{H}} \newcommand{\calI}{\mathcal{I}} \newcommand{\calJ}{\mathcal{J}} \newcommand{\calK}{\mathcal{K}} \newcommand{\calL}{\mathcal{L}} \newcommand{\calM}{\mathcal{M}} \newcommand{\calN}{\mathcal{N}} \newcommand{\calO}{\mathcal{O}} \newcommand{\calP}{\mathcal{P}} \newcommand{\calQ}{\mathcal{Q}} \newcommand{\calR}{\mathcal{R}} \newcommand{\calS}{\mathcal{S}} \newcommand{\calT}{\mathcal{T}} \newcommand{\calU}{\mathcal{U}} \newcommand{\calV}{\mathcal{V}} \newcommand{\calW}{\mathcal{W}} \newcommand{\calX}{\mathcal{X}} \newcommand{\calY}{\mathcal{Y}} \newcommand{\calZ}{\mathcal{Z}} \newcommand{\bh} \newcommand{\bf} \newcommand{\Pker}{P_{\ker{L}^{\perp}}} $$

About Me

I am Yidan, or you may know me as Eden. I'm currently a 2nd year M.S. Statistics student at University of Washington. My undergraduate studies were at Imperial Colllege London, where I spent three wonderful years studying Math and Statistics. My current interests admit Kernel Methods, Bayesian Inference, Spatial Statistics and Representation Learning for Strcutured Data. I spent Last summer working on Small area estimation with Log-Gaussian Cox Process (Github repo) with Professor Seth Flaxman. Before that, I did a UROP with Professor Anthony Bellotti at Imperial. For more details, please see my CV.

Email

yx2516[at]uw.edu

LinkedIn

https://www.linkedin.com/in/yidan-xu-06622215a/

Github

https://github.com/edenx

Talk

Gaussian Process on Non-Euclidean Domain

Geometric Data Analysis (GDA) Reading Group (UW)

[slide]

Coursework

Graph Laplacian: Consistency and Connection with Kernel Learning

STAT 535 Statistical Learning: Modeling, Prediction, and Computing (UW)

[report]

A Review: Toward Deeper Understanding of Neural Networks, Daniely et al. 2016

STAT 538 Statistical Learning: Kernel Methods (UW)

[report]

A Review: Constructing Priors that Penalizes the Complexity of Gaussian Random Fields, Fuglstad et al. 2019

STAT 517 Stochastic Modelling of Scientific Data (UW)

[report]

Preprint

Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization.

Qi Zhu, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han, Carl Yang arXiv preprint arXiv:2009.05204.

[arxiv]