Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Dr. Qiwei Yao

Autoregressive Networks We propose a first-order autoregressive model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as the maximum likelihood estimators which are proved to be (uniformly) consistent and asymptotically normal. The model diagnostic checking can be carried out easily…


Devdutt Yellurkar

Bringing Ideas to Fruition—An Investor's Perspective After a brief description of my long journey from a Statistics graduate to an entrepreneur to an investor, I will give examples of how our venture capitalist firm (CRV) helps bring some new ideas to fruition. I will also talk about how statisticians and data scientists can contribute to today's business environment.


Dr. Sheng Li

Knowledge-Guided Graph Representation Learning Graph-structured data are ubiquitous, which have been extensively used in many domains such as social networks and recommender systems. In this talk, I will present our recent work on graph representation learning with applications in multiple domains. First, we leverage the line graph theory and propose novel graph neural networks, which jointly learn embeddings for both nodes and edges. Second, we…


Dr. Wenguang Sun

Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing Exploiting spatial patterns promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally–adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference by constructing robust and structure-adaptive weights according to the estimated local sparsity…


Dr. Yuehua Cui

Kernel-based genome-wide gene-set association test With the radical breakthrough in biotechnology, high throughput genomic data are routinely generated. These data present unprecedented opportunities in disentangling the genetic secret of complex diseases, while also present daunting challenges in statistical modeling and inference. In the last few years, we have witnessed significant advancement in statistical methodology development for…


Dr. Ethan X. Fang

Statistical Modeling and Optimization for Optimal Adaptive Trial Design in Personalized Medicine We provide a new modeling framework and adopt modern optimization tools to attack an  important open problem in statistics. In particular, we consider the optimal adaptive trial design problem in personalized medicine. Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accruing data in a randomized…


Dr. Pang Du

A new change point analysis problem motivated by a liver procurement study Literature on change point analysis mostly requires a sudden change in the data distribution, either in a few parameters or the distribution as a whole. We are  interested in the scenario where the variance of data may make a significant jump while the mean changes in a smooth fashion. The motivation is a liver procurement experiment monitoring organ surface…


Yang Feng

RaSE: Random Subspace Ensemble Classification We propose a new model-free ensemble classification framework, Random Subspace Ensemble (RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners, where each weak learner is a base classifier trained in a subspace optimally selected from a collection of random subspaces. To conduct subspace selection, we propose a new criterion, ratio information criterion (RIC), based…


Aaditya Ramdas

The lady keeps tasting coffee: opening the doors to interactive experimental design and causal inference via betting and cooperation We revisit Fisher's classical randomized experiment from the early 1920s titled The Lady Tasting Tea. To recall, algologist Dr. Muriel Bristol was asked to taste 8 cups of milky tea, 4 made one way (milk first) and 4 made another (tea first). These were presented to Muriel without labels, Fisher conjecturing that…


Bharath Sriperumbudur

Approximate Kernel Principal Component Analysis: Computational vs. Statistical Trade-off Kernel principal component analysis (KPCA) is a popular non-linear dimensionality reduction technique, which generalizes classical linear PCA by finding functions in a reproducing kernel Hilbert space (RKHS) such that the function evaluation at a random variable X has a maximum variance. Despite its popularity, kernel PCA suffers from poor scalability in big…
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