Tags: Colloquium Series

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


Yang Yang

Conditional likelihood – the key to analyzing infectious disease transmission in close contact groups Abstract  Close contacts groups such as households, schools and hospitals are ideal venues for understanding transmission characteristics of infectious diseases and for evaluating intervention effectiveness, due to the feasibility of tracking individual-level exposure history. However, observation of such transmission dynamics is often…


Rik Sen

Estimating the causal effect on a proportional change in probability -- an application to measuring vaccine effectiveness Abstract Instrumental variables analysis and other similar techniques, such as regression discontinuity design (RDD), have become important in finance and economics for obtaining credible causal estimates using observational data. This talk will first introduce a situation where the standard RDD falls short of producing a…


Hongyue Sun

Data science enabled decision-making in advanced manufacturing and personalized safety Abstract The advancements of sensing and information technology have brought significant opportunities to engineering systems, where data containing rich streaming and heterogenous information are collected in manufacturing, occupational, and other systems. However, there is a lack of systematic methodologies to address these high dimensional, streaming, and…


Aditya Mishra

On integrative statistical learning approach for cancer genomics and microbial science Abstract The modeling of interrelated responses utilizing correlated predictors within high-dimensional contexts emerges as a compelling conundrum in the realm of integrative statistical learning, resonating across diverse scientific inquiries. This dilemma is manifest in scenarios such as genomics, where insights into the modulation of gene expression in…


Ansu Chatterjee

Bayesian and machine learning frameworks for studying climate anomalies and social conflicts Abstract Climate change stands to have a profound impact on human society, and on political and other conflicts in particular. However, the existing literature on understanding the relation between climate change and societal conflicts has often been criticized for using data that suffer from sampling and other biases, often resulting from being too…


Dipak Kumar Dey

Agenda: 3:30 - 4:00pm - Arrival (145 Brooks Hall)4:00 - 4:05pm - Opening remarks by Associate Dean Lyall4:05 - 4:10pm - Introduction by Head TN Sriram4:10 - 5:00pm - Lecture, Dr. Dipak Dey, University of Connecticut.5:00 - 5:30pm - Break5:30 - 7:00pm - Dinner at Founders Memorial Garden7:00 - 7:05pm - Remarks by Dean Stenport (145 Brooks Hall)7:05 - 7:30pm - After-Dinner Talk, Dr. Dipak Dey, University of Connecticut. Bio: Dipak Kumar Dey is an…


Ninghao Liu

Language Models for Cold-Start Recommendation Abstract: Recommender systems help users to find contents that fit their interests. However, in cold-start scenarios, we cannot collect user-item interaction records as data for model training. This talk will present our research on developing personalized recommendation systems without using historical user-item interactions. Specifically, we will first discuss a prompt learning framework with pre-…


Xiaoming Huo

Some New Results on the Stochastic First-Order Methods in Parameter Estimation Abstract: We study the first-order stochastic methods that can be utilized to solve the optimization problems derived from parameter estimation in statistics. The stochastic algorithm has a low cost per iteration and is more suitable for a large-size dataset. The first-order method only involves gradients; therefore, its implementation is more straightforward than…


Yingqi Zhao

Estimation and inference on high-dimensional individualized treatment rule in observational data Abstract: With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules (ITRs), which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for ITRs developed from this type of data in…


Ray Bai

Generative Quantile Regression with Variability Penalty Abstract: Quantile regression and conditional density estimation can often reveal structure that is missed by mean regression, such as heterogeneous subpopulations (i.e. multimomodality) and skewedness. In this talk, we introduce a deep learning generative model for joint quantile regression called Penalized Generative Quantile Regression (PGQR). Our approach simultaneously generates…
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