Tags: General event


2023 David Lifsey Award Ceremony

On Thursday, December 7, 2022, at 3:15 pm, Mr. David Lifsey will present the "Lifsey Graduate Fellowship in Statistics and Data Science" award to Mr. Ryan C. Smith who is pursuing a master's degree in Statistics as a Double Dawgs student. Dean Anna Stenport will also be attending the ceremony and saying a few words about Mr. Lifsey. Mr. Lifsey is takes very keen interest in the success of our program. When he…


Partha Lahiri

The contribution of Joint Program in Survey Methodology (JPSM) to train graduate students in Survey and Data Science Abstract: The founding of JPSM in 1993 resulted from an initiative of the United States Federal Statistical Agency heads, the head of the Office of Management and Budget’s Statistical Policy Office, and the chair of the U.S. President’s Council of Economic Advisors. The founders of JPSM brought together a consortium of…


Richard A. Davis

Sample Splitting for Assessing Goodness of Fit in Time Series Abstract: A fundamental and often final step in time series modeling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are inherently…


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…


Shiyu Wang

Exploratory Cognitive Diagnosis Models: Attribute Hierarchy Estimation and Exploration of Utilizing Eye-tracking Data. Abstract: Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying Cognitive Diagnosis Models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this…


Andrew Brown

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging Abstract: Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease.…
Subscribe to General event

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.