Tags: General event


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.…


William Brenneman

The Interplay between Statistical Practice and Academic Research Abstract: In the statistics field, there is a strong symbiotic relationship between academics and industry. We will explore this relationship primarily from a practicing statisticians’ perspective and discuss the lessons learned on how to foster, grow and bridge the gap between statistical practice and academic research. Several problems encountered in industry that led to academic…


David Banks

Deming and the Industries of Today Abstract: Dr. Deming was one of the foundational leaders in industrial statistics, with contributions to experimental design, sampling, and process control. More importantly, he changed the culture of business leadership in two nations, and implicitly, around the world. But the industries of his day focused on manufacturing, while today’s industries reflect the knowledge economy. This talk asks the industrial…


Vladimir Dragalin

SIMULATION GUIDED CLINICAL TRIAL DESIGN   Abstract: Simulation guided Clinical Trial Design is a framework for stimulating scientific dialogue between trial designer and clinical team with the final goal of developing the most appropriate design for the study. We create different “what-if” scenarios under different assumptions about the drug effect and simulate data from such scenarios and apply the design. We then see if under a scenario…
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