Tags: Colloquium Series

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


Xiaoming Zhai

AI and the formative Assesment: The Train Has Left the Station Abstract: Researchers have been questioning AI --whether we can and should use AI for formative assessment. AI is already being employed, for better or worse, to facilitate formative assessment in various educational contexts. In this talk, Dr. Zhai will demonstrate research on AI-based assessment in science education. He will respond to the many concerns raised in the field…


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…


Dr. Yishi Wang

Detecting True Lies, a Bayesian Approach for Modeling Veterans' Credibility using TOMM Abstract: The goal of this research is to evaluate the likelihood of credible responses from examinees, based on Performance validity tests (PVTs) scores from TOMM, the Test of Memory Malingering. Traditional research with TOMM adopts a single cutoff score. Recent studies suggest that different cutoff scores might be a better option, given various preexisting…


Pej Rohani

Early warning signals for disease re-emergence Abstract Developing statistical methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers, especially in the context of disease (re-)emergence. In this talk, I will demonstrate an operational, mechanism-agnostic detection algorithm for disease (re…


WenZhan Song

Connecting the dots for health and security monitoring Abstract This talk introduces our research on sensor web for health and security monitoring. In concern of cyber-physical security, we have created sensor web systems that utlize the spatio-temporal electrical signals in power networks, together with cyber signals, for the security and health monitoring of devices, machines and infrastructures. Electrical devices (including computers,…


Mandev Gill

Mixture models for improved inference of evolutionary dynamics Abstract Scientific studies in many areas of the biology routinely employ evolutionary analyses based on the probabilistic inference of phylogenetic trees from molecular sequence data. Evolutionary processes that act at the molecular level are highly variable, and properly accounting for heterogeneity in evolutionary processes is crucial for more accurate phylogenetic inference.…


Ruizhi Zhang


Adrienne Hoarfrost

Deep Learning Gateways to Illuminating the Functional Potential and Ecosystem Impacts of Microbial Communities Astract: We live in a world dominated by microbes. These microbial communities drive biogeochemical cycles that regulate current and future climate, impact ecosystem health and services, and have shaped the coevolution of life and Earth. Biology, and the biogeochemistry that is driven by it, is characterized by its complexity: in…


Garrett N Vande Kamp

Spatial Regression Models in the Presence of Measurement Error Abstract The estimation of dynamic regression models in the presence of measurement error is well understood, both in terms of consequences and solutions. But there is considerable ambiguity as to the estimation of spatial regression models in the presence of measurement error. My coauthor and I investigate this possibility, finding that there are both unique challenges and unique…
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