Geographical and two-dimensional regression discontinuity designs (RDDs) extend the classic, univariate RDD to multivariate, spatial contexts. We propose a framework for analyzing such designs with… read more about A Bayesian Nonparametric Approach to Geographic and Two-Dimensional Regression Discontinuity Designs »
Discrete random probability measures stand out as effective tools for Bayesian clustering. The investigation in the area has been very lively, with a strong emphasis on nonparametric procedures based… read more about Finite-dimensional discrete random structures and Bayesian clustering »
Chris Glynn is Head of Data Science at the Indeed Hiring Lab Economic policy organizations across the globe – including central banks, ministries of treasury, housing, commerce, labor, and more –… read more about Building Economic Data Products for Global Policymakers »
A broad class of regression models that routinely appear in several fields of application can be expressed as partially or fully discretized Gaussian linear regressions. Besides incorporating the… read more about The role of skewed distributions in Bayesian inference: conjugacy, scalable approximations and asymptotics »
Kelci Miclaus - Global Head of HLS AI Solutions, Dataiku The opportunities for machine learning applications in the drug discovery and development process continue to grow and are changing the… read more about Machine Learning in Drug Discovery with Dataiku »
As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming increasingly popular for approximating intractable posterior distributions in large-scale… read more about On the Convergence of Coordinate Ascent Variational Inference »
Funda Güneş, Director of the Master's Program, Statistical Science, Duke University This talk delves into the important topic of fairness and accountability in artificial intelligence systems.… read more about Ensuring Fairness and Transparency in Artificial Intelligence: Best Practices and Techniques »
Randomized experiments are the gold standard for inferring a causal effect. Consequently, many organizations run thousands of randomized experiments to quantify the impact of product changes, which… read more about Design-Based Anytime-Valid Causal Inference »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Topics in Applied Statistics »
Larry Blakeman and Gaurav Gupta, QuaEra Insights read more about Data Science, QuaEra »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Some Advances in Nonparametric Statistics »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Modeling Heterogeneity with Bayesian Additive Regression Trees »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about Causal Inference for Natural Language Data and Multivariate Time Series »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Advanced Topics in Introductory Statistics »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Geometric Methods for Point Estimation »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Advances in Bayesian Hierarchical Models Motivated by Environmental Applications »
In this session, Christina will cover job search strategies for students looking for internships and jobs in the data science and statistics fields. She'll also discuss resources for international… read more about Job Search Strategies for Data Scientists »
To select outcomes for clinical trials testing experimental therapies for Huntington disease, a fatal neurodegenerative disorder, analysts model how potential outcomes change over time. Yet, subjects… read more about Mission Imputable: Correcting for Berkson Error When Imputing a Censored Covariate »
Dr. Radhika Kulkarni, Retired VP of Advanced Analytics, SAS Reflections from my Analytical Journey: Challenges, Opportunities, Lessons Learned As you embark on your journey with an… read more about Reflections from my Analytical Journey: Challenges, Opportunities, Lessons Learned »
Dr. Thomas will provide an overview of Data Science and Biostatistics work at DCRI and highlight core work areas such as clinical trials and implementation science. She will share her… read more about Biostatistician Careers, Duke Clinical Research Institute »
Dr. Elizabeth Mannshardt, Director of the Statistics, Methods, and Innovation Program, NSF Combining experiences across industry, government, and academic research, as well as university… read more about Navigating Your Professional Success: Tips for Networking and Launching Your Career »
This week's department seminar will feature talks from Statistical Science undergraduate students involved in research. Each of the undergraduate speakers will have 5-7 minutes to present their… read more about Undergrads Take Over StatSci Seminar! »
Richard Zink, VP of Biostatistics and Statistical Programming, Lexitas Pharma Services Students and young professionals with statistics, biostatistics, or data science degrees have numerous career… read more about Statistics Careers in the Medical Product Industry »
Dr. Zachary Abzug, Manager, Vectra AI and Daniel Salo, Manager, Proofpoint Have you ever trained a perfect classification model on the Iris dataset on your laptop in eight lines of… read more about Machine Learning Metamorphosis: Breaking Models Out from your Local Machine and Releasing them into the Cloud »
Randomized experiments allow for consistent estimation of the average treatment effect based on the difference in mean outcomes without strong modeling assumptions. Appropriate use of pretreatment… read more about To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates »