Phase Transitions of Random Constraint Satisfaction Problems The framework of constraint satisfaction problems (CSPs) captures many fundamental problems in combinatorics and computer science, such as finding a proper coloring of a graph or solving Boolean satisfiability problems. To study the … Read more
New Perspectives on Data-Driven Optimization and Neural Network Uncertainty Quantification I will talk about some recent methodologies to understand and quantify the impact of data in relation to optimization and simulation. In the first part of the talk, we create … Read more
Coalescent Theory and its Applications in Population Genomics As we gain access to more datasets with finer resolution, what assumptions should we make in our models? Idealized models in population genetics, which often lack spatial structure and other realistic features … Read more
Enhancing Decision-making in Security & Defense: A Compilation of Quantitative Approaches Navigating the intricacies of modern security problems often involves grappling with stochastic, uncertain, and non-stationary elements, compounded by the presence of competing agents who have disparate knowledge and varied … Read more
Active Label Acquisition in Predict-then- Optimize Framework: Feature-dependent Values of Data Points When collecting data for decision-making, the informativeness of the data is crucial. In the predict-then-optimize framework, a common method for obtaining personalized decisions, training a prediction model with … Read more
Holistic Robust Data-Driven Decisions via Distributionally Robust Optimization The design of data-driven formulations for decision-making and machine learning with good out-of-sample performance is a key challenge. The observation that good in-sample performance (training) does not guarantee good out-of-sample performance (deployment) … Read more
Manifolds and Decision Theoretic Methods for Precision Medicine Precision medicine has undergone substantial theoretical advancements spanning various levels of human health, from cellular subtyping to the development of individualized dynamic treatment regimes (DTRs), decision rules that map patient states to … Read more
Inference and Decision-Making Amid Social Interactions From social media trends to family dynamics, social interactions shape our daily lives. In this talk, I will present tools I have developed for statistical inference and decision-making in light of these social interactions. … Read more
Advice-Augmented Algorithms for Online Matching and Resource Allocation Real life problems are full of uncertainty. How we handle it is important, since it affects the design and performance of algorithms. Often, the uncertainty is assumed to follow some known distribution, … Read more
Peter Song, Department of Biostatistics, University of Michigan Title: Supervised Homogeneity Pursuit via Mixed Integer Optimization Abstract: Stratification is one statistical principle in data processing to mitigate the underlying population heterogeneity, which is typically handled by clustering when stratum labels are … Read more
TITLE: Generative Quantile Regression with Variability Penalty ABSTRACT: Quantile regression and conditional density estimation can reveal structure that is missed by mean regression, such as multimodality and skewness. In this talk, we introduce a deep learning generative model for joint quantile estimation … Read more
Sajad Mordaresi Kenan-Flagler Business School University of Northing Carolina at Chapel Hill Exploration Optimization for Dynamic Assortment Personalization under Linear Preferences Abstract We study the dynamic assortment personalization problem of an online retailer that adaptively customizes assortments based on … Read more
Sara Shashaani Department of Industrial and Systems Engineering North Carolina State University Title: Adaptive Sampling with Trust-region Optimization for Nonconvex Stochastic Functions. Abstract: Simulation Optimization problems are often non-convex, derivative-free, and with stochastic noise that may be notably heteroskedastic. There is … Read more
Generative modeling with trees and recursive partitions Trees and recursive partitions are most well-known in supervised learning for predictive tasks, such as regression and classification. Famous examples include CART and its various forms of ensembles—e.g., random forest and boosting. A … Read more
Mike Baiocchi Department of Epidemiology and Population Health Stanford University
