Industrial engineering
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Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification
Uncertainties exist in both physics-based and data-driven models of systems. Understanding how system inputs affect a system output's uncertainty is essential to improve system outputs such as quality and productivity. ... -
Take-Over Time Modeling and Prediction for Conditional Driving Automation
Autonomous vehicles are designed to enhance the overall driver safety by taking the driver out of the loop. However, the autonomous vehicles that are currently available on the market still require that the driver is ... -
Zero-inflated Models for Semi-continuous Transportation Data
Zero-inflated models have been widely studied and are commonly used in the transportation safety area. Despite the success of zero-inflated models to analyze static data with counting outcomes, challenges remain in the ... -
Simulation and Statistical Methods in Proactive and Strategic Obsolescence Management
In fields with sustainment-dominated systems, where the sustainment costs are larger than the original system costs, managing the life-cycle costs is a non-trivial task. These fields include aviation, power plants, medical, ... -
Exact and Heuristic Approaches to Middle and Last Mile Logistics
Logistics is a well-studied field in operations research. Numerous authors have done extensive work in this area, especially in the domain of routing problems. However there are still aspects of routing to be better explored. ... -
The Effect of Task Complexity on Time Perception in the Virtual Reality Environment: An EEG Study
Background: Virtual reality (VR) technology is increasingly being utilized for multiple purposes. Unlike traditional 2D devices, VR headsets allow individuals to enjoy an immersive experience that includes multisensory ... -
Feature Extraction Using Topological Data Analysis for Machine Learning and Network Science Applications
Many real-world data sets can be viewed as a noisy sampling of an unknown high-dimensional topological space. The emergence and development of topological data analysis (TDA) over the last fifteen years or so provides a ... -
Mixed Integer Quadratic Optimization for Learning Directed Acyclic Graphs from Continuous Data
The study of probabilistic graphical models (PGMs) is an essential topic in statistics and machine learning fields. Bayesian networks (BNs), arguably one of the most central classes of PGMs, is frequently used to represent ... -
Modeling driver behavior and their interactions with driver assistance systems
As vehicle automation becomes increasingly prevalent and capable, drivers have the opportunity to delegate primary driving task control to automated systems. In recent years, significant efforts have been placed on developing ... -
Contemporaneous Health Monitoring and Biomarker Discovery by Integration of Patient Data and Disease Knowledge
Technological innovations have given rise to data-rich environments that support the use of heterogeneous sensor measurements to monitor complex healthcare systems. Despite these advancements, however, there remains little ... -
System Dynamic Modeling as Applied to Coast Guard Cutter Home Porting Decisions: Innovating Systems Engineering Processes in Federal Agencies to Engage Stakeholders, Stimulate Local Economies and Benefit the Public Good
The U.S Coast Guard uses a codified Systems Engineering process to determine the best location for new Coast Guard cutters. This process, called the Cutter Homeport Decision Process (CHDP), quantifies stakeholder input ... -
Optimizing Healthcare Policies through Healthcare Delivery and Insurance Design
The problems of efficient prevention, screening, and treatment interventions for chronic diseases and acute infections have received much attention in recent years because of the rise in American healthcare costs. Chronic ... -
Optimizing Personalized Treatment Selection for Partially Observable Chronic Conditions
For many chronic diseases, an individual patient may experience a wide variety of progres- sion pathways. Personalized medicine needs tools to predict the trajectory of an individual patient’s disease progression, which ... -
Stochastic Combinatorial Optimization with Applications in Graph Covering
We study stochastic combinatorial optimization models and propose methods for their solution. First, we consider a risk-neutral two-stage stochastic programming model for which the objective value function of the second-stage ... -
Convex and Robust Optimization Methods for Modality Selection in External Beam Radiotherapy
The goal in external beam radiotherapy (EBRT) for cancer is to maximize damage to the tumor while limiting toxic effects of radiation dose on the organs-at-risk (OAR). EBRT can be delivered via different modalities such ... -
Risk-Averse Optimization in Multicriteria and Multistage Decision Making
Risk-averse stochastic programming provides means to incorporate a wide range of risk attitudes into decision making. Pioneered by the advances in financial optimization, several risk measures such as Value-at-Risk (VaR) ... -
Optimizing Population Healthcare Resource Allocation Under Uncertainty Using Global Optimization Methods
Due to the rise in American healthcare costs, clinic administrators are increasingly concerned with optimally delivering service to patients. Due to the complex and uncertain nature of patient demand and other factors, ... -
Information theoretic learning methods for Markov decision processes with parametric uncertainty
Markov decision processes (MDPs) model a class of stochastic sequential decision problems with applications in engineering, medicine, and business analytics. There is considerable interest in the literature in MDPs with ... -
Methodology for the Preliminary Design of a System of Integrated Clinical Laboratories
Clinical laboratory testing is used for deriving patient diagnoses, monitoring treatment and predicting the expected outcome of a disease (prognosis). Thus, the performance of clinical laboratories is critical for our ... -
Optimization and Scheduling Methodologies to Enable Low Earth Orbit Nano-satellite Communication
Communications with low earth orbit (LEO) nano-satellites (nanosats) are challenging due to the short contact time intervals with ground nodes and uncertainty in successful delivery of messages due to the varying signal-to-noise ...
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