Industrial engineering
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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 ... -
Approximate dynamic programming for weakly coupled Markov decision processes with perfect and imperfect information
A broad range of optimization problems in applications such as healthcare operations, revenue management, telecommunications, high-performance computing, logistics and transportation, business analytics, and defense, have ... -
Robust, Non-stationary, and Adaptive Fractionation in Radiotherapy
In external beam radiotherapy for cancer, high-energy radiation is passed through the pa- tient’s body from an outside source to kill tumor cells. The challenge is that radiation also damages healthy tissue and organs-at-risk ... -
Large-Scale Personalized Health Surveillance by Collaborative Learning and Selective Sensing
Recent advance in sensing and information technology provides abundance of risk predictive data, leading to the development of personalized health surveillance. However, effective use of the sensing technology is prohibited ... -
Diagnostic Monitoring of High-dimensional Networked Systems (with Applications in Manufacturing and Healthcare System)
Rapid advances in sensor and information technology have resulted in both spatially and temporally data-rich environment, which creates a intensive need for us to develop novel statistical methods and computationally ... -
Integrated Approach and Analysis of Reliability, Robustness, Resilience and Infrastructure Applications
A rising number of man-made and natural disasters have occurred since the beginning of the 21st century in the United States. Improving the quality of civil infrastructure in an increasingly hazardous environment has become ... -
The Influence of Hospital Resource Factors on Adverse Health Outcomes
The "Influence of Hospital Resource Factors on Adverse Health Outcomes" covers the exploratory, observational study that aims to determine the association between resource accessibility variables and adverse health outcomes ... -
Assessing Cognitive Workload of In-Vehicle Voice Control Systems
In-Vehicle Information Systems (IVIS) are becoming more accessible to drivers and contain more complex communication features. Voice control systems (VCS's) promise to be less distracting than visual-manual interfaces, but ... -
Optimization and Machine Learning Methods for Medical and Healthcare Applications
The increasing amounts of data being gathered in healthcare and medical systems and the convergence of different domains are leading medical and healthcare research to a new direction of precision and personalized medicine. ... -
Objective Methods for Characterizing Physical Exposures which may contribute to Work-related Musculoskeletal Disorders in Agricultural Workers
US Migrant farmworkers perform physically-demanding work but are less likely to report poor working conditions, which exposes them to greater risk for musculoskeletal disorders (e.g., prolonged non-neutral postures, ... -
Systems Modeling of Lung Cancer Screening Programs to Improve Quality
The main aim of this thesis is to study the factors that affect the quality of lung cancer screening with special focus on community based lung cancer screening programs. The variation in false positives and other outcomes ... -
Vehicle Distance Threshold for Quayside Operations of Container Terminals
In container terminal quayside operations, vehicles play a vital role in shuttling containers between the quay and the yard. The problem of determining the vehicle fleet size to employ consists of determining the exact ... -
Discrete-event Simulation and Optimization to Improve the Performance of a Healthcare System
Healthcare systems have attracted the attention of management and analysis due to their high percentage of the gross domestic product (GDP) and increasing rate of growth of expenditures. Within the various types of healthcare ... -
Modeling Depression Progression Dynamics from Electronic Health Record
To assess and monitor the progression dynamics of patients' depression severity conditions, Markov models are refined from other disease progression modeling methodologies to identify the characteristics and evolvement of ...
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