Speakers and Events

MUIDSI Comprehensive Exam — Measuring Geodiversity in Remotely-Sensed Imagery: Deep Spatial Change Detection Methods for Dataset Bias Mitigation and Visual Landscape Characterization

Amid explosive growth in availability of multimodal remotely sensed imagery (RSI) data from a constellation of overhead sensors, a lack of understanding persists concerning the actual content of these data sources, in particular the nature of spatial variation in the visual and contextual features in the landscape being imaged. Whether described as spatial domain shift,

GIScience as an Interdisciplinary Bridge in Indigenous Health Equity

GIS and geographic theories can help bridge a crucial gap in interdisciplinary research projects. Geography is uniquely poised to offer critical and practical analytical support, wrangle spatial data and relate them to other datasets, and ground community-based science within the communities it aims to serve. In the context of the Navajo Nation, a key concern

Comprehensive Exam: Using subgroup discovery techniques to identify a high-risk group of suicide attempts among people with diabetes

In 2019, 37.3 million Americans had diabetes mellitus, and 1.2 million Americans aged 18 and older had attempted suicide in the past year. According to previous studies, people with type 1 diabetes were three to four times more likely to attempt suicide, and newly diagnosed with type 2 diabetes were two times more likely to

Dissertation Defense: DESIGN AND DEVELOPMENT OF GEOSPATIAL ANALYTICAL RESEARCH KNOWLEDGEBASE (GeoARK)

A consistent finding across health, social, business, and environmental literature is that location matters. To conduct impactful research that can be applied to real-world issues and problems, the research must be grounded within the context of the real world in both place and culture. Significant differences exist and can vary across scales from blocks to

Dissertation Defense – UNDERSTANDING GENOME COMPOSITION OF EUSOCIAL HYMENOPTERAN INSECTS

Genome sequencing of the Western honey bee (Apis mellifera), a model for the biology and evolution of eusocial behavior, has revealed unusual genome compositional characteristics, including a low but heterogeneous GC content, bimodal GC content distribution, and a biased tendency of genes to be located in low GC regions. In this dissertation, we sought to

Comprehensive Exam: Accelerating Data-Driven Discovery In Type 1 Diabetes:An Informatics-Based Approach

Type 1 diabetes (T1D) is an immune-associated or immune-mediated chronic disease characterized by the progressive failure or targeted destruction of insulin-producing beta (β) cells in the pancreas. Management of the disease is challenging, involving lifelong exogeneous insulin replacement and 24/7 blood glucose monitoring. Although more than 1.6 million Americans are living with T1D, most diabetes research is

An Evaluation of an Electronic Medical Record (EMR) Based System to Characterize and Correlate Physician Burnout and EMR Use

Burnout disproportionately affects healthcare workers and continues to rise, contributing to cost, quality, and patient safety risk in an already overburdened United States healthcare system. While the causes of burnout are complex, evidence suggests that Electronic Medical Record use (EMR) is one major contributor due to the increased clerical burden that decreases patient contact time and disrupts the provider clinical workflow. The challenge of improving the

EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR PATIENT STRATIFICATION AND DRUG REPOSITIONING

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning

Bioinformatics Research and Collaboration in the Era of Big Data and Precision Medicine

As we have entered the precision medicine and big data science era, there are many unmet challenges on identifying the disease related information from large, heterogeneous data, and translating the findings for clinical use. Among these challenges, one is how to effectively identify driver mutations and genes in cancer genomes, especially those with the potential

Computational Subgroups Stratification and Precision Drug Repositioning Using Explainable Artificial Intelligence

Enabling precision medicine requires developing robust patient stratification methods and identifying drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Once discovering these subgroups, we can align patients and medications more specifically to achieve precision-based therapy. Developing de novo drugs is expensive and time-consuming with an ultimately low FDA approval rate. These limitations make developing

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