April 13, 2022

Detecting formation and growth of refugee / displaced person camps in the Ukraine crisis: Assisting first-phase humanitarian response using satellite imagery

Amid the worst population displacement crisis in Europe since World War 2, governments and international organizations have struggled with the massive task of tracking and providing aid to Ukrainian refugees and internally displaced persons (IDPs). This presentation reviews requirements and explores solutions for AI-assisted monitoring of formation of ad-hoc refugee encampments and temporary/informal settlements in remotely-sensed imagery to support time-critical humanitarian operations. Data and models for binary geospatial prediction of camp location as well as time-series camp expansion will be discussed, as will deep methods for characterizing similarities and differences among detected encampments.

April 13, 2022

A Computational Respiration Factor to Detect Abnormal Respiratory Patterns Using a Hydraulic Bed Sensor for Older Adults Aging-in-place

Hydraulic bed sensors are efficient non-wearable, passive sensors for unobtrusive and continuous collection of health data for older adults aging-in-place. Continuous collection of data can speak volumes about onset and development of a disease much before it is diagnosed and that is our vision through this work. Hydraulic bed sensor data yields signal from which three major physiological components can be extracted for sleeping individuals, the ballistocardiogram component, the respiration component, and the bed restlessness component. In this work, we focus on the respiration component to detect any abnormal patterns in respiration, more specifically related to Chronic Obstructive Pulmonary Disorder…

April 11, 2022

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 neighborhoods to regions. The collection, integration, and use of varied data are foundational to addressing the complex questions of today’s health research. To strategically transform this research, a robust integrated data platform is needed. This research presentation focuses on the design, development, implementation, and use…

April 6, 2022

MUIDSI Online Data Science and Analytics Program Ranked #7 in Fortune Magazine

The MS Data Science and Analytics (DSA) Program of the MU Institute for Data Science and Informatics (MUIDSI) was  recently ranked the 7th best online master’s degree in Data Science Programs in 2022 by Fortune magazine. The rankings were composed of a selectivity score and a demand score. The selectivity score made up 85% of the ranking which assessed undergraduate GPA of incoming students, the average years of work experience and the universities acceptance rate.  The demand score consisted of total enrollment size of the programs and the number of applicants for the most current year.  As data information continues to rapidly…

April 4, 2022

Harnessing the power of AI to advance knowledge of Type 1 diabetes

An interdisciplinary team of researchers from the University of Missouri, Children’s Mercy Kansas City and Texas Children’s Hospital has used a new data-driven approach to learn more about persons with Type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The team gathered its information through health informatics and applied artificial intelligence (AI) to better understand the disease. In the study, the team analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry. By applying a contrast pattern mining algorithm developed at the MU College of Engineering, the team was able to…

April 4, 2022

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 determine whether those features are specific to Apis or shared with other insects and the biological meaning of those features. Chapter 1 reviews the major concepts that tie my dissertation research together, highlighting the importance of recombination, GC composition, and their relationship to the evolution of eusociality.

March 25, 2022

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 currently focused on type 2 diabetes (T2D), which accounts for approximately 90% of diabetes cases. The expanding availability, granularity, and size of real-world health data, however, is opening unprecedented opportunities to use health informatics to advance T1D research that is computationally innovative and responsive…

March 16, 2022

Development of AI Models for Remote Sensing City Fitness

Tracking a city’s fitness is particularly important for the continued urbanization of civilization. Urban environmental and air quality and overall fitness are decreasing due to natural and anthropogenic events, causing degradation of living quality and leading to various population health issues, including heart and lung problems and even premature death. City fitness monitoring is mostly dependent on ground sensor deployment. However, this ground sensor-based monitoring is often not continuous and extensive due to the lack of resources and a very low number of ground observations. Another approach is to estimate city fitness parameters using models built with remote sensing (RS)…

March 16, 2022

Tool development for resolving and visualizing irregular heartbeats measured by cardiac magnetic resonance

Atrial fibrillation (AFib) is the most common irregularity of heartbeats. it can cause significant symptoms and impair heart function and daily life. Its irregular and often very rapid heart rhythm can lead to blood clots that cause stroke or heart attack, especially as the patient ages. The irregularity of heartbeats has prevented visualization of the heart using standard cardiac magnetic resonance (CMR), which attempts to average the beats. movie involves more information about the heart movement. Unaveraged, real-time CMR imaging can scan the heart of AFib patients. However, Radiology lacks methods to manage the irregularity. We developed tools for easy…