You can contact Ace Vo at firstname.lastname@example.org.
The expertise of Ace Vo includes data mining, spatial analytics, and big data machine learning, especially in the healthcare informatics domain. With a transdisciplinary approach to his work, he continually seeks ways to integrate and augment different domains’ strengths in order to bridge the gap between practice and theory and solve the most pressing problems. Professor Vo joined LMU in fall 2019. He previously taught at San Francisco State University. His research has been featured in several premier journals in information systems such as Decision Support Systems, Information Systems Frontiers, and Journal of Computer Information Systems. He has consulted and worked in various fields, including online advertising, manufacturing, hospitality, and healthcare. In addition, he holds several well-regarded professional certifications, one of which is the Project Management Professional (PMP®) and the Amazon Web Services (AWS) Solution Architect.
Claremont Graduate University: Ph.D., Information Systems and Technology 2017
California State University, Fullerton: M.S., Information Systems 2012
University of Arizona: B.S., Information Systems and Business Management 2008
Areas of Expertise (3)
Big Data Machine Learning
Examining Bitcoin and Economic Determinants: An Evolutionary PerspectiveJournal of Computer Information Systems
After Nakamoto introduced Bitcoin in 2008 as an alternative online payment system, it became an appealing investment vehicle and an attractive area of study for many researchers. Although much research has been done on the valuation of Bitcoin, comparatively little treatment has been spent on Bitcoin’s relationship to established economic indicators, at least in IS research. This study examines Bitcoin across its history using a time-series structural break analysis to differentiate five distinct periods within Bitcoin’s evolution. Data obtained includes the open-high-low-close and volume Bitcoin trading data by the minute from October 15, 2010 until January 1, 2020. Within each period, we examine Bitcoin’s closing price in relation to an established set of economic indicators that encompass economic health, long-term economic stability, monetary policy, and investor sentiment. We find that Bitcoin has matured from a speculative trading mechanism to an independent investment instrument that is responsive to underlying macroeconomic factors.
Identifying socially vulnerable regions with persistent low accessibility to emergency care through a spatial decision frameworkJournal of Decision Systems
During an emergency, areas with high social vulnerability suffer the most. Identifying vulnerable areas with low access to emergency services will aid in prevention and response efforts when hazardous events strike. This paper utilises both the Two-Step Floating Catchment Area and the Enhanced Two-Step Floating Catchment Area methodologies of Geographic Information Systems to measure the accessibility levels for three emergency response thresholds: zero to four minutes, four to eight minutes, and eight to fifteen minutes. An accessibility measurement using the combination of those three response times, from zero to fifteen minutes, is also computed and studied. After investigating the accessibility measurements in light of the Centre for Disease Control’s Social Vulnerability Index, we identified seven tracts in Los Angeles County that are persistently very high in social vulnerability and very low in accessibility. The findings in this research will help to optimise resource utilisation and planning efforts during an emergency.
Developing a High Frequency Algorithmic Trading Strategy for CryptocurrencyJournal of Computer Information Systems
Cryptocurrency such as Bitcoin is a rapidly developing phenomenon in financial technology with considerable research interest but is understudied. In this research article, we use a Design Science Research paradigm to create a high-frequency trading strategy at the minute level for Bitcoin using six exchanges as our Information Technology artifact. We created financial indicators and utilized a machine learning (ML) algorithm to create our strategy. We provided two sets of evaluation. First, we evaluated this strategy against another popular ML algorithm and found our algorithm performed better on the average. Second, we analyzed the economic benefits using the strategy against out-of-sample trading in foreign exchange currency. We presented both descriptive and prescriptive contributions to Design Science Research via the development and testing of our artifacts.
Designing Utilization-based Spatial Healthcare Accessibility Decision Support Systems: A Case of a Regional Health Plan AccessDecision Support Systems
In the U.S., myriad healthcare reforms have begun to show some positive effects on enabling “potential access”. One facet of healthcare access, “having access”, which is the availability and accessibility of health services for the surrounding populations, has not been adequately addressed. Research regarding “having access” is presently championed by a family of methods called Floating Catchment Area (FCA). However, existing scholarship is limited in integrating non-spatial factors within the FCA methods. In this research, we propose a novel utilization-based framework as the first attempt to adopt the Behavioral Model of Health Services Use as a theoretical lens to integrate non-spatial factors in spatial healthcare accessibility research. The framework employs a unique approach to derive categorical and factor weights for different population subgroup's healthcare needs using predictive analytics. The proposed framework is evaluated using a case study of a regional health plan. A Spatial Decision Support System (SDSS) instantiates the framework and enables decision makers to explore physician shortage areas. The SDSS validates the practicality of the proposed utilization-based framework and subsequently allows other FCA methods to be implemented in real-world applications.
A conceptual framework for quality healthcare accessibility: a scalable approach for big data technologiesInformation Systems Frontiers
Healthcare accessibility research has been of growing interest for scholars and practitioners. This manuscript classifies prior studies on the Floating Catchment Area methodologies, a prevalent class of methodologies that measure healthcare accessibility, and presents a framework that conceptualizes accessibility computation. We build the Floating Catchment Method General Framework as an IT artifact, following best practices in Design Science Research. We evaluate the utility of our framework by creating an instantiation, as an algorithm, and test it with large healthcare data sets from California. We showcase the practical application of the artifact and address the pressing issue of access to quality healthcare. This example also serves as a prototype for Big Data Analytics, as it presents opportunities to scale the analysis vertically and horizontally. In order for researchers to perform high impact studies and make the world a better place, an overarching framework utilizing Big Data Analytics should be seriously considered.