You can contact Au Vo at firstname.lastname@example.org.
The expertise of Dr. Au Vo are 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. Dr. Vo joined LMU in fall 2019. He previously taught at San Francisco State University. His research work 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
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.
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.
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.