Research at GVSU

The effects of big data are profound. Everything from tracking disease across the world, managing cities and crisis situations, employing marketing techniques to specify individual consumers, targeting business solutions, and winning elections are just a few of the ways that our society has been changed by big data.

Below are "Big Data" research projects currently underway at Grand Valley.

Edward Aboufadel, Ph.D., Professor of Mathematics

  • Mathematics – applied mathematics, including data-intensive projects and wavelet-based projects.  A recent project involved detecting potholes using data collected from smartphone accelerometers.

Robert Deaner, Ph.D., Associate Professor of Psychology

  • Athletic participation, performance, and pacing in non-elite or recreational athletes. The availability of large datasets from marathons, triathlons, and endurance events allows new questions to be addressed. Our recent study showed that among 91,000 marathoners, slower runners and men paced more poorly.

Mario Fifi, Ph.D., Assistant Professor of Psychology

  • Develops computational models of how short-term memory processing distributes its resources, elucidating the relationship between short term story and attention.  Other work develops computational models of how people decide to stop collecting evidence and proceed with making a decision, as when we stop reading recommendations and decide to make a purchase.  Quantitative model optimization and model-fitting are used to develop and test these computational cognitive models.

Daniel Frobish, Ph.D., Associate Professor of Statistics

  • Predicting Survival Probabilities based on Gene Expression Levels – The goal is to build a statistical model that can be used to predict a patient’s survival probability as a function of time, based on his/her gene expression profile.  Because there are tens of thousands of genes in human beings, there are many, many more variables (gene expression levels) than the typical sample size, which presents significant challenges in developing a model.  My research is focused on determining optimal methods to efficiently sort through the large number of genes to find the ones with most predictive ability.

Deborah Herrington, Ph.D., Associate Professor of Chemistry

  • Evaluation of Target Inquiry (TI) Professional Development Model – collaborative research with Dr. Ellen Yezierski, Miami University, evaluating the impacts of TI on teachers and their students. This evaluation involves combining qualitative analysis of classroom observational video, teacher interviews, and program artifacts with Hierarchical Linear Modeling (HLM) of multiple student pre and post-test measures.  

Sok Kean Khoo, Ph.D., Distinguished Associate Professor of Molecular Genomics

  • Next-Generation Sequencing (NGS) Data Analyses – Collaborative research with Michigan State University Department of Epidemiology & Biostatistics and Pediatrics & Human Development and Wayne State University Institute of Environmental Health Sciences using NGS technology on bloodspot samples to investigate molecular etiology of cerebral palsy.

Jonathan P. Leidig, Ph.D., Assistant Professor of Computing and Information Systems

  • Modeling, simulation, and digital libraries - Scalable computing applications are utilized to model and simulate experiments involving the spread of diseases in humans and animals. Current scientific research is data-intensive and requires digital libraries to be developed that can preserve and provide access to large volumes of scientific content.

Shaily Menon, Ph.D., Professor of Biology and Natural Resources Management

  • Biodiversity Informatics and Ecological Forecasting - collaborative research with colleagues at The Biodiversity Institute, University of Kansas and at various institutions in the tropics, on the use of species occurrence data together with spatial bioclimatic data layers to study the effects of global change – land, sea-level, and climate change.

Wael Mokhtar, Ph.D., Associate Professor of Engineering

  • He is currently an associate professor in the School of Engineering. In his research, Dr. Mokhtar uses Computational Fluid Dynamics (CFD) to study fluid flow applications. His current research includes: vehicle aerodynamics, drag reduction tools, wind tunnel testing, and bio-fluid flow simulation. More information can be found in: http://faculty.gvsu.edu/mokhtarw/index.htm

Rachel Powers, PhD, Associate Professor of Chemistry

  • Research in the Powers lab involves the structure-based discovery and design of novel beta-lactamase inhibitors. The three-dimensional structures of beta-lactamase in complexes with inhibitors are determined by X-ray crystallography and rely on diffraction data sets measured at the Advanced Photon Source at Argonne National Laboratory.

Jerry Scripps, Ph.D., Assistant Professor of Computing and Information Systems

  • Computer Science:  current research in the area of mining large networks.  These are typically social networks or other networks that have the properties of social networks.  Sizes range from a few hundred nodes to hundreds of thousands of nodes.  Currently working on projects of community finding in networks and analysing massive mobile phone data sets for improving public health services.

Paul Stephenson, Ph.D., Professor of Statistics

  • Biosurveillance – Research on developing a Group Control Chart for Monitoring Michigan Geospatial Data and Simulating its Performance
  • Modeling of Fire Data – Statistical Analysis regarding the potential of Independent Occurrences of Serious Residential Fires  

David Zeitler, Ph.D., Associate Professor of Statistics

  • Lake Michigan Wind Assessment data quality and modeling with dozens of variables collected once a second for roughly 9 months each during 2012 and 2013. Working on the GVSU Statistics Department analytics server using Revolutions R Enterprise.
  • Meijer Marketing Analytics, working with trillions of transactions and hundreds of variables using SAS and SQL on their new Teradata high performance computational facilities.
  • Empirical Spectral Test - collaborative research with WMU Computer Science and Statistics faculty using multidimensional spatial Fast Fourier Transforms in R on our analytics server and at the WMU High Performance Computational Science Laboratory.

Jeroen Wagendorp, Geography & Planning Department