Faculty

• Aeroecology and biologging
• Radio-frequency identification (RFID), geolocators, moonwatcher
• Data processing and management (ETAG For RFIDs, TAGS)

Carlson uses large administrative datasets to estimate the effects of policies on social, political, and economic outcomes of interest. I incorporate data exercises into all the courses I teach.

Michael Crespin's research focuses on legislative politics, congressional elections, and political geography. I teach undergraduate research fellows and graduate students outside of the classroom how to work with data.

Chris Garneau is a quantitative social researcher who primarily conducts research with large data sets. His broader research disciplines include sociology, political science, social psychology, and environmental studies. The more specific focus of his research has centered on the intersection of politics, religion, social psychology, and environment. He has experience with higher-level statistical analysis like hierarchical age-period-cohort modeling to measure social change. Garneau recently started exploring machine learning techniques that complement foundational social science statistical methodology. Some of the ML techniques he is familiar with include the SHAPley method, SOM and K-Means clustering, and natural language processing. He has a strong interest in transdisciplinary research with scholars in dissimilar disciplines in the humanities and natural sciences.

My primary research topic concerns data models for semantically encoding the many types of information in critical editions of literary and documentary texts in ancient languages, especially Latin. I also develop software based on these models to facilitate the creation and the use of born-digital critical editions. Leveraging the best practices known as Linked Open Data to record, preserve, and study texts, people, and objects related to the ancient world is another research interest of mine. Although I have not taught courses on these subjects, I have directed many undergraduate research projects that focus on the intersection of technology and the humanities.

• Greek in Late Antiquity and Byzantium
• Multilingualism in the Christian East
• Syriac Language and Literature
• The Organization of Knowledge in the Ancient and Medieval Worlds

My research predominantly use survey data in addition to publication data (bibliometrics) in some cases. In most cases, I use ego-centric network data collected by the survey via name generator and interpreter questions to conduct social network analysis and visualize individuals' social network. Method-wise, I'm trained as quantitative methods scholar with some understanding of Bayesian statistics. I use R for my research.

Asian immigrants' diabetes disparity research is in the process of collecting data. The purposes are to investigate diabetes indicators salient to Asian immigrants’ diabetes and why such a high diabetes disparity is observed among Asian immigrant groups.

My research is in the area of frames, or overcomplete systems.  A frame will allow you to represent data with a kind of structured redundancy, to provide resilience to erasures or noise while still providing stable reconstruction.  There are applications of frame theory in signal processing, imaging, sparsity, compressed sensing, and a variety of other data-related fields. Most recently, I have worked in a new area called dynamical sampling.  The idea is to use samples from a system that evolves in a way that can be modeled, so that samples taken at time increments, as well as different positions, add to the knowledge of the original signal. This could be useful in cases where sensors are sparse and/or expensive.  If you have a problem like that, I'd love to talk to you!

I use panel data econometric techniques to research strategic behavior of bidders in auctions. I analyse the public procurement process, contract renegotiation and market efficiency. I also study network dynamics using panel data analysis with applications in industrial organization and emphasis in auctions, procurement contracting and the airline industry. My teaching interests include Econometrics and Industrial Organization.

I am a specialist in topological data analysis. I develop new methods in this field and apply them to problems ranging from material science and fluid dynamics to atmospheric science and biology.

My research broadly falls under computational social science and digital humanities. I primarily focus on using computational methods (such as NLP, text mining, machine learning) to provide better solutions for information retrieval and organization of digital libraries. I teach one of the core MLIS courses (LIS 5043 - Organization of Information) which is heavily based on the management and organization of data. It covers the basic concepts of information organization and management; information storage and retrieval systems; design and structure of information systems; identification and organization of knowledge resources; and knowledge organization methods.

Dr. Larson leads the Sport, Health, and Exercise Data Analytics laboratory, which is an interdisciplinary data analytics hub created to serve the general research areas of health promotion, exercise physiology, and sport business. Research projects taking place in his laboratory range from analysis of physical activity, training, and physical performance metrics, to analyzing neurological measurements of sports product consumers.

The laboratory is currently involved in, and open to, research investigations from across the entire spectrum of the production and exchange processes of elite and professional sport, as well as the empirical analyses of exercise and health behaviors. Students participating in laboratory research are competitively selected for the ability, experience, and/or potential capability of becoming a top data analyst in their respective fields.

Dr. Mark Laufersweiler has always had a strong interest in computers, computing, data and data visualization. Mark started at OU as the Computer Systems Coordinator for the School of Meteorology from 1999-2013. Part of his duties included managing the real time data feed and maintaining the departmental data archive. He assisted the faculty, students, and staff in their courses to help promote the computing and data skills needed for the classroom and instruction based on current best practices regarding research data management and code development. Since the Fall of 2013, he has served as the Research Data Specialist for the OU Libraries.

Mark supports the educational mission of the OU Libraries by developing and offering workshops, seminars and short courses, helping to inform the university community on data management plans and better practices for research data management. He is the university's representative to ORCID and the Software and Data Carpentry Foundation and is an active Carpentry instructor and trainer. Currently, he is activities include OU joining as an institutional member with the Open Science Framework (OSF) tool, hosted by the Center for Open Science and organizing activities for the Data Analytics, Visualization, and Informatics Syndicate (DAVIS) located in Bizzell Memorial Library.

My current research involves development and implementation of SimBAL, a novel spectral synthesis method to analyze Broad Absorption Line Quasar (BALQ) spectra.

At SLIS, I teach information visualization, data analytics, and evaluating interactive information systems with users. My courses include modules that allow students to learn and practice data analysis, visualization, and interpretation skills.

I am an archaeologist that uses computer and statistical methods to study the past, including analyses of movement in the past using geographic data, digital image analysis of archaeological specimens for classification, and analyses of datasets large and small. I regularly teach ANTH 5083, the graduate level statistics class in Anthropology which covers basics and emphasizes Exploratory and Multivariate applied analyses that are frequently of use the small and flawed data sets that anthropologists frequently work with; and ANTH 5593, a graduate level class which covers the use of GIS and spatial data analysis.

• Applied Empirical methods
• Causal inference
• Large-scale modeling of natural phenomena

• Near-Field Cosmology
• Galaxies
• Star Formation
• Stellar IMF
• Big Data

• Research Computing

I teach Econ 2843- Business Statistics. I also have research in the areas of public policy that effect low and middle income households. I have used advanced econometrics and regression analysis. I also use ArcGIS for spatial analysis and to capture spatial information that I can use in STATA for regression analysis. I have papers over how Universal Free Breakfast effects test scores and conflict in schools. How fracking has effected home prices, and how flat rate tuition effects student enrollment and GPA's.

My interest is in Topological Data Analysis (TDA) which is an application of Algebraic Topology. A standard set-up is: Given a data cloud, a parametrized family (usually 1 real parameter) of topological spaces (simplicial or cell complexes) are constructed based on proximity restraints of the data points and the variation of their topological invariants (usually homology) with respect to the parameter is studied (using tools like persistent homology). This provides some insight into the deeper structure of the data, the most basic case being clustering properties. Currently with my PhD student Wenwen Li we are working on multi-persistence (several parameters) with applications to robotics.

• Migration
• Social Networks
• Inter-Generational Relations
• Household Demography
• Quantitative Research Methods

The focus of my laboratory is determining the mechanisms of CRISPR-Cas systems, the bacterial and archaeal adaptive immune system. These RNA-protein complexes have been repurposed as powerful gene-editing tools. There are two aspects of data-related research in my laboratory.

1. Bioinformatics analysis of sequences of protein and nucleic acid components of the CRISPR-Cas systems across the microbial world; this gives us information on evolutionary relations and conservations, which leads to experimental approaches testing bioinformatics-derived hypotheses.
2. Deep-sequencing approaches (DNA-seq) to determine sequences promoting promiscuous activities of Cas proteins; this enables us to perform protein engineering to develop Cas variants that are devoid of promiscuous activities.

I primarily focus on risk and public policy, with an emphasis on social and behavioral responses to information about risk. My data-related research involves a combination of social data (e.g., large surveys) and physical data (e.g., weather/climate data). I teach intro and intermediate analysis of political data (PSC 5923 & PSC 5933), data visualization (PSC 5923), and other special topics, such as survey research and interactive data visualization.

I am an historian of nineteenth and twentieth century West Africa (religious, legal, cultural, and social, women and gender history).

I teach a statistics course for Graduate students in Biology.

I am a quantitative methodologist. My research program focuses on developing advanced statistical modeling techniques that can be useful to address issues common in behavioral, cognitive, social, and health-related data. I particularly focus on the methodological aspects of Bayesian statistics, network psychometrics, and ecological momentary assessment data. More recently, I collaborate with health domain researchers, where we use digital mobile technology coupled with statistical modeling to address health-related issues and promote healthy living. I also collaborate with engineering researchers to assess the decision-making and decision-choice processes that will inform intervention.

• Risk Perception
• Environmental Politics and Policy
• Science and Technology Policy
• Climate, Weather, and Social Science
• Contingent Valuation Methodology
• Policy Analysis
• Cost Benefit Analysis
• Risk Analysis and Assessment

My current research interests lie in three areas:

1. Statistical techniques for analysis of change in panel data and dynamics in intensive longitudinal data
2. Measurement equivalence/bias across groups and occasions
3. Applications of quantitative methods in health, social, and behavioral research

•  Statistics and R
• Bayesian Statistics
• Teaching Bayesian Statistics

I perform interdisciplinary and data-driven research with emphasis on computation, modeling and analysis of differential equations. I have been focusing on the study of numerical solutions and mathematical modeling of the PDEs describing various applications, and how the computational and analytical results can be interpreted to answer problem-specific questions.

I use large datasets from satellites to measure and monitor the Planet Earth, specifically, land use and land cover change, agriculture, forestry, surface water resources, and atmospheric composition. My teaching interest includes remote sensing and biogeochemical models.

Research on data-driven decision-making (DDD) and scientific learning in entrepreneurship, using field experiments and natural experiments.