People

Tom Zylkin is an international economist who specializes in the study of trade agreements and other determinants of international exchange. Much of his research focuses on issues of measurement and inference that arise in modern econometric models of trade. A particular methodological challenge that Tom's research engages with is the interconnected nature of the global trade network, whereby reduced barriers to trade between any one pair of countries affects all trade flows throughout the entire network. Correctly assessing the economic effects of a given trade policy change from the trade data requires using high-dimensional models that account for these network effects, an area in which Tom has made several computational and methodological contributions. In more recent work, Tom is working with a team of researchers at the World Bank and at the University of Surrey to combine machine learning techniques with general equilibrium trade modelling. The goal is to learn which types of trade agreement provisions are most beneficial for increasing bilateral trade.

The Yang Lab uses a mix of genomic and computational tools to study evolutionary genetics, with a particular emphasis on human prehistory. In particular, we apply population genetic tools to genome-wide data from ancient and present-day humans. We also work with the Genomics Education Partnership, with whom we are researching the evolutionary genetics of Drosophila species using online genomic databases.

Dr. Carrie Wu is an evolutionary ecologist who studies how plant populations respond to a swiftly changing world.  Her research group uses the tools of field ecology, quantitative genetics, genomics, and spatial analysis to understand how genetic and environmental variation translates into patterns of phenotypic diversity, adaptation, population dynamics, and species distributions. Current efforts in the lab focus on invasive species that are colonizing new locations, as well as native montane species responding to environmental change.   

As a digital humanities scholar, my research focuses on how computational methods can facilitate humanities inquiry. I develop methods and theories for how computer vision can facilitate the large scale study of images, with a focus on American film and photography.

Using data analytics to evaluate and manage risk for large-scale operations. Examples of applications include cyber-infrastructures and the movement of goods & services.

I’m interested in landscape-level human-environment interactions. My work focuses on using remotely-sensed data analyses, climate modelling, GIS, and spatial statistics to understand what drives our decisions to change land cover and land use, and what the environmental consequences of those changes are.

Some of Prof. Singal’s research involves analysis of large astrophysical datasets. His group utilizes both novel statistical techniques and machine learning methods. Currently these efforts are focused in the areas of studies of the cosmological evolution of supermassive black hole systems and empirical approaches to photometric redshift estimation for future large-scale galaxy surveys.

Mapping the Foundations explores the global grant making practices of large US philanthropies, from World War II through the present. By bringing together GIS methods, quantitative data (building a database of tens of thousands of international grants from the Ford and Rockefeller foundations), and qualitative archival research (historical records of individual grants), the project aims to produce digital maps and visualizations that will yield new insights about the global impact of US philanthropy during the twentieth century and beyond. Research team: Nicole Sackley (History and American Studies), Tim Barney (Rhetoric & Communication Studies), Justin Madron (Digital Scholarship Lab) and student summer research fellows since 2018.

Projects in Jonathan’s lab use both genomic data and spatial modeling of animal movement, gene flow and habitat suitability. This includes bioinformatic pipelines, spatial statistics, and GIS models of urban landscapes.

My research interests primarily lie in natural language processing and argumentation. Recent projects focus on extracting arguments from user generated text, such as tweets and product reviews. The automatically extracted arguments can then be used to summarize the data and to infer characteristics of the data, such as the perceived level of helpfulness.

The Parish lab is a computational chemistry lab with projects ranging from molecular dynamics simulation of host-guest systems, to using highly correlated multireference quantum methods to study molecular excited states. Most recently, we have turned to developing machine learned algorithms for predicting molecular properties from quantum data.

In the Nolin lab, we utilize data analytical tools to determine for trends in the origin, structure and expression of biologically-active natural products, chemical compounds made by plants, animals, fungi, and bacterial. As a number of these compounds have been shown to have medicinal properties, we are working to streamline the discovery process.

Dr. Mew has developed unique programs for upskilling and reskilling the current workforce in data analytics competencies. The results are a new bachelor’s degree (BSPS) and certificate of applied studies (CAS) in data analytics offered by the University of Richmond’s School of Professional and Continuing Studies (SPCS). He has also implemented a data analytics digital credential in partnership with the CAPITAL CoLab under a grant from the National Science Foundation. His research interests are in information systems pedagogy and online social networking.

Prof. Mehkari's research lies in the areas of fiscal policy and real business cycle (RBC) theory. His most recent work aims to explore the effects of the American Recovery and Reinvestment Act (ARRA) of 2009 on various economic aggregates such as employment, consumption, and schooling expenditures. As part of his research he works with complex computational models and large datasets. For example, to understand how consumption varies in response to fiscal policy his research uses the Nielsen RetailScan dataset that contains weekly sales of millions of fast-moving UPCs at thousands of retail stores.

Dr. McGraw’s research and teaching involves time-series analysis and visualization using music performance data and spatial analysis using social and sound datasets.

The serious challenges faced by college students during COVID-19 have been reported anecdotally, but there is limited scientific evidence about the pandemic’s effects on college students’ academic, interpersonal, and psychological well-being. My research team is investigating a model of risk and protective factors for college students’ adjustment across three time points within the 2020-21 academic year. Specifically, we are evaluating contextual risk factors, such as financial strain and caring for sick family members, and whether contextual risk is more pronounced for minoritized students during COVID-19. We are also examining whether campus supports and resources (e.g., close relationships with faculty, access to mental health care) mitigate risk for adjustment difficulties during the pandemic. College students (N=522) from two, residential liberal arts colleges participated in the first wave of data collection in the fall and will be invited to participate in two waves of data collection in the spring. We hope that findings contribute to our understanding of the extent to which COVID-19 has affected students’ experiences and outcomes, and importantly, provide data to illuminate effective avenues for intervention — especially ones that might be effective for addressing the inequities this health crisis has highlighted.

The Johnson research group uses computational methods to supplement its study of catalysts. Researchers synthesize catalysts and model them using Density Functional Theory (DFT). Students can gain insight into the steric and electronic properties of compounds in a way that would be challenging or impossible by experimental methods alone.

Dr. Hilliker studies how cells use our genetic recipes (mRNAs), which contain the instructions to make the proteins required for cellular function. She uses high throughput RNA and DNA sequencing to scale up from looking at one mRNA at a time to looking at the entire transcriptome (i.e. all of the mRNAs in the cell) to better understand how cell’s control protein production.

Dr. Kristine Grayson promotes the incorporation of quantitative literacy in undergraduate biology education as a member of the HHMI BioInteractive Ambassador Community. She has served as a facilitator with the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project and co-organizes a Research Coordination Network on sharing data-centric teaching resources. Her research examines the spread of invasive forest insects and population dynamics of amphibians and reptiles.

The Erickson lab uses a combination of lab experiments, fieldwork, and genomics to study the evolution of invasive insects. Currently, we are using next-generation DNA sequencing to track genetic changes in invasive populations across seasons and years and to compare the genetic makeup of populations found in different habitats along the East Coast.

In the Donald research group, we employ state-of-the-art computationally demanding methods to model and investigate the structure, chemical bonding, and reactivity of chemical systems involving elements from across the periodic table. Areas of particular interest for us include the nature of weak bonding interactions, and structure prediction for crystal structures. With modern progress in machine learning and artificial intelligence, computational tools are becoming crucial for chemical insights.

At the Dias Lab, we combine computational and experimental methods to fully characterize, understand, and predict the optical behavior of materials at the nanoscale. The use of nanostructures with different chemical compositions is a promising route to improve the performance of optoelectronic devices such as solar cells, sensors, and catalysts. We use a novel approach combining Density Function Theory (DFT) and Machine Learning (ML) for composition screening, presenting a new pathway to accelerate the discovery of new materials. A detailed roadmap for material testing and device evaluation is also performed using the Finite-Difference Time-Domain (FDTD) method.

I work with genetic and genomic data to understand evolutionary history and biodiversity of anurans.

Contemporary Slavery in Armed Conflicts (CSAC) illustrates some of Monti's recent quantitative research, in partnership with Kevin Bales at the University of Nottingham. The CSAC database is the first systematic and large-scale inquiry into various types of enslavement within modern armed conflicts. It measures reports of conflict-related enslavement committed by armed actors during the years 1989-2016. It includes every case and type of slavery across 171 wars and conflicts fought between those dates. We find Slavery and human trafficking are present in 90 percent of modern wars. We also find the most common type of enslavement in war zones was the use of child soldiers, occurring in 87 per cent of armed conflicts, with child soldiers more likely to be used by Side B. Enslaved children were found in 252 disputes over territory and 221 disputes over governmental issues. When Side A and Side B both enslaved children, 190 instances were over territory, and 282 were due to governmental disputes.

Chris Cotropia, Dennis I. Belcher Professor of Law and Director of the Law School’s Intellectual Property Institute, uses data analysis in empirical legal studies regarding intellectual property, the judiciary, and other legal issues. He is also part of the SCALES – OKN (Systematic Content Analysis of Litigation EventS Open Knowledge Network) team composed of computer and data scientists, legal scholars, journalists and policy experts developing a suite of tools to enable access to court records and analytics, which was recently awarded a Phase II grant as part of the NSF Convergence Accelerator. 

My research group develops theoretical and computational methods for testing theories of the early Universe. For example, we have developed new analysis techniques for extracting the maximum possible amount of information from the large data sets produced by telescopes that map the cosmic microwave background radiation (the oldest light in the Universe). We are also currently working on a novel method for simulating the complex dynamics of the fields thought to have caused rapid “inflationary” expansion immediately after the Big Bang; such simulations allow us to make the specific predictions needed to test inflationary theories. 

Dr. Bukach is a Cognitive Neuroscientist who explores the factors that influence the way we categorize objects and faces, and what happens when expertise for these categories fails to generalize (e.g., racial bias in face recognition).  She uses cognitive electrophysiology (a direct method of measuring brain activity through electrodes on the scalp) to link brain processing with behavior. She has established a large database of classic electrophysiology paradigms and individual difference measures to engage undergraduates in authentic research experiences through an NSF-funded project (https://pursue.richmond.edu/).

Dr. Jennifer Bowie studies judicial decision making and behavior on courts in federal, state, and comparative courts. She empirically analyzes judicial opinions in order to better understand what motivates judges to decide the cases the way they do. Her more recent research, funded by the National Science Foundation, develops and advances a new and innovative dataset on the dynamic transmission of information between the courts of appeals and Supreme Court across the judiciaries of Canada, the United States, and the United Kingdom. The project represents the first comprehensive assessment of hierarchical influences on judicial decision making within a comparative context.

I study large cultural datasets in order to address new and existing research questions in the humanities and social sciences. I specialize in the application of statistical computing to large text and image corpora.

Dr. Abrash’s group studies the structure and dynamics of model systems for atmospheric fine particulate matter (PM 2.5). Fine PM is 10 times as deadly as ozone and still exceeds EPA and WHO maxima in large numbers of communities. Our model system consists of common atmospheric pollutants and 1-12 water molecules, and includes primary and secondary ozonolysis reactants and products, which we study using the Gaussian suite of programs and the University of Richmond’s Cluster program.

Beth Zizzamia is the GIS Operations Manager for the Spatial Analysis Lab in Room 300 of the International building. Mrs. Zizzamia maintains the physical and virtual infrastructure of the lab, while providing technical GIS training and support to the UR community. Her areas of expertise include GIS instruction, tabular and spatial database management, vector analysis, field collection protocols and cartographic production. Her research background is diverse and includes structural geology, hydrology, invasive species management, carbon sequestration and habitat reclamation.

Carol’s research focuses on text and data mining (TDM) as a tool for advancing research in the humanities. Her current project focuses on how TDM techniques can help explore journal literature in library science and writing studies as a way to historicize the relationship between academic library instruction and writing studies related to academic and library literacies.

Samantha is a social sciences librarian at Boatwright Library with specialties in data services and government information. She works with faculty across disciplines to support data use and quantitative literacy, including assistance with finding data for reuse and archiving and preserving data. She co-teaches a first-year seminar called A Generation of Cynics: Bias, Neutrality, and the Internet and her research interests include library services design and evaluation, data pedagogy, and workforce issues in Library & Information Science.

George's current areas of research interest are Natural Language Processing in English and Spanish, synthetic text generation, simulations, and data analysis using Python and pandas.