Simply put, data science is the extraction of knowledge from data. While the act of producing insights from information has figured prominently in disciplines ranging from statistics to sociology, the recent proliferation of data across a broad spectrum of applications has caused an exciting new field to emerge at the intersection of several traditional ones.
At Mount Holyoke, faculty have been exploring ways to connect the liberal arts to this developing field—both in curricular terms, so that students can more easily locate opportunities to build competencies in data science, and in research terms, so that faculty can work together in using data science to innovate in their respective fields.
The work is an exercise in integration, one that brings together technical skills and domain-specific knowledge to create new pathways for both teaching and learning.
Interested in learning more? Email us at email@example.com.
|Name||Data Science Focus||Affiliations|
|Mara Breen||Breen explores the cognitive processes involved in speech production and comprehension with a focus on how speakers and listeners use prosody, which describes musical aspects of speech-like pitch, phrasing, and rhythm, to understand language in real time. Much of her work requires analysis of large data sets, including brain waves, acoustic measures, and content analysis.||Departments of Psychology and Education and Neuroscience and Behavior|
|Tim Chumley||Chumley is a mathematician working on problems in the foundations of statistical physics and related probabilistic models. His work on billiard models and other mechanical systems combines elements of probability, geometry, and analysis, as well as stochastic simulation of complex systems. He is interested in Markov chains, stochastic differential equations, and other stochastic processes, as well as their applications in the sciences.||Department of Mathematics and Statistics|
|Amber Douglas||Douglas has a background in clinical psychology and is interested in data analysis and statistical models for understanding predictors of mental-health outcomes. Her work focuses on the subfields of traumatic stress studies and ethnic minority psychology, using new and existing data sets to identify pathways and trajectories associated with these outcomes. She utilizes various quantitative analytic techniques including structural equation, hierarchical linear, and growth curve modeling in her research.||Departments of Psychology and Education and Gender Studies, Program in Africana Studies|
|Andrea Foulkes||Foulkes is a statistician who develops methods for characterizing the relationships among high-dimensional molecular and cellular data and measures of disease progression at the intersection of cardiovascular and HIV research. Her methods draw from cluster analysis, recursive partitioning, mixed effects modeling, and Markov modeling and have helped to identify and develop appropriate techniques for analyzing genetic and down-stream biomarker associations with clinical outcomes.||Department of Mathematics and Statistics|
|Janice Gifford||Gifford works with statistical models, known as item-response models, which are used primarily in the large-scale educational testing setting. In addition, her work involves providing statistical support for the research of students and faculty working in a variety of disciplines.||Department of Mathematics and Statistics|
|Gary Gillis||Gillis studies the biomechanics and neuromuscular control of animal locomotion. The recent focus in his lab has been on studying the control of rapid decelerations using toad landing as a model system. Extracting data from high-speed motion capture/video requires interpreting large quantities of data from small sample sizes and exposes his students to many data visualization techniques.||Department of Biological Sciences|
|KC Haydon||Haydon examines the developmental origins of how people behave in their closest relationships. Haydon collects a wide array of cross-sectional and longitudinal data including behavioral and physiological measures. Her research is guided by the premise that relationship and developmental outcomes are probabilistically shaped by experiences in multiple contexts. She uses analytic methods that capture dyadic interactions of participants including mixed effect, hierarchical linear, growth curve, and structural equation modeling techniques.||Department of Psychology and Education|
|Martha Hoopes||Hoopes combines experimental and modeling approaches to answer ecological questions. Much of her research has examined spatial community dynamics and the impacts of invasive species. Her empirical work often requires mixed effects and time series models, and her theoretical work uses differential equations and integrodifference models.||Departments of Biological Sciences and Environmental Studies|
|Barbara Lerner||Lerner works on the provenance of scientific data, which includes history about data processing from its point of collection to its point of dissemination. Provenance can affect the quality, reliability, and interpretation of data and analyses. By focusing on ways to automate the collection of data processing, she is working to make provenance information accessible to scientists using visualization and query technology.||Department of Computer Science|
|Tim Malacarne||Malacarne's work uses network analytic techniques to examine educational decisions. In particular, he looks at the structural characteristics within schools that lead to friendships between students from different groups (racial or socioeconomic) and the networks of meanings that lead to students' educational and career choices.||Department of Sociology|
|Eitan Mendelowitz||Mendelowitz is a computer scientist and media artist whose work combines the interpretation of realtime sensor data with data-driven visualization. He creates media art, realtime-media for performance, and public art installations and engages in computer science research to support the production of physically interactive media art. Mendelowitz is also excited to be working on the Global Proverbs Project, an aesthetically motivated digital humanities research initiative.||Department of Computer Science|
|Heather Pon-Barry||Pon-Barry's research on spoken-language processing and human-robot communication combines elements of artificial intelligence, computational linguistics, signal processing, and cognitive science. She develops technologies that analyze acoustics and intonation to augment traditional speech recognition—essentially teaching computers to move beyond just the words a person says and incorporate other signals.||Department of Computer Science|
|Andrew Reiter||Reiter studies conflict resolution and post-conflict reconstruction, particularly the use of transitional justice mechanisms, such as trials, truth commissions, reparations, and amnesty laws. He often examines these issues globally and has extensive experience designing coding schema and implementing data collection processes to turn qualitative aspects of the political world into quantitative data.||Departments of Politics and International Relations|
|Michael Robinson||Robinson is an applied econometrician who uses data to answer questions such as why economists study some countries more than others, what is the impact of SAT-optional admissions, and how much liberal arts college faculty publish. The application of his research has local relevance as it has helped the College evaluate and formulate financial aid, admission, and tuition policies.||Department of Economics, Nexus in Global Business, Entrepreneurship, Organizations, and Society|
|Steven Schmeiser||Schmeiser applies game theory to strategic voting, group formation and effort, the regulation of consumer information, advertising within industries, online advertising, and internet economics. He works with data sets on topics such as corporate board memberships and internet advertising networks. While working in these areas, he has become particularly interested in documentation, automation, and repeatability of data analysis.||Department of Economics, Nexus in Global Business, Entrepreneurship, Organizations, and Society|
|Dan Sheldon||Sheldon uses probabilistic modeling and statistical algorithm development to answer questions in ecology and conservation, such as using data from citizen scientists to fit models of bird migration and insect phenology. Much of his work models, analyzes, or optimizes processes that take place on networks.||Department of Computer Science|
|Dylan Shepardson||Shepardson is an applied mathematician who applies optimization theory and linear programming to a variety of questions from voting theory to computational chemistry to infectious disease dynamics. Recent research links social/epidemiological contact networks to the movement of infectious diseases through human populations.||Department of Mathematics and Statistics|
|Jessica Sidman||Sidman is a mathematician who studies combinatorial algebraic geometry, computational commutative algebra, and rigidity theory. She is also interested in algebraic statistics and connections between algebraic geometry and computer vision and machine learning.||Department of Mathematics and Statistics|
|Kate Singer||Singer works primarily on eighteenth- and nineteenth-century texts, most often on those by women writers from the Romantic period in Britain (roughly 1789–1832). In some of her work, she uses text encoding and quantitative analysis, which has led her to consider notions of virtuality, media, and embodiment as she experiments with more data-related methodologies of reading literary texts.||Department of English|
|Eleanor Townsley||Townsley is a cultural sociologist who studies data analysis, research methods, and the scientists who use them. She has an emerging interest in big data and communication in social and other mass media.||Department of Sociology, Nexus in Journalism, Media, and Public Discourse|
|Sam Tuttle||Tuttle is a hydrologist who examines interrelationships between hydrological, atmospheric, and land surface processes, especially at large scales using satellite remote sensing. His research mainly consists of statistical analyses of observational data, modeling, and simulation studies, often using causal identification techniques, generalized statistics, and stochastic methods to account for the nonlinear and coupled nature of natural systems.||Departments of Geology and Geography|