Resident Faculty

Strategic faculty hiring across and within colleges in foundational and applications areas of data science, complementing current strengths of 100+ faculty.

Augmented Reality and Immersive Analytics, Embodied Cognition, Multimdal Machine Learning

Roghayeh (Leila) Barmaki is an Assistant Professor at the Computer and Information ‎Sciences Department and affiliated with the Data Science Institute at the University of ‎Delaware. Dr. Barmaki leads the Human-Computer Interaction Lab at the University (https://sites.udel.edu/hci-lab/).

Her research interests span Multimodal Data Analytics, Human-Computer Interaction, Virtual and Augmented Realities with applications in Education and Healthcare.

health, biomedical, artificial intelligence

Rahmat Beheshti is an assistant professor in the Data Science Institute and also the Department of Computer & Information Sciences at the University of Delaware. He has a unique interdisciplinary background by finishing his postdoctoral training in Public Health and his PhD in Computer Science and MSc in AI. He has been working in the area of Health Data Science and Biomedical Informatics for the past eight years. Specifically, he has worked extensively on two major public health epidemics: smoking and obesity, and has focused on very different aspects of these two, including the social, economic, environmental, and lately biological factors that affect these epidemics.

Astronomy Supernovae Energy Time-domain

I am a data-driven scientist working on multi-disciplinary and inter-disciplinary problems. My specialty are lightcurves, time series of light, in astronomy, with applications in stellar evolution, cosmology, and solar system science, and in the urban environment, where the study of urban lightcurves enables sociological, ecological, economic inference.

I study astrophysical transients, particularly Supernovae, exploding stars, trying to understand the progenitors of explosions from the explosion signature.

I am the Large Synoptic Survey Telescope (LSST) Science Collaborations Coordinator: in this role I facilitate the work of the international science community in preparing to the advent of the LSST revolutionary survey, which will take a movie of the entire southern hemisphere sky every three nights down to 24th magnitude depth, delivering tens of Tb of data per night.

Austin J. Brockmeier is an assistant professor in the Department of Electrical and Computer Engineering and the Department of Computer and Information Sciences, and is a resident faculty of the DSI. His research interests center on designing algorithms and models for gaining insights into complex data sets, with applications focused on biomedical signals and text mining. He has worked on machine learning methods to search and organize large collections of scientific references for evidence-based research. He has also worked on new approaches for analyzing brain waves and neural recordings for brain-machine interfaces.

He received a BS in computer engineering from the University of Nebraska–Lincoln in 2009 and a Ph.D. in electrical and computer engineering from the University of Florida in 2014. He then worked as a postdoctoral researcher at the University of Liverpool and the University of Manchester. He is a member of the IEEE and IEEE Engineering in Medicine and Biology Society.

food systems; sustainability; global environmental change; geospatial data science; nutrition; foreign land investments; human migration

Dr. Kyle Davis is an Assistant Professor in the departments of Geography & Spatial Sciences and Plant & Soil Sciences whose work focuses on food systems, agricultural sustainability, and global environmental change. His current research in India, Nigeria, China, & the US combines environmental, economic, and social considerations with direct stakeholder engagement to inform agricultural decision-making and to improve nutrition, environmental sustainability, & climate adaptation strategies. He also explores other human-environment interactions through projects on: the environmental and livelihoods impacts of large-scale land investments; variability & shock propagation through food trade networks; human migration modelling as driven by anticipated climate change impacts; and farmer coping strategies for climate variability and extremes.

Urban Science; Data Science; Complex Systems

Dr. Dobler is currently the Director of the “Urban Observatory” (UO; cuspuo.org), a multi-institutional facility designed to study complex urban systems through remote imaging. His expertise is in image analysis, computer vision, time series, statistical analysis, and mathematical modeling of large data sets. As the Director of the UO, he applies data analysis techniques from astronomy, computer vision, and machine learning to images of urban skylines to study air quality, energy consumption, lighting technology, public health, and sustainability. In addition, he has led data analysis projects related to equitable distribution of greenspaces, mapping long timescale economic trends across cities, and surrogate measures for traffic safety. Prior to his work on urban systems, Dr. Dobler was an astrophysicist specializing in multi-wavelength, full sky data sets from radio to gamma-ray energies, and led the discovery of one of the largest structures in the Milky Way.

Geospatial Data Science, Machine Learning, Human Dimensions of Global Change, Sustainability

Jing Gao is an Associate Professor in the Department of Geography and the Data Science Institute at UD. Her research investigates large-scale human-environment interactions, especially the relationship between global urban land use, population, and climate change. Trained in Geography and Computer Science, she approaches interdisciplinary scientific inquiries by integrating diverse data, quantitative and computational methods from spatial statistics, machine learning, big data mining, geo-visualization, and remote sensing, with narrative-based scenario analyses of societal development. Her research is generating new insights and datasets on global, long-term, spatially-explicit changes in urbanization and population characteristics, extending the SSP-RCP scenario framework used by the IPCC and the research community for understanding global environmental change impacts, spearheading creative data-science practices in long-term spatially-explicit modeling of socioeconomic processes, and developing new methods for evaluating the uncertainty and the success of such practice.

Low-rank matrix and tensor methods, heterogeneous and messy data, big data, statistical machine learning, imaging and inverse problems

David Hong is currently an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. Previously, he was an NSF Postdoctoral Research Fellow in the Department of Statistics and Data Science at the University of Pennsylvania. He completed his PhD in the Department of Electrical Engineering and Computer Science at the University of Michigan, where he was an NSF Graduate Research Fellow. He also spent a summer as a Data Science Graduate Intern at Sandia National Labs.

Longitudinal Modeling, Time Series, Psychometrics, Multilevel and Structural Equation Modeling

Dr. McCormick is an Assistant Professor in the School of Education, focusing on longitudinal and psychometric modeling. He obtained his PhD from the University of North Carolina and worked previously as an assistant professor of Methodology & Statistics at Leiden University, NL. Dr. McCormick’s research is primarily concerned with the measurement and modeling of short-term dynamics and long-term growth in behavior and cognition across the lifespan through the development of longitudinal and time series models. Current projects include work with large-scale, intensive education data to hybridize short- and long-term longitudinal models, new approaches for estimating nonlinear random effects models, and methods for predicting downstream consequences of change over time.

Remote sensing; GIS; Agriculture; Forest; Climate

Dr. Pinki Mondal is an interdisciplinary geospatial data scientist interested in the dynamics of coupled natural and human systems. She has a PhD in Land Change Science from the University of Florida with research focus on environmental remote sensing and Geographic Information Systems (GIS). Her research projects in India, Viet Nam, eastern USA, and several West African countries have revolved around three themes: (a) agricultural sensitivity to climate variability, (b) adaptation strategies in smallholder agricultural systems, and (c) effects of national-level policies on forestry and conservation. Currently, her research focuses on documenting climate change impacts and adaptation in the low- and middle-income countries. Prior to joining UD, she was a Senior Research Associate at Columbia University in the City of New York. She has taught several GIS-focused courses (both at undergraduate and graduate level) at institutions including Columbia University, the City University of New York, and the University of Florida.

Deep Learning, Transfer Learning, Explainable AI, Human-centered AI

Dr. Xi Peng works in the area of Machine Learning, Deep Learning, and Computer Vision. His research focuses on structure and model-oriented deep learning. The goal is to develop frontier AI systems that are not only robust to unknown but also explainable for human.

Currently, he is making efforts to cross-disciplinary data analytics including workspace safety enhancement (biomechanics), biomarker based pain prediction (biochemistry), and multimodal human behavior analysis (psychology/linguistics).

He received the Ph.D. degree in Computer Science from Rutgers University in 2018. He was a research intern at NEC Labs America in 2016, a research intern at IBM T.J. Watson Research Center in 2015, and a full-time engineer at Baidu Research in 2011.

Cencheng Shen received the BS degree in Quantitative Finance from National University of Singapore in 2010, and the PhD degree in Applied Mathematics and Statistics from Johns Hopkins University in 2015. He worked as a visiting assistant professor in the Department of Statistical Science at Temple University from 2015 to 2016, re-joined Johns Hopkins University as an assistant research scientist in The Center for Imaging Science from 2016 to 2018, and is currently an associate professor in the Department of Applied Economics and Statistics at University of Delaware. His research has been funded by NSF DMS, DARPA SIMPLEX, and DARPA L2M.

Psychometrics, Measurement, Student Growth, Structural Equation Modeling

Dr. Sanford R. Student is an assistant professor specializing in measurement, psychometrics and quantitative methods in the School of Education at the University of Delaware. His research spans a variety of areas, united by his interest in connecting technical aspects of educational and psychological measurement such as instrument design and psychometric modeling with their practical implications for students, teachers, and downstream researchers. Dr. Student’s recent methodological research deals primarily with issues related to the measurement of students’ academic growth, including the psychometric properties of assessments designed to measure growth and the implications for research and practice when those properties differ across assessments. He also conducts both methodological and applied research in large-scale science assessment, and has worked with states, districts and assessment developers on a variety of applied projects.

High dimensional statistics, network data analysis, bayesian statistics

Dr. Zhao is an assistant professor in the Department of Applied Economics and Statistics at the University of Delaware. Previously, he worked as a postdoc researcher in the Department of Statistics, Texas A&M University. He obtained his Ph.D. degree in statistics from Florida State University. Dr. Zhao’s primary research interests revolve around developing novel statistical methodologies and real applications in areas like high-dimensional estimation and inference, statistical learning for network data, variational inference and nonconvex optimization.