Learn about our instructors for Data Matters Summer 2024.
Andy Bowman is an Assistant Professor of Management Information Systems at East Carolina University. His research interests include information security behaviors, digital piracy, and human-computer interactions. As a professor he focuses on teaching the innate potential of information systems to improve organizational processes and supplement our ever-growing digital world. He also teaches students to think logically, design databases, and how to create effective programs to analyze data.
Trent Buskirk
Old Dominion University
Teaching: Introduction to Machine Learning and Big Data for Social Science Research
Trent D. Buskirk, Ph.D. has recently joined the new School of Data Science at Old Dominion University as one of several founding faculty members. Prior to this appointment, Trent was the Novak Family Distinguished Professor of Data Science and outgoing Chair of the Applied Statistics and Operations Research Department at Bowling Green State University. Dr. Buskirk is a Fellow of the American Statistical Association and his research interests include big data quality, recruitment methods through social media, the use of big data and machine learning methods for health, social and survey science design and analysis, mobile and smartphone survey designs and in methods for calibrating and weighting nonprobability samples and fairness in AI models and interpretable ML methods. Trent has also been involved in various professional organizations serving as the President of the Midwest Association for Public Opinion Research in 2016, the Conference Chair for AAPOR in 2018 and as part of the scientific committee for the BigSurv series of conferences which began in 2018. Trent has also serveed as an Associate Editor for Methods for the Journal of Survey Statistics and Methodology. When Trent is not geeking out over data science, big data or survey methodology, you can find him playing a competitive game of Pickleball!
Jonathan Duggins
NC State University
Teaching: Introduction to Programming in R, Exploratory Data Analysis Using R Markdown
Jonathan Duggins is an award winning Associate Teaching Professor and Coordinator of the Undergraduate Professional Partnership Program in the Department of Statistics at North Carolina State University. He has worked in industry as a biostatistician and has taught at multiple universities. Jonathan has always been dedicated to education and that is his main role at NCSU where he teaches undergraduate and graduate courses with an emphasis on statistical programming languages in both face-to-face and distance education courses.
Siobahn is the first woman computer science Ph.D. graduate from North Carolina Agricultural and Technical State University (2018). She is an Assistant Professor and Program Director of Information Science/Systems in the School of Library and Information Sciences at North Carolina Central University, Lab Director for the Laboratory for Artificial Intelligence and Equity Research (LAIER), Co-Director for the Center fOr Data Equity (CODE), an AAAS IF/THEN ambassador, and an Office e-Learning faculty fellow at North Carolina Central University. Her research focuses on utilizing machine learning to identify sources of misinformation on social media and on improving fault detection in autonomous vehicles.
Dr. Grady advocates increasing the number of women and minorities in computer science. She believes that “the STEM workforce has both gender disparities and that of historically disenfranchised groups. As an AAAS IF/THEN ambassador, she affects change by examining girls’ perceptions, attitudes, and behaviors, helping them gain confidence in curating and developing a STEM identity.”
Yufeng Liu
University of North Carolina, Chapel Hill
Teaching: Introduction to Statistical Machine Learning Using R
Yufeng Liu is currently a professor in the Department of Statistics and Operations Research, the Department of Biostatistics, and the Department of Genetics at UNC-Chapel Hill. His current research interests include statistical machine learning, high dimensional data analysis, personalized medicine, and bioinformatics. He has taught statistical machine learning courses multiple times at UNC, as well as short courses on this subject at Joint Statistical Meetings, ENAR, and Biostatistics Summer Institutes at the University of Washington. Dr. Liu received the CAREER Award from the National Science Foundation in 2008, the Phillip and Ruth Hettleman Prize for Artistic and Scholarly Achievement in 2010, and the inaugural Leo Breiman Junior Award in 2017. He is currently an elected fellow in the American Statistical Association, the Institute of Mathematical Statistics (IMS), and an elected member of the International Statistical Institute.
Dr. Lobaton received the B.S. degree in mathematics and the B.S. degree in electrical engineering from Seattle University in 2004. He completed his Ph.D. in electrical engineering and computer sciences from the University of California, Berkeley in 2009.
He is currently a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. Dr. Lobaton joined the department in 2011.
His research focuses on the development of pattern recognition, estimation theory, and statistical and topological-data-analysis tools applied to wearable health monitoring, robotics and computer vision. He was awarded the NSF CAREER Award in 2016. Prior to joining NC State, he was awarded the 2009 Computer Innovation Fellows post-doctoral fellowship award and conducted research in the Department of Computer Science at the University of North Carolina at Chapel Hill. He was also engaged in research at Alcatel-Lucent Bell Labs in 2005 and 2009.
Vanessa Miller is an epidemiologist with UNC’s Injury Prevention Research Center, and she teaches Quantitative Methods for Healthcare Professionals, a biostatistics course, in the Gillings School of Global Public Health. She enjoys using innovative and interactive teaching methods to foster the application of statistical approaches to answer research questions in her student’s areas of interest. Her research has focused on chronic pain, suicide prevention and opioid and polysubstance overdose mortality. She uses “big data” approaches to studying suicide and substance overdose mortality in North Carolina and is involved in clinical trials for non-pharmacological interventions for chronic pain. She obtained her PhD from UNC in 2018.
Eric Monson is a data visualization specialist with the Duke University Libraries’ Data and Visualization Services. Although his PhD is in Applied Physics, from 2007 until he joined DVS in 2015, he collaborated with Duke faculty and graduate students from Math to Computational Biology to Art History, helping them transform, visualize and understand their data. In this position he enjoys introducing people to important skills they need but were never trained in, whether that means teaching visual design and communication to scientists, or helping humanists incorporate technology into their scholarship.
Santiago Olivella
University of North Carolina, Chapel Hill
Santiago Olivella is an Associate Professor of Political Science. Prior to joining the faculty at the University of North Carolina-Chapel Hill, he was an Assistant Professor at the University of Miami and a Visiting Research Scholar at Princeton Politics’ Q-APS Program. Originally from Colombia, he received his Ph.D. in Political Science from Washington University in St. Louis, and specializes in developing and applying statistical models—particularly Bayesian probabilistic models and machine learning techniques—to study electoral and legislative politics. His work focuses on the measurement of latent traits (such as group memberships of networked actors) and the political consequences of electoral and legislative institutions (particularly as they interact with geographic patterns of political support). You can find some of his peer reviewed work in Political Analysis, American Journal of Political Science, British Journal of Political Science, Electoral Studies, and theJournal of Politics.
Gabriel Peterson
NC Central University
Teaching: Intermediate Effective Information Visualization, Visualization in Data Science Using R
Gabriel Peterson is an Associate Professor at the School of Library and Information Sciences at North Carolina Central University in Durham, NC. He holds Bachelors degrees in Biochemistry (BS), Chemistry (BA), and Spanish (BA) from New Mexico State University, an MS in Biotechnology from the University of Texas at San Antonio and earned his Doctorate in Information Science from the University of Missouri in 2006.
Dr. Peterson is an information scientist; his areas of specialization are Scholarly Communication (especially Anomalous Literature), Information Policy (especially Information Security), and Inforamticw (especially Health Information Disparities). His research lies at the intersection of health sciences, biomedical literature, and the information society. Dr. Peterson studies the impact of errors in the scientific record as a way of evaluating the self-correcting properties of science.
Dr. Peterson’s research focuses on scholarly communication and the use of health sciences & biomedical literature; he studies the self-correcting attributes of science, the integrity of the scientific record and the changes that open access is having on the nature and use of health information. Health literacy and open access to scientific and health information can improve lives by reducing health information disparities. It is important to understand how to make high-quality information accessible to those who can benefit most from it.
Laura Tateosian is an Associate Teaching Professor in the Center for Geospatial Analytics at NC State University. She is a computer scientist with a research focus on visualizing geospatial-temporal data. She uses controlled studies and eye tracking technology to investigate innovative ways to represent and interact with geospatial data. Her interests include aesthetic climatology geovisualization, coastal terrain time-series visualization, gaze-based map interaction, geo-mining narrative archives, storytelling with maps, and open-source natural resources web mapping.
William D. Wheaton is a geographer and senior geospatial consultant with more than 30 years of experience applying geographic information systems (GIS) technology in environmental and social science research. Mr. Wheaton was a senior instructor at Environmental Systems Research Institute from 1984-1992 and from 1993 to 2018 held several geospatial positions at RTI International, including senior geospatial scientist and director of the Geospatial Science and Technology program. He currently co-leads the Geospatial Working Group and is a senior analyst and consultant for the Data Analysis Center component of NIH’s Environmental influences on Child Health outcomes (ECHO) project.