Instructors – Summer 2023

Adel Alaeddini

University of Texas, San Antonio

Teaching: Deep Learning with Python

Adel is an Associate Professor of Mechanical Engineering at the University of Texas at San Antonio (UTSA). He is also the Faculty Fellow of the School of Data Science at UTSA. Adel’s interests involve both theoretical and applied aspects of artificial intelligence and machine learning, integrated with engineering knowledge with applications in healthcare, manufacturing, and energy. His focus areas of research include (1) Functional/deep graph analytics for dynamic characterization and optimal self-management of the emergence trajectories of multiple chronic conditions, (2) Active/transfer learning for efficient estimation and optimization of complex expensive systems in manufacturing and robotics, and (3) High dimensional data/sensor analytics for predictive modeling and control of energy systems. Adel’s teaching interests include deep/machine learning, optimization, and quality/reliability engineering.

Trent Buskirk

Bowling Green State University

Teaching: Survey Research/Big Data

Trent D. Buskirk, PhD is the Novak Family Professor of Data Science at Bowling Green State University.  His research interests are varied and include Mobile and Smartphone Survey Designs, methods for calibrating and weighting nonprobability samples, and in the use of machine learning methods for social and survey science design and analysis.  Prior to his post at BGSU, Trent served as the Director for the Center for Survey Research at UMASS Boston and prior to that Trent was the Vice President for Statistics and Methodology at the Marketing Systems Group (MSG) and was tenured in the department of Biostatistics in the School of Public Health at Saint Louis University. 

Jason Carter

University of North Carolina, Chapel Hill

Teaching: Introduction to Python, Intermediate Python

Jason Carter is an IT Engineer with Cisco Systems. He develops machine learning systems to improve the corporate content experience. He also works collaboratively with a distributed team of software developers. As adjunct faculty, he teaches students how to think logically and analytically, how to program using Python, and how to design database systems.

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.

Siobhan Day Grady

NC Central University

Teaching: Overview to AI and Deep Learning

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: 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.

Vanessa Miller

University of North Carolina, Chapel Hill

Teaching: Basic Statistics in R

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

Duke University

Teaching: Introduction to Effective Information Visualization

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

Teaching: Basics of R for Data Science and Statistics

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.

Laura Tateosian

NC State University

Teaching: Geospatial Analytics

Laura Tateosian is a Research Assistant 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.

Bill Wheaton

Teaching: Introduction to Geospatial Data for the Data Scientist

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. 

Angela Zoss

Duke University

Teaching: Visualization for Data Science in R, Advanced Visualization in R: R Shiny

Angela Zoss is an Assessment & Data Visualization Analyst with the Duke University Libraries. She has been teaching visualization practices and tools for over 10 years, including six years as a Data Matters instructor and over 100 workshops, guest lectures, and conference presentations. In her role as Duke University’s first Data Visualization Coordinator, she created new library workshops on visualization; hosted an annual student data visualization contest; co-organized a weekly talk series on visualization topics; consulted with students, researchers, and faculty members on research projects; and helped to introduce visualization concepts and tools into several undergraduate and graduate courses. She holds a Master of Science in Communication from Cornell University and a Ph.D. in Information Science from Indiana University. She is a certified instructor for both RStudio (tidyverse certified) and The Carpentries.