Learn about our instructors for Data Matters: Spring Ahead 2024.
East Carolina University
Teaching: Intermediate Python
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.
NC State University
Teaching: Introduction to Programming in R
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.
Teaching: Overview to AI and Deep Learning
Amy Hemmeter is a Sr. Manager of Data Science in Natural Language Processing at Workhuman. She has over 5 years of experience in industry in NLP as a Data Scientist, Machine Learning Engineer, Manager and Lecturer. She has worked on projects ranging from Conversational AI to Information Retrieval to Generative AI. She received her MSA from the Institute for Advanced Analytics at North Carolina State University in 2018, where she has also taught an annual workshop on Natural Language Processing since 2020. As someone who transitioned into data science and NLP from a linguistics background (an MA she also earned at NCSU in 2016), she cares deeply about translating complex topics in data science into information that students from all backgrounds can understand, as well as making that information relevant and practical to students who wish to make a career in industry.
NC State University
Teaching: Deep Learning with Python
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.
Teaching: Intermediate Data 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.
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.