Meet the Data Science Faculty

Dan Johnson

Professor Johnson leads the Computational Cognition and Creativity Lab, which is dedicated to probing the mechanisms involved in creativity processes, specifically the generation and evaluation of novel ideas. A key objective of the lab is to automate creativity assessment, with the aim of providing easy and open access to researchers and educators. The research employs a wide array of methodologies, including natural language processing, large language models, machine learning, distributional semantic modeling, simulation, and interactive web applications. Additionally, the lab relies on the statistical and graphics platform R for modeling and data analysis. Beyond creativity, Professor Johnson's team also explores the realm of metacognition in creative thinking, contributing to a deeper understanding of the cognitive processes involved in creativity assessment and enhancement.

Courses

CBSC 240: Introduction to Data Science: Mind Analytics
CBSC 250: Statistics and Research Design
CBSC 359: Capstone: Advanced Methods

Bob Ballenger

Professor Ballenger's research encompasses a diverse range of topics within the field of Instructional technology and data management. His work focuses on web-based instructional technologies, exploring their applications and impacts on education. He also delves into Learning Management Systems and the diverse online learning styles they cater to, aiming to enhance the effectiveness of virtual education. Ballenger's expertise extends to the domain of business analytics and data mining, providing valuable insights into data-driven decision-making. Additionally, he explores database management systems, contributing to the efficient organization and retrieval of data. His research endeavors span the intersection of technology, education, and data management, making a significant impact in these domains

Courses

BUS 316: Business Analytics
BUS 317: Data Mining for Business Analytics

Jeff Barry

Professor Barry's research is centered on several key areas. He is dedicated to unraveling the intricacies of how individuals convey and interpret narratives, especially in the realm of non-fiction, within the context of digital media. Furthermore, his work explores the evolution of literary magazines, shedding light on their historical development and contemporary significance. Professor Barry also employs social network analysis as a tool to delve into the intricate "worlds" of literature and the arts, offering insights into the interconnections and dynamics of these creative domains. His research endeavors contribute significantly to our understanding of storytelling in digital platforms, the evolution of literary publications, and the exploration of artistic and literary networks.

Courses

DCI 110: Web Programming for Non-programmers
DS 401: Directed Individual Study

Justin Davis

Professor Davis is highly proficient in the development of computer programs tailored for web scraping and text analysis, which constitute integral components of his research and teaching efforts. His background includes a predoctoral research fellowship in Barcelona, where he successfully instructed undergraduate courses in both English and Spanish. With a diverse teaching portfolio, he brings expertise in data analysis, computer programming, and the utilization of statistical software, in addition to offering traditional business economics courses. Professor Davis's multifaceted skills make him a valuable asset in the field, enabling students to explore the intricacies of web scraping, text analysis, and business economics.

Courses

BUS 306: Seminar in Management Information Systems: Text Analytics for Business Insights

Jon Eastwood

Professor Eastwood's current research portfolio encompasses several areas of study. A social theorist with a strong interest in quantitative and computational methods, he works on theories of social structure (complex lacings of relationships, representations, and rules), aiming to find ways to conceive of social structural processes that will help us to better measure, represent, and model them with empirical data. He is also working (with students) on a project related to transportation networks, neighborhoods, and inequalities. Ongoing research also includes longer-term project, drawing on the analysis of public opinion data, focused on cynicism, distrust, and partisanship.

Courses

SOAN 218: Basic Statistics for Social Sciences
SOAN 219: Applied Bayesian Regression
SOAN 220: Baseball and Statistics
SOAN 222: Data Science Tools for Policy Analysis
SOAN 265: Exploring Social Networks
SOAN 266: States, Data, and Population Policies
SOAN 268: Neighborhoods and Inequality

Lingshu Hu

Professor Hu's research expertise spans a wide range of topics within the realm of machine learning, deep learning, and natural language processing (NLP). His work focuses on understanding communication patterns and their effects, with a particular emphasis on social media analytics. In addition, Hu delves into the complexities of social identities in the digital age, exploring how they are constructed and conveyed online. His research also delves into the dynamics of the digital public sphere, shedding light on the evolving landscape of online discourse and its implications. Hu's contributions provide valuable insights into the intersection of technology, communication, and social identity in the digital era.

Courses

BUS 202: Fundamentals of Business Analytics
BUS 306A: Applied AI and Machine Learning
INTR 202: Applied Statistics

Keri Larson

Professor Larson's current research involves the analytics of unstructured textual data to support organizational decision-making. She is working on a number of projects that examine how technologies developed by computational linguists and computer scientists can enable the automated discovery of patterns across large textual datasets to create business value. Her other research interests include innovation in health care IS, the impact of IT/IS on professional group identity, and social media analytics. Professor Larson's research has been published in Information and Organization, The Cooperative Accountant, and the proceedings of several conferences including the International Conference on Information Systems, the European International Conference on System Sciences, and the Academy of Management Annual Meeting. She has presented her research at a number of these conferences.

Courses

BUS 314: Data Science for Business
BUS 315: Data Management
BUS 390: Green IS in Iceland

Sybil Prince Nelson

Professor Nelson's research is primarily focused on the classification and prediction of diseases through the application of data mining techniques. Her work involves the development and utilization of decision trees, with a specific emphasis on logic regression, to facilitate accurate disease prognosis and identification. Additionally, she explores the effectiveness of Random Forests, a machine learning ensemble method, in enhancing disease prediction and classification models. By harnessins these data-driven approaches, Sybil Prince Nelson's research contributes significantly to the field of healthcare by improving our ability to anticipate and classify diseases, ultimately aiding in early detection and effective treatment.

Courses

MATH 101: Calculus I
MATH 309: Probability
MATH 401: Dir Study: Topics in Statistics

Holly Shablack

The research led by Professor Shablack delves into the intricate interplay between language, culture, and emotions, examining their collective impact on various aspects of human life, including health, well-being, relationships, and emotional perceptions and experiences. To gain a comprehensive understanding, Shablack employs a multi-method approach, incorporating diverse statistical modeling and methods. Current projects encompass a wide array of research methodologies, such as neuroimaging meta-analyses, longitudinal analyses, text analysis, multilevel modeling, structural equation modeling, and time series approaches focused on cardiovascular functioning. This multifaceted research seeks to uncover how individuals and broader groups or cultures interpret and derive meaning from their emotional states, offering valuable insights into the complex dynamics of human emotions and their consequences on various aspects of life.

Courses

CBSC 298: Introduction to Data Science: Trends over time
CBSC 250: Statistics and Research Design
CBSC 398: Advanced Methods in Emotion, Language, and Culture

Cody Watson

Professor Watson's research focuses on the application of artificial intelligence techniques to solve complex and intriguing problems across various domains. Recently, his lab has been leveraging AI for advanced programming tasks such as source code generation, automatic documentation, and neural machine translation.

Professor Watson's lab is not confined to a single discipline and remains open to interdisciplinary collaborations. His lab has engaged in diverse projects that include the use of reinforcement learning and content generation, as well as addressing problems related to medical imaging and identifying geological structures.

Courses

CSCI 111: Fundamentals of Programming I
CSCI 112: Fundamentals of Programming II
CSCI 230: Machine Learning and Big Data
CSCI 257: A Walk Through the Ages
CSCI 315: Artificial Intelligence
CSCI 297A: Machine Learning
CSCI 397D: Reinforcement Learning

Natalia Toporikova

Professor Toporikova's research centers on the application of computational modeling techniques to investigate a broad spectrum of biological systems. Her work involves the analysis of animal behavior, encompassing aspects such as locomotor activity, eating, and drinking. She also focuses on modeling hormone release in both healthy and diseased conditions, providing insights into the underlying physiological mechanisms. In addition, Professor Toporikova conducts simulations of neural networks in the brain, offering valuable perspectives on the intricate workings of the nervous system. Some of her recent projects include the exploration of neural control of breathing, the initiation of pregnancy in rats, and the daily circadian cycle.

Courses

BIOL 187: Intro to Data Science
BIOL 201: Biostatistics
BIOL 282: Modeling and Simulation in Public Health

Gregg Whitworth

Professor Whitworth's research is primarily focused on unraveling the intricate mechanisms of post-transcriptional gene regulation in eukaryotes. He seeks to shed light on the essential processing steps that eukaryotic mRNA undergoes, rendering it ready for translation. These processes are executed by large molecular machines. In contrast to the extensive knowledge we possess regarding transcriptional regulation, our understanding of how these complexes are modulated to influence the timing and diversity of gene expressions remains relatively limited. Dr. Whitworth's work holds the promise of expanding our comprehension of post-transcriptional gene regulation, ultimately advancing our knowledge of eukaryotic gene expression.

Courses

BIOL 185: Intro to Data Science: Visualizing and Exploring Big Data
BIOL 385: Molecular Mechanics of Life