Faculty Research

Zeyu Chen

Chen’s research focuses on software security, particularly in enhancing memory safety through innovative static and dynamic analysis techniques. His work involves automating bug detection, patch generation, and verification using Large Language Models (LLMs). He specializes in analyzing vulnerabilities like use-after-free (UAF) in C and C++ and aims to create robust methods for identifying and mitigating complex software issues. His research also explores multi-language support, adapting the analysis framework to accommodate languages such as Rust.

Seth Fogarty, Ph.D.

Fogarty’s research interests span automata theory, programming languages, and interdisciplinary projects. He studies the use of automata in formal verification, proving a program correct, including bespoke data structures used in that domain. The dsmodels domain-specific language is a tool built in R for visualizing models studied by mathematicians. He has co-advised research projects in the philosophy of computation and digital anthropology. Summer research projects are ideally preceded by a directed study in the spring semester.

Matthew Hibbs, Ph.D.

Hibbs’s research is focused on how to best utilize high-throughput data sources to understand biology at multiple levels. This problem has become increasingly challenging over the past decade as new experimental techniques and resources (e.g., gene expression microarrays, deep sequencing, tandem mass spectrometry, etc.) have grown widely available and more affordable. While these data promise to shed light on cellular mechanisms, gene regulation, protein functions, and ultimately human disease, the rate at which these data are translated into knowledge is currently much slower than the rate of data generation. In order to help bridge this gap, his focus is on developing novel algorithms and approaches for the analysis, exploration and visualization of this data. In particular, these methods incorporate biologists into the early phases of analysis in order to utilize their existing, expert knowledge.

Britton Horn, Ph.D.

Horn's research interests lie in the fields of game design, artificial intelligence, human-computer interaction, and computational creativity. His primary research focuses on leveraging novel AI applications to enhance the efficacy and quality of educational games. In addition, he researches computational creativity methods to design AIs that produce artistic artifacts (e.g. sculpture, music, digital images) in an effort to expand creative expression.

Johanna Jacob

Jacob's research revolves around imagining and fostering equitable, joyous, and liberatory learning experiences in computing and cybersecurity, particularly within rural school communities. Focused on creating inclusive opportunities for students to engage with cybersecurity, her work addresses disparities in access and resources. Utilizing data-driven approaches, Jacob researches the current state of cybersecurity in rural and underserved schools and districts. She also studies the effectiveness and outcomes of cybersecurity competitions for these populations, working closely with national programs like CyberPatriot, National Cyber League (NCL), and initiatives by CYBER.ORG. Her recent efforts include a pilot project aimed at establishing cybersecurity pathways for middle and high school students in a rural Texas school district. In addition to her research, Jacob serves as a reviewer on the Google Cybersecurity Clinics Fund Panel, HICCS, ACM SIGCSE, and is an active member of the Women in Cybersecurity national organization.

Paul Myers, Ph.D.

Myers’s research has included software engineering, theoretical computer science, and the mathematical foundations topic of constructivity (Intuitionism) applied to computer science.  More recently he has investigated the historical 1980s-1990s Japanese Fifth Generation Computing Project. He has just begun working in computing/AI/security ethics and a more general notion of socially responsible computing, including the relatively new area of vulnerability theory.  This has tied in with some of his past publications regarding women (students) in computer science.

Eva Tuba, Ph.D.

Tuba’s research is at the forefront of integrating nature-inspired metaheuristics, digital image processing, and deep learning, with applications of a wide range of real-world challenges in medicine, biology, and other domains. Her work includes the use of artificial intelligence for the automatic design of optimization algorithms, driving efficiency in complex, high-dimensional problem-solving. She further enhances these algorithms through rigorous statistical analysis and adjustment, ensuring that nature-inspired approaches are precisely tuned to meet the demands of the specific applications. Another key aspect of the research is the application of these techniques to optimize AI models, enabling high-performance solutions in predictive tasks and enhancing robustness across models. Examples of applications are optimized image analysis for early medical diagnosis, adaptive algorithms for robot navigation, and AI-driven detection systems for rapid response in environmental disasters.

Sheng Tan, Ph.D.

Tan’s research interests span mobile computing and cybersecurity with an emphasis on wireless and mobile sensing. His work utilizes wireless network and mobile devices to sense the human activity at various scales as well as objects in the surrounding environments. He also works on developing biometric-based user authentication protocol to enhance security on smartphones. His current projects include mobile safety system for distracted driver/pedestrian and mobile sensing for human computer interaction applications.

Yu Zhang, Ph.D.

Zhang’s research falls within Agent-based Modeling and Simulation, which is a subfield of Artificial Intelligence. Her previous research concentrates on multi-agent social simulation, which is to understand how the decentralized interactions of agents could generate collective social behaviors. Her current research focuses on deep learning. Her current projects include deep learning neural networks and its applications in bioinformatics.