Members

Ilene R. Berson, Ph.D.

Professor, Early Childhood Education, College of Education

  • Ph.D., Counseling with an emphasis in School Psychology, The University of Toledo
  • Dr. Berson's research explores the intersection of technology, pedagogy, and equity in early childhood education. Her work investigates how emerging technologies, including artificial intelligence, multimodal tools, and language-based technologies, can foster inquiry, creativity, and inclusivity in PreK-12 contexts. Dr. Berson’s internationally recognized research emphasizes participatory methodologies, visual research, and digital citizenship, with a focus on leveraging tools like natural language processing, automated assessment, and sentiment analysis to promote multilingualism, developmental appropriateness, and accessibility.

Michael Berson, Ph.D.

Professor, Social Science and Elementary Education, College of Education

  • Ph.D., Curriculum and Instruction K-12, The University of Toledo
  • Dr. Berson is an internationally recognized leader in social science education, technology integration, and global child advocacy. His research focuses on leveraging emerging technologies, including AI and language-based tools, to foster inquiry, creativity, and equity in education, with emphasis on multilingualism, inclusivity, and developmental appropriateness.

Leah Burger

Ph.D. Candidate, Literacy Studies, College of Education

  • Leah Burger is a Doctoral Candidate at the University of South Florida pursuing her Ph.D. in Literacy Studies at the College of Education. Her research centers on elevating children’s literacy practices on/with digital platforms, focusing specifically on youth’s game design processes with generative AI through the lens of raciolinguistics and composition. Through this research, she hopes to build infrastructures that connect computer science, literacy research, and education in efforts to create more linguistically inclusive digital environments.

Shaun Canavan, Ph.D.

Associate Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Computer Science, Binghamton University of Rochester
  • Faculty Member, Computer Vision and Pattern Recognition Group
  • Dr. Canavan's research focuses broadly on Affective Computing and Human-Computer Interaction, with research interest in sentiment analysis. He has over 60 publications in top conferences and journals such as CVPR, ICPR, ICMI, ACII, FG, Pattern Recognition Letters, IEEE Transactions on Visualization and Computer Graphics, and IEEE Transactions on Affective Computing.

Anshuman Chhabra, Ph.D.

Assistant Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Computer Science, UC Davis
  • Director, Pioneering Advances in Learning Methods (PALM) Lab
  • Dr. Chhabra's research primarily focuses on holistically improving current and next-generation Artificial Intelligence models (e.g. Large Language Models) with respect to multiple qualitative facets, such as utility, fairness, robustness, and security. His work also aims to develop data-centric learning approaches that can benefit a large class of AI models in multiple domains such as Natural Language Processing and Computer Vision.

Ly Dinh, Ph.D.

Assistant Professor, School of Information, College of Arts and Sciences

  • Ph.D, School of Information Sciences, University of Illinois Urbana-Champaign
  • Dr. Dinh's research applies methods from natural language processing and network science to aid natural crisis response. She also develops and tests new measures to capture the complexities of social interactions across various network levels, including egos, dyads, triads, subgroups, and whole networks. Her recent work includes developing and benchmarking large language models (LLMs) to determine their accuracy and reliability in various real-world contexts, including enhancing decision-making in crisis situations, and combating cyber scams. Dr. Dinh's research has been published in Quantitative Science Studies, Journal of Contingencies & Crisis Management, Scientific Reports, and other peer-reviewed conferences in computational social science. She is a 2023 FL-AGEP Scholar, and a 2020 Grace Hopper Scholar for Women in Computing.

Zhao Han, Ph.D.

Assistant Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Computer Science, UMass Lowell
  • Director, Reality, Autonomy, and Robot Experience (RARE) Lab
  • Dr. Han's research lies broadly in human-robot interaction (HRI), augmented reality (AR), robotics, and AI. He focuses on designing, developing, and evaluating novel robotic systems and interactions, for embodied robots to be more capable and understandable while interacting, collaborating, and teaming up with humans. His group also studies natural language, mainly references, and through the lens of HRI. Their current work uses LLMs for a mental health story telling robot to be deployed to dorms.

Gene Kim, Ph.D.

Assistant Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Computer Science, University of Rochester
  • Director, Language G.R.A.S.P. Lab
  • Dr. Kim's research aims to integrate the benefits of neural and symbolic methods for modeling language meaning which supports automatic parsing and computer reasoning while being informed by linguistic semantics.

Seungbae Kim, Ph.D.

Assistant Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Computer Science, University of California, Los Angeles
  • Dr. Kim's research interests include fairness in machine learning, explainable AI, data mining, including social media and web mining, and applied data science, particularly in online social networks and mental health applications.

Lingyao Li, Ph.D.

Assistant Profession, School of Information, College of Arts and Sciences

  • Postdoc, School of Information, University of Michigan
  • Ph.D., Civil and Environmental Engineering, University of Maryland College Park
  • Dr. Li's research focuses on natural language processing and crowdsourced data, with an emphasis on large language models, their reasoning, trustworthiness, and ethical use. He applies these methods in urban and health informatics to study community resilience, disaster response, and public reactions to health policies.

John Licato, Ph.D.

Associate Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Computer Science, Rensselaer Polytechnic Institute
  • Director, Advancing Machine and Human Reasoning Lab
  • Dr. Licato is an AI and NLP researcher focused on human-level cognitive reasoning, including computational modeling, cognitive architectures, and AI-based reasoning methods. His interests span natural language processing, robotics, automated theorem proving, analogical and deductive reasoning, argumentation, legal reasoning, and formal and informal logics.

Ankur Mali, Ph.D.

Assistant Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Informatics, Pennsylvania State University, University Park
  • Director, Trustworthy Knowledge-Driven Artificial Intelligence (TKAI) Lab
  • Dr. Mali's research lies at the intersection of language, memory, and computation, focusing on designing interpretable, knowledge-guided deep learning systems for trustworthy information generation. He explores the theoretical and empirical underpinnings of deep learning's success in natural language recognition. His lab also develops brain-inspired neural architectures tackling challenges like lifelong learning, minimal supervision, and sparsity in NLP and computer vision, as well as low-resource, robust NLP models to generate ethical information and support underrepresented groups.

Tempestt Neal, Ph.D.

Associate Professor, Department of Computer Science and Engineering, College of Engineering

  • Ph.D., Computer Engineering, University of Florida
  • Director, Cyber Identity and Behavior Research Lab
  • Dr. Neal's research focuses on mobile-based sensing for biometrics and human behavior understanding in interdisciplinary applications, with an emphasis on cybersecurity awareness among underrepresented populations in science and engineering. Her lab also conducts work in natural language processing (NLP), specializing in opinion mining using deep learning architectures to address challenging tasks such as implicit and cross-domain contexts.

Jenifer Jasinski Schneider, Ph.D.

Interim Dean and Professor, Literacy Studies, College of Education

  • Ph.D., Language, Literature, and Reading Education, The Ohio State University
  • Dr. Schneider's research is centered on the composing processes of children, encompassing both print-based writing development and multimodal composition in digital and embodied spaces. Her work specifically investigates how youth use Artificial Intelligence (AI) tools in their creative processes to produce visual, print, and performance texts. Fundamental to her research in composition is an emphasis on arts-based approaches to literacy education, incorporating elements of process drama, children's literature, and digital tools to enhance symbolic development and meaning-making strategies among youth.

Ehsan Sheybani Ph.D.

Professor, School of Information Systems and Management, Muma College of Business

  • Ph.D., Electrical Engineering, University of Florida
  • Dr. Sheybani's primary research areas include communication, signal processing, and data analysis. He has been involved in teaching, practicing, researching, and consulting DSP applications in technology, systems analysis, and data science for the past 20 years.

Thanh Thieu, Ph.D.

Researcher, Health Outcomes & Behavior Program, Machine Learning Program, Moffitt Cancer Center

  • Ph.D., Computer Science, University of Missouri-Columbia
  • Director, Language and Intelligence Laboratory (LAILab)
  • Dr. Thieu’s research interests center on using natural language processing (NLP) to process free text clinical notes in electronic health records and free text scientific reports in (bio)medical literature. His research spans whole-person functional status information, knowledge graph extraction, high throughput text mining, lexical complexity and language generation, and computer-assisted coding for healthcare and medical billing.

Kaiqi Xiong, Ph.D.

Professor, Department of Mathematics and Statistics, College of Arts and Sciences

  • Ph.D., Computer Science, North Carolina State University
  • Dr. Xiong's research interests lie in the broad areas of Security, Networking, and Big Data Analytics with a focus on experiments and modeling in cyber physical systems (e.g., power grids, emergency response, and transportation), user and attacker behavior analytics with social sciences, cvloud computing and mobile networks (e.g., smartphones), sensor networks (e.g., video/sensor surveillance for target tracking), Internet of Things (IoT) and cyberinfrastructures for smart cities, applied cryptography and theoretical foundation of computer science, and mathematical modeling and statistical data analysis for computer science, computer engineering, and health-care problems.

Yasin Yilmaz, Ph.D.

Associate Professor, Department of Electrical Engineering, College of Engineering

  • Ph.D., Electrical Engineering, Columbia University, New York, NY
  • Director, Secure and Intelligent Systems (SIS) Lab
  • Dr. Yilmaz's research interests include machine learning algorithms and applications in computer vision, cybersecurity, biomedical systems, intelligent transportation systems, energy systems, and communication systems. In terms of language intelligence and analytics research, Dr. Yilmaz investigates vision-language models for image and video understanding, multimodal models for biomedical datasets (e.g., electronic health records, radiology images, histopathology images, molecular data), and the security and robustness of language agents.