Core Areas of Research

The USF NLP Group bridges the gap between cutting-edge research and real-world applications in language and data intelligence. With a mission to create impactful technologies, our work spans diverse domains, addressing challenges in understanding, interpreting, and generating language in ways that benefit society. By combining insights from multiple disciplines, we aim to foster innovation, inclusivity, and ethical practices in AI and NLP. Below are the core areas driving our research and development efforts:

Natural Language Processing (NLP) and Text Analytics

  • Clinical text mining and processing of electronic health records.
  • Sentiment analysis and opinion mining in implicit and cross-domain contexts.
  • Multilingual applications and accessibility in language technologies.
  • Automated assessment and authorship attribution.

Human-Computer and Human-Robot Interaction

  • Designing embodied robots with improved human interaction capabilities.
  • Using large language models (LLMs) in mental health applications.
  • Enhancing accessibility and inclusivity in digital communication.

Fairness, Ethics, and Equity in AI

  • Developing interpretable and knowledge-guided deep learning systems for fairness and inclusivity.
  • Addressing ethical considerations in AI and NLP applications, including low-resource languages.
  • Promoting multilingualism and equity in educational settings.

Machine Learning and Multimodal Data Integration

  • Vision-language models for image and video understanding.
  • Multimodal analysis of biomedical datasets, including radiology and histopathology images.
  • Robust and sparse deep learning models for NLP and computer vision tasks.

Cybersecurity and Trustworthy AI

  • Behavior analytics for cybersecurity awareness and human identity verification.
  • Fairness and robustness of language agents in adversarial settings.
  • Applying AI in cybersecurity challenges such as user and attacker behavior modeling.

Applications in Education, Health, and Society

  • Leveraging AI tools for creative multimodal composition among youth.
  • Enhancing inquiry and equity in PreK-12 education through language-based tools.
  • Automated coding for healthcare and medical applications.

Knowledge Representation and Reasoning

  • Advancing machine and human reasoning through analogical, deductive, and informal methods.
  • Building cognitive architectures and AI-based reasoning methods.
  • Knowledge graph extraction and high-throughput text mining.