The Responsible AI Lab at the University of North Texas (UNT) advances the science and practice of AI. Founded in August 2022, our mission is to build systems that are both powerful and trustworthy. We focus on the intersection of Efficient Generative AI, Responsible AI, and Applied AI, spanning large language models, multimodal learning, and scalable systems.

Efficient GenAI

We develop cutting-edge techniques for LLMs and Multimodal pipelines that reason over text, code, diagrams, and figures. Key areas include:

  • Inference & RAG: Efficient long-context inference and hallucination-aware retrieval-augmented generation.
  • Edge AI: Lightweight models for safety-critical, resource-constrained environments.
  • GenAI for Quantum: Interplay between Generative AI and Quantum Computing.

Trustworthy AI

We design algorithms prioritizing security, privacy, and fairness, keeping humans at the center of the loop. Our research covers:

  • Privacy & Security: Differentially private learning, prompt protection, and adversarial auditing.
  • Fairness: Bias mitigation in generation/search and fairness-aware model compression.
  • Transparency: Explainable recommendation and information access systems.

Applied Impact

We collaborate with domain experts to translate methods into real-world impact across Bio-Medicine, IoT, and Science:

  • Biology & Med: Trustworthy diffusion models for cardiac organoids and clinical decision support.
  • Education & Social: AI analysis of handwritten work and large-scale social media analytics.
  • Science & Ag: Smart agriculture, HPC, and large-scale earth system modeling.

Our Impact: Our work is published in top-tier venues including AAAI, IJCAI, ACL, EMNLP, CVPR, ICCV, USENIX Security, IROS, WWW, and HPDC.

Our Partners: Research at the lab is supported by federal agencies and industry leaders, including the National Science Foundation (NSF), Oak Ridge National Laboratory (ORNL), Microsoft, Google, and NVIDIA.

NSF ORNL Microsoft Google NVIDIA

Join Us: We are actively looking for motivated undergraduate and graduate students (Masters and PhDs) and welcome collaborations with academic, industrial, and clinical partners. To join our team or collaborate on building responsible, human-centered AI, please contact Dr. Yunhe Feng.