The College of Computing & Informatics (CCI) at Drexel University is associated with research labs and centers that lead a number of the University's cutting-edge research initiatives.
CCI Research Centers & Labs
- Applied Symbolic Computation Laboratory (ASYM)
Research focus: Symbolic computation and computer algebra, including program generation, verification, and quantum computing applications.
- The DIVA Lab
Research focus: Human-computer interaction and extended reality, with a focus on virtual avatars and agents.
- DONUTS Collaboratory
Research focus: Ethical AI and online safety, focusing on governance frameworks that preserve human agency, dignity, and safety in digital environments.
- Economics and Computation (EconCS) Lab
Research focus: Algorithmic game theory and mechanism design, with emphasis on fairness, approximation algorithms, and machine learning applications.
- Interactive Systems for Healthcare (IS4H) Research Lab
Research focus: Human-computer interaction and collaborative systems, applied to medical informatics and healthcare technologies.
- Metadata Research Center
Research focus: Metadata and semantic technologies, formalizing metadata-driven approaches for cross-sector data utilization.
- Software Engineering and Analytics Research (SOAR) Lab
Research focus: Enhancing software engineering tools through empirical analysis, natural language processing, and machine learning.
- SPiking And Recurrent SoftwarE (SPARSE) Coding Lab
Research focus: Computational neuroscience and non-feedforward architectures, with applications in generative AI, cryptography, and distributed systems.
- The TeX-Base Lab
Research focus: Textual agents and explainable AI, using case-based reasoning to support human-centered decision-making.
- The Visual Intelligence Lab (VILab)
Research focus: 3D computer vision and human-centric perception; generative AI for controllable human synthesis and editing; trustworthy generative models and DeepFake detection; physically grounded human modeling; vision-language and multimodal learning for robust human understanding and real-world deployment; biometric recognition in the wild.