Research
Building AI that can be trusted
Artificial intelligence now makes consequential decisions — in a hospital, on a power grid, inside a moving vehicle. The hard problem is no longer whether a model can produce an answer, but whether anyone should trust it.
That question runs through our work. Our faculty build AI systems that explain themselves, hold up under attack, and keep working as the world changes around them — and they build them for domains where being wrong carries a real cost. It is also why our research reaches across the university: computer science at TCU is most useful pointed at someone else's hardest problem.
Research Areas
Foundations — the methods we develop
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Machine Learning & Explainable AI — explainable deep learning, transfer and continual learning, graph representation learning, and efficient adaptation of large language models.
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Cybersecurity & AI Security — AI as a defensive tool and as a new attack surface: adversarial LLMs, threat detection, automotive and cyber-physical security, and privacy-preserving machine learning.
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Networks & Cyber-Physical Systems — 5G/6G network intelligence, signal processing, time-sensitive IoT communication, and edge sensing.
Applications — the domains we work in
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AI for Health — electronic health record mining, graph-driven drug discovery and repurposing, and clinical decision support, informed by faculty ties to the Mayo Clinic and to TCU's Burnett School of Medicine.
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AI for Science & Spatiotemporal Prediction — rare-event forecasting for physical systems, from solar flares and space weather to intelligent transportation and power grids.
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AI for Software Engineering & Education — large language models and knowledge graphs applied to how software is specified and built, and to how computing is taught.
[Meet our faculty →]
Labs & Research Groups
— Dr. Robin Chataut Trustworthy AI, cybersecurity, and intelligent systems for high-stakes settings.
— Dr. Kaiqun Fu Spatial data mining, GeoAI, and urban computing.
Additional faculty-led groups work on interpretable and adaptive machine learning (Dr. Chetraj Pandey), secure wireless and cyber-physical systems (Dr. Afia Anjum), and graph learning for biomedical informatics (Dr. Shuteng Niu).
Research Computing
Through AI² (Accelerating Institutional Artificial Intelligence), a university-wide investment built with Dell Technologies and AWS, our researchers work on infrastructure usually reserved for major research labs:
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16 NVIDIA H200 GPUs, NVLink-interconnected for large-scale deep learning and model training
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High-performance all-flash storage sized for GPU training workflows
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Hybrid cloud architecture — security-sensitive research stays on premises; flexible workloads route to AWS
For a doctoral applicant, this answers a fair question: can I do serious computational research here? Yes — on campus, from day one.
[Learn more about AI² →]
Partners & Support
Our faculty's research is supported by federal agencies including the National Science Foundation and NASA.
Collaborations extend well beyond campus — to Los Alamos National Laboratory, the Mayo Clinic, and the NASA-partnered Frontier Development Lab — and across TCU, with the Burnett School of Medicine, the Neeley School of Business, and colleagues in psychology, energy, and the sciences.
Students in Research
Research here is not reserved for doctoral students.
Undergraduates work directly with faculty on real projects, often alongside graduate students in a lab — an advantage of a department where classes are small and faculty are reachable. If an area or lab above interests you, email that faculty member directly and ask whether they take undergraduate researchers. That is the expected way to start, and you do not need prior research experience to ask.
Ph.D. students join a small, fully funded program with close faculty mentorship and early research engagement. Admission depends on finding the right advisor, so read the labs above before applying — and say who you want to work with.
[Meet our faculty →] · [Ph.D. in Computer Science →]
Work With Us
Industry partners — sponsored research, senior design projects, and access to faculty expertise. [Senior Design Sponsorship →] · [Advisory Board →]
TCU colleagues — if you have a research problem with a computational core, we want to hear about it.
Prospective faculty — the department is growing. [Open Positions →]
Dr. Bingyang Wei Department Chair, Computer Science · 817-257-7166 Tucker Technology Center, Suite 341