AI & Quantum Innovation Lab

Data is classified and handled in partner-isolated sandboxes with least-privilege access, audited logging, and reproducible pipelines. We prefer synthetic/de-identified datasets and explicit Data Use Agreements; when regulated data is needed, we operate under FERPA/HIPAA-aligned controls and IRB review as appropriate. Every project carries a DPIA-lite and an IP/publication plan from day one.

Our default is “privacy by design”: minimize data, maximize utility via techniques such as differential privacy, secure aggregation/federated learning, and model-level redaction when appropriate. We publish model cards and data sheets, run privacy threat modeling at kickoff, and measure leakage/attack surfaces during evaluation. The goal is actionable models for partners without moving or exposing sensitive data.

We take a hybrid-first approach: students and faculty use simulators and vendor backends to test algorithms like VQE/QAOA and quantum-enhanced ML on real use cases (optimization, materials, logistics). Projects emphasize noise modeling and error-mitigation so results are honest and decision-relevant today—not hype. The lab helps partners build a roadmap from strong classical baselines to future quantum advantage.

Christian faith shapes both what we build and how we build it: each project includes an Ethics Note that considers human dignity (imago Dei), fairness, and downstream impact on workers and communities. Through a Faith & Technology colloquium, service-oriented projects, and faculty mentoring, students learn to see innovation as vocation—using their gifts to serve the neighbor. Decision gates favor solutions that reflect compassion, stewardship, and truth over mere novelty.

Click here to view Concordia's AI & Quantum Innovation Lab Biblical Guidelines.

AI Clinical-Trial Navigator. In partnership with a regional oncology group, our student–faculty team is co-developing an AI tool that uses large language models and knowledge graphs to transform de-identified patient data into rapid, evidence-based trial matches. Deliverables include a secure clinician dashboard, pilot-ready prototype, and paid student internships—showcasing how CUW talent accelerates real-world impact in healthcare.

CUW students and faculty are working in collaboration with several external partners (Santa Fe Institute / Portland State, Oak Ridge National Laboratory (ORNL), Quantinuum, IonQ, and the U.S. Army Research Laboratory) on a swarm-intelligence control layer that teaches AI “guardians” to detect and preempt qubit decoherence in quantum systems, trained in high-fidelity Lindblad simulations built from real device T₁/T₂ data and validated across multiple hardware platforms.

In partnership with Concordia’s School of Health Professions, faculty and students are developing a cutting-edge educational simulation that allows occupational and physical therapy students to explore how different environmental modifications can accommodate individuals with disabilities. This faculty and student-led project, housed in the AI & Quantum Innovation Lab, blends real-world learning with emerging technologies like generative AI, offering both a standard PC application and an immersive VR experience to prepare future practitioners for compassionate, effective care.

The goal of this project is to develop privacy-preserving machine learning models, using technologies such as PySyft, to help generalize efforts at universities to improve student success and retention. Many universities collect extensive data on their students, including academic performance, demographics, financial aid information, campus involvement, and more. Applying advanced machine learning techniques to this rich data has the potential to uncover valuable insights that could help identify at-risk students early in their academic journey and enable targeted interventions to improve their likelihood of success and ultimately boost retention rates.

National Artificial Intelligence Research Resource (NAIRR) Grant 240195

Text classification in natural language processing (NLP) is evolving rapidly, particularly with the surge in transformer-based models, including large language models (LLM). This paper presents an in-depth survey of text classification techniques across diverse benchmarks, addressing applications from sentiment analysis to chatbot-driven question-answering. Methodologically, it utilizes NLP-facilitated approaches such as co-citation and bibliographic coupling alongside traditional research techniques. Because new use cases continue to emerge in this dynamic field, the study proposes an expanded taxonomy of text classification applications, extending the focus beyond unimodal (text-only) inputs to explore the emerging field of multimodal classification. While offering a comprehensive review of text classification with LLMs, this review highlights novel questions that arise when approaching the task with transformers: It evaluates the use of multimodal data, including text, numeric, and columnar data, and discusses the evolution of text input lengths (tokens) for long text classification; it covers the historical development of transformer-based models, emphasizing recent advancements in LLMs; it evaluates model accuracy on 358 datasets across 20 applications, with results challenging the assumption that LLMs are universally superior, revealing unexpected findings related to accuracy, cost, and safety; and it explores issues related to cost and access as models become increasingly expensive. Finally, the survey discusses new social and ethical implications raised when using LLMs for text classification, including bias and copyright. Throughout, the review emphasizes the importance of a nuanced understanding of model performance and a holistic approach to deploying transformer-based models in real-world applications.

Read the full paper here.