Faculty research
Research questions led by Northeastern faculty across AI, software, data, and engineering.
Experiment · evaluate · implement
Research & Innovation
SEIS AI Lab creates room for practical research: questions tested with real systems, real constraints, and collaborators who care about what happens after the prototype.
Areas of work
Research that stays close to implementation.
New projects can be added here as they become public. The structure below keeps faculty, student, applied, and experimental work visible without overclaiming.
Student research
Paths for students to contribute through independent work, courses, capstones, and research groups.
Applied AI projects
Projects that connect a concrete domain problem with data, models, evaluation, and deployment.
Emerging technologies
Focused experiments with language models, agents, multimodal systems, and new AI tooling.
Interdisciplinary research
Collaborations that bring AI methods into conversation with domain expertise and lived needs.
AI infrastructure
Practical work on compute, data, evaluation, LLMOps, and the systems that support experimentation.
A research loop
Question to working evidence.
Research and implementation reinforce one another. A useful experiment makes its assumptions visible, measures what changed, and leaves behind something another person can build on.
- Frame a question with a real use case.
- Build a small, testable experiment.
- Evaluate behavior, limitations, and tradeoffs.
- Share artifacts, methods, and next questions.
Looking for a research connection?
Students, faculty, researchers, and collaborators can use the contact pathways to describe a question, capability, or project that could benefit from practical AI work.
Start a conversation →