DAMLR works with companies that have a consequential AI or machine-learning problem—not simply a request to add AI to an existing product. We connect research depth with deployment realities: data, reliability, scale, evaluation, and the people who will use the system.
We collaborate with startups, established companies, public organizations, and mission-driven teams on foundational machine learning and applied AI systems. A strong partnership begins with a well-defined problem, access to the right technical context, and a shared commitment to evaluate whether the work creates genuine value.
Two ways to collaborate
Build a research project through a funding agency
This route is primarily delivered by DAMLR research students—most often PhD and master’s students—under Dr. Wu’s supervision. The company and lab define a concrete research or engineering agenda together, then prepare an application to an appropriate funding program.
- Best fit: companies that need additional engineering capacity for a well-defined research or technical objective
- Day-to-day driver: the assigned graduate researcher develops and evaluates the solution
- Faculty role: Dr. Wu supervises the researcher, guides technical decisions, and oversees delivery of the project outcomes
Program eligibility, contribution levels, review timelines, and approval are determined by the funding agency. Funding is never guaranteed.
Establish an ongoing advisor or consulting engagement
In this route, Dr. Wu works directly with the company’s engineering team and becomes deeply involved in product development. The engagement is designed to solve real business and technical problems, or establish a feasible roadmap that can lead to a successful product and company—not simply produce research.
- Direct involvement: hands-on feedback and recommendations for the company’s engineering team
- Product development: model, data, evaluation, architecture, and implementation decisions
- Business focus: practical solutions and feasible roadmaps aimed at stronger products and measurable business gains
- Minimum engagement: four months, or 17 weeks
This route is often a strong fit for post-seed startups and established technical teams that need sustained senior guidance. Advisory capacity is intentionally limited, so please reach out early to reserve time and review the scope, cadence, conflicts, and applicable university requirements.
Partnership portfolio
Company or collaborating organizationFunding agency
Obruta Space Solutions
Vision-model compression and object-pose detection for autonomous spacecraft rendezvous, proximity operations, and docking.
Harrison Healthcare
Machine learning for mental healthcare, including LLM-based personality analysis and retrieval-augmented systems for clinical knowledge.
Layer 6 · TD Bank Group
Foundational machine-learning research spanning representation learning, machine unlearning, and explainable AI.
LastMile AI
Reliable and explainable retrieval-augmented generation for trustworthy commercial applications. Mitacs Elevate has since transitioned into the Accelerate postdoctoral model.
Farpoint Technologies
Scalable, reliable AI platforms designed to serve large user populations and diverse model workloads with strong performance and cost control.
OQULi
LLM-powered document retrieval that can efficiently scan large collections and identify their semantic meaning.
Outsource Marketing
Evaluating domain-adapted language models for patent-document drafting with the goal of reducing preparation time.
UpBeing
Machine learning for emotional wellbeing, including modelling how individual emotions shift within complex social environments.
Loblaw Digital
Large-scale recommender-system research whose results inform recommendation services used in Canadian retail.
BoardyBoardy AI
Exploring agentic AI for high-context matching, trusted introductions, and more meaningful professional connections.