Partners

The Applied Machine Learning Research Lab envisions a transformative role in empowering start-up companies and medium enterprises by providing specialized AI/ML systems tailored for scalability, reliability, and performance enhancement. Recognizing the unique challenges these businesses face, the lab offers cutting-edge research and practical solutions that enable seamless integration and optimization of machine learning models within their operations. By leveraging advanced algorithms and robust data analytics, the lab aims to enhance decision-making processes, drive innovation, and foster sustainable growth. The lab’s commitment to continuous improvement and adaptive learning ensures that the AI/ML systems evolve alongside the businesses, meeting their dynamic needs and positioning them for competitive advantage in the rapidly changing technological landscape.

We are seeking partnerships with industry, environmental protection, and the public welfare sectors for 1) theoratical machine learning research, 2) landing AI system applications. If you are interested in partnering with us, please contact Dr. Ga Wu.

Our collaborators

  - Accelerate

We partner with UpBeing for machine learning driven mental healthcare research, including understanding/predicting individual emotion shifting given complex social environments.

  |     - Accelerate

We partner with Layer6 AI (TD research institute) for theoratical machine learning research, including data representation learning, machine unlearning, and explainable AI.

  - Accelerate

We partner with LastMile AI for advancing Retrieval Augmented Generation (RAG) research, aiming to achieve reliable and explainable RAG system for trustworthy commercial applications.

We collaborate with Loblaw Digital to advance research on large-scale recommender systems. The results of our work are directly reflected in the recommendation service of Canada’s largest supermarket chain.

  - BSI

We partner with Farpoint Technologies Inc for enhancing scalable and reliable AI platform by building a solution that can handle a large volume of users and deverse AI models, ensuring high performance and cost-effectiveness.

  - PIVP

We help OQULi for enhancing their Large Language Model powered document retrieval system by building a solution that can efficiently scan and identify sematic meaning of large amount of documents.

  - IRAP

We are collaborating with Outsource Marketing to verify the feasibility of using large language models to generate patent documents (in terms of professional domain adaptation). The success of this research will significantly reduce the time it takes to prepare patent application documents.