Grants and Contributions

Bladder cancer is the 6th most common cancer in men and the 17th most common cancer in women globally. Early diagnosis of bladder cancer is very important as it is associated with a higher survival rate, for both men and women. There is currently no clinically relevant detection test for bladder cancer. The goal of the Firm's bladder cancer project is to develop a highly accurate test that can detect clinically significant bladder cancer using machine learning and our extracellular vesicle (EV) detection platform. Similar to our successful prostate project we will first validate our selected bladder cancer biomarkers and then incorporate relevant clinical features using machine learning algorithms to create a risk score of disease.

A software development project that delivers an AI enabled esports tournament management platform with moderation and support system included so that players and organizers can experience tournament formats that are optimal to their preferences resulting in less interventions from humans and a better experience for organizers and players.

Create a new purpose-built compact, cost-effective camera that is low-power, connected and rugged for construction, with Computer Vision analysis for Productivity and Safety.

How to efficiently distribute, evaluate, rank, and then display 100-1000+ retirement decumulation strategies in under 2-3 seconds at scale

Develop an International co-innovation plan with a UK partner, a manufacturer of power systems for construction, for further collaboration to displace diesel power generators with cost competitive hydrogen fuel cell systems.

Implementation of a new eCommerce platform.

The project aims to develop a reference instrument based on
photothermal interferometry (PTI) that enables direct measurement of aerosol light absorption and will be augmented with a light scattering component. This approach is expected to lower uncertainties in aerosol light absorption and scattering measurements and to tighten the bounds on the effects of aerosols in climate models.

The project will support development of intellectual property capacity within the firm.

Implementation of software modifications and cybersecurity processes.

Organic semiconductors have been intensely investigated in the last two decades because of their wide variety of prominent commercial applications. The device fabrication process and performance heavily depend on the inherent properties of organic semiconductors. In this project the McGill team will collaborate with a team from the NRC and from NYMCTU on developing and applying machine learning (ML) methods and models to condense the available data on organic semiconductors into a ‘computational compass’ to guide the optimization of materials needed for high-performing, stable, and ‘green’ organic photovoltaic (OPV) and thermoelectric (TE) devices. This goal will be achieved by combining datadriven ML approaches with physics-based (but ML-enhanced) simulation that will fill in the details of manifestation of structural diversity and help encode the necessary morphology-function relationships into generative ML models which will be used to propose hypothetical materials. Synthesis, purification, and characterization protocols will be developed for the experimental realization of these novel materials. The work will be done in a close collaboration between the McGill and the NRC teams with HQP visiting the NRC facilities regularly and with the computational effort supported by the experts on the NRC team. The outcomes expected from this work include machine learning models for design of novel OPV and TE materials, new synthesis, purification, and characterization protocols, and ultimately new OPV and TE devices.