Collaborative Projects

Our Approach

Are you looking for deep expertise in machine learning to help you develop cutting-edge technologies and solve complex problems in your organization?

Our scientific teams at Mila are committed to applying the most recent developments in machine learning to tackle your high-impact projects. Whether you collaborate with our Applied Machine Learning Research Team or our academic professors, knowledge and know-how transfer always remain at the heart of our approach, to ensure you build and strengthen your in-house machine learning capabilities throughout our collaboration. 

Applied Research Collaboration

Our seasoned team of machine learning experts has the mission to support organizations on their machine learning projects by developing proofs of concept in areas where the industry typically hasn’t found any solutions yet. With their privileged access to the broad network of Mila faculty members and students researchers, the team stays up-to-date on the most recent research trends and breakthroughs in machine learning. If you are looking to collaborate with top AI experts, and want to achieve mutually beneficial agreements regarding intellectual property, our Applied Machine Learning Research Team is here to help. 

Success Stories

 

Hydro-Québec

  • IREQ is a leading research institute owned by Hydro-Québec (HQ) with deep expertise in high voltage testing, mechanics and network simulations and calibration;
  • IREQ is interested in exploring opportunities and pursuing research in solar photovoltaic (PV) systems;
  • Given Hydro-Quebec must match supply and demand at all moments to guarantee a reliable and stable electricity grid, it is crucial to understand how solar irradiance can be predicted for the upcoming next few hours;
  • IREQ and Mila are collaborating on a project to understand how deep learning can help predict solar irradiance for the next 6 hours (“solar irradiance nowcasting”) in Québec and the US east coast;
  • The proposed approach is to use geostationary satellite images, in addition to the solar irradiance measurements continuously provided by several weather stations, to help better “nowcast” the solar irradiance on the entire territory of interest.

Natural Resources Canada

  • Mila is collaborating with the Geological Survey of Canada (GSC), a scientific agency within the Lands and Minerals sector of Natural Resources Canada. GSC is Canada’s national organization for geoscientific information and research on mineral exploration;
  • Predicting rock type and occurrences of mineralized deposits in the subsoil remains an important challenge in geology, due to a number of different factors – amongst others the relatively small size of the deposits, the limited number of deep geological constraints and certain limitations of the current geophysical methods used for their detection;
  • Mila and GSC are working together to explore the application of innovative machine learning tools to help predict rock types, in particular the presence of economic mineralization (i.e., minerals that can be used for economic and/or industrial purposes);
  • Available data includes a drilling database with rock types, a detailed 3D geological model constructed from drilling data and a 3D seismic dataset. The ultimate goal is to determine whether machine learning can predict rock types and the presence of mineralization from seismic data where there is no drilling information. The potential economic and environmental outcomes of predicting rock type occurrence without using drilling techniques is significant for society.

Dialogue Technologies

  • Dialogue is a virtual healthcare services provider that offers a telemedicine platform that aims to assign a user to the right practitioner based on information provided by the user in a Q&A session;
  • The sequence of questions asked during the Q&A session is not predefined nor static; it instead evolves based on the answers provided by the user;
  • Mila is working with Dialogue to design scalable and easy-to-maintain machine learning based solutions which can improve the efficiency and engagement of conversations led by the platform;
  • More specifically, the objective is to design a system that can identify the most likely pathologies of a given user and gather all relevant findings through a dialog with the smallest number of turns.

Our Team

Pierre-Luc St-Charles

Applied Research Team
Applied Research Scientist

Margaux Luck

Applied Research Team
Applied Research Scientist

Jean-Philippe Nantel

Applied Research Team
AI project manager

Joumana Ghosn

Applied Research Team
Director, Applied Research

Joseph D Viviano

Applied Research Team
Applied Research Scientist

Rishab Goel

Applied Research Team
Applied Research Scientist

Hadrien Bertrand

Applied Research Team
Applied Research Scientist

Mirko Bronzi

Applied Research Team
Applied Research Scientist

Jeremy Pinto

Applied Research Team
Applied Research Scientist

Simon Blackburn

Applied Research Team
Applied Research Scientist

Mathieu Germain

Applied Research Team
Applied Research Scientist

Pierre Luc Carrier

Applied Research Team
Applied Research Scientist

Arsene Fansi Tchango

Applied Research Team
Applied Research Scientist

Gaétan Marceau Caron

Applied Research Team
Applied Research Scientist

Fundamental Research Collaborations

Intro à venir

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