Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

29 670 projets achevés

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Projets par catégorie

Debugging the Brain: Reading and Writing Human Cortex using Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS)

The Temerty Centre for Therapeutic Brain Intervention aims to find new understandings of and approaches for hard-totreat mental illnesses across patients’ lifespans by delivering high quality clinical care and research using brain stimulation and psychedelic therapies. Personalized modulation of brain networks with Transcranial Magnetic Stimulation (TMS) is a promising treatment for neuropsychiatric brain disorders such a major depressive disorder. However, there is a need to
better optimize the treatment, to increase patient’s quality of life and reduce healthcare costs (Fitzgibbon et al., 2020). To do so requires the ability to reliably decode the brain activity evoked by TMS, as measured by Electroencephalography (EEG). However, it is challenging to analyze this EEG data because it has high dimensionality (an EEG recording from one patient contains thousands of data points for each of the 64 electrodes in an EEG cap) and noise (patient movements such as eye blinking can introduce artifacts in the signal (Louis et al., 2016)). EEG data analysis for TMS sessions is further complicated because, to extract relevant insights from a single trial data of EEG data, prior information on the expected signal features is required, yet currently not known (Hernandez-Pavon et al., 2023).

Voir la description complète du projet
Superviseur du corps professoral :

Arvind Gupta;Huaxiong Huang

Étudiant :

Partenaire :

Centre for Addiction and Mental Health

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Implementation of Software Features for Electrochemical Impedance Spectroscopy Analysis in Industrial Electrochemical Assets

Pulsenics will collaborate closely in this project by partnering with the intern to enhance their software platform, specifically targeting real-time Electrochemical Impedance Spectroscopy (EIS) analysis. Pulsenics faces challenges with their current EIS software, which lacks the efficiency and diagnostic accuracy needed for effective real-time monitoring, resulting in increased downtime and less-than-optimal asset management. This research aims to develop a modular solution that addresses these shortcomings by improving diagnostic accuracy and reducing maintenance interruptions.
Pulsenics is an OEM specializing in EIS solutions for R&D, production QA/QC, and real-time monitoring of industrial-scale electrochemical systems. Their integrated hardware and software platforms support high-performance measurement, data analysis, and system control across a broad range of applications—including electrolyzers, fuel cells, batteries, electrochemical water treatment, and metallurgy. Pulsenics’ technology is used globally to enable innovation, scalability, and reliability in the transition to electrochemical-based systems.
As industries rapidly scale green technologies, there is a critical need to shift from R&D-centric workflows to scalable, production-ready operations. This transition demands faster iteration cycles to develop and validate new technologies, and robust, automated QA/QC systems that derisk sales while maintaining high throughput. This project will accelerate development, streamline QA/QC, and help deploy production-scale electrochemical systems with greater confidence and less manual intervention.

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Superviseur du corps professoral :

Wai Tung Ng

Étudiant :

Partenaire :

Pulsenics

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Enhancing DAG Deployment and Testing through User-Centered Design

Geotab, the global leader in connected vehicle and asset solutions, leverages advanced data analytics and AI to enhance fleet performance, safety, and sustainability while optimizing costs. Backed by a team of industry leading data scientists, engineers, and AI experts, we serve over 50,000 customers
across 160 countries, processing billions of data points hourly from more than 4 million vehicles.
The Geotab Data Platform team is responsible for enabling and empowering the work of data scientists and developers by providing a platform for data ingestion, orchestration, digestion, and all applications of data. The sheer quantity of data that flows Geotab requires significant engineering hours working towards deployments, environment configuration, error handling, anomaly detection, and workflow performance optimizations. The goal of this project is to explore and develop automation tooling leveraging AI to increase efficiency, reduce manual effort, reduce errors, and identify ways to improve performance within the system.
Improvements to all the above makes impacts throughout Geotab, improving the quality of the solutions provided, as well as increasing the throughput of data, allowing Geotab to serve more customers and bring in more revenue.

Voir la description complète du projet
Superviseur du corps professoral :

Azadeh Farzan

Étudiant :

Partenaire :

Geotab Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

Université :

University of Toronto

Programme :

Accelerate

Structural brain connectivity in predicting treatment outcomes in major depression

(1) CAMH is a research hospital that aims to improve the provision of clinical care to clients with psychiatric conditions through research. Through analyses of large-scale multi-modal data and clinical trials with biological assessments, the Kimel TIGR lab seeks to identify biological bases of psychiatric conditions that can be targeted using existing and novel treatments, such as SSRIs, SNRIs, repeated TMS, and iTBS, among others.
(2) This project aims to a) develop novel machine learning tools that link structural brain connectivity to cognitive function and clinical symptoms in several datasets, starting with the large-scale UK Biobank data (63k participants with MRI). Additionally, b) the project will test the performance of graph neural nets alongside other machine learning tools in predicting treatment outcomes in clinical trials for depression treatment.
(3) If successful, this work may lead to wide-spread adoption of graph neural networks in neuroscience. In the longer term, if confirmed by prospective biomarker-guided clinical trials such as those that Dr. Zhukovsky is currently involved in (SMART Trial, McLean hospital with Prof Pizzagalli, Wellcome Leap funded; co-leading biomarker search as part of the BAARD trial across CAMH, Pittsburgh and Washington University St Louis, NIH funded with Dr. Felsky), biomarker-guided algorithms could help target medications for depression treatment.

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Superviseur du corps professoral :

Hans-Arno Jacobsen

Étudiant :

Partenaire :

Centre for Addiction and Mental Health

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

From Slow to Fast Thinking for LLMs

The partner, Boson AI Inc., is a leading AI solutions provider, specializing in customized model serving for businesses. Through this project, Boson AI aims to tackle key challenges in improving the reasoning capabilities of large language models (LLMs).
While techniques like Chain-of-Thought prompting have shown promise in improving LLM performance on complex tasks, they remain limited in scope and introduce significant computational and latency overhead. This project seeks to improve reasoning efficiency by leveraging recent advances in LLM fine-tuning and alignment, enabling faster, more efficient inference without compromising reasoning accuracy.
The anticipated benefits for Boson AI include significant cost savings, higher customer satisfaction, and enhanced regulatory compliance. Ultimately, the project’s success is expected to strengthen Boson AI’s position as a leader in scalable and efficient AI systems, while advancing its broader research strategy in next-generation generative models.

Voir la description complète du projet
Superviseur du corps professoral :

Xujie Si

Étudiant :

Partenaire :

Boson AI

Discipline :

Computer science

Secteur :

Education; Information and cultural industries

Université :

University of Toronto

Programme :

Accelerate

Collision (or crash) Severity Inference

Geotab is a global leader in IoT and connected transportation, providing valuable insights to over 80,000 customers worldwide by collecting more than 4 billion data points daily. The Safety & Video Analytics team at Geotab currently detects vehicle collisions using in-house machine learning models on telematics data. However, the severity of crashes is currently assessed solely through g-force measurements, which is not always reliable.
This project aims to develop a more sophisticated method to infer crash severity using additional data sources, such as kinematic data, contextual data lakes, and external crash databases (e.g., Federal Motor Carrier Safety Administration – FMCSA). The new methodology will help in collision reconstruction, insurance claim processing, and overall vehicle safety analysis. By providing a more precise and data-driven severity assessment, Geotab can enhance its services for customers and insurance partners, leading to better risk evaluation, pricing, and claims management. The success of this project would help Geotab provide more contextual information about a crash to the customers that can be used for planning and for litigation purposes. This will also help us build more strategic partnerships by providing more value to our insurance partners

Voir la description complète du projet
Superviseur du corps professoral :

Andrei Badescu

Étudiant :

Partenaire :

Geotab Inc

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

Université :

University of Toronto

Programme :

Accelerate

Pollution control of gas mixtures: gas monitoring and detection of contaminants usingnovel THz technology

Electric power plants are the number one toxic air polluters in North America. The emitted pollutants are proven to cause serious health and environmental issues. The emission of Carbon dioxide and of other pollutants, such as nitrogen oxides, sulfur dioxide – major drivers of the human-accelerated global climate change- must be monitored insitu. Our goal for the present project is to explore the properties of Terahertz radiation for control of pollution in the atmosphere. In particular, we intend to develop a new waveguide-integrated gas monitor, based on Bragg grating sensors. Such devices, whose sensitivity will be increased through modulation via an external magnetic field, will be based on probing induced anisotropy. The integration of the proposed sensor into the existing line of products of our industrial partner, QPS Photronics, can potentially result in a versatile tool for industrial applications, which could give the company a leading edge over the competition.

Voir la description complète du projet
Superviseur du corps professoral :

Roberto Morandotti

Étudiant :

Partenaire :

QPS Photronics Inc

Discipline :

Physics

Secteur :

Health and Related Sciences & Technology; Manufacturing

Université :

Université du Québec : Institut national de la recherche scientifique

Programme :

Elevate

Agent Learning in SLMs

The partner organization Layer 6 AI is a leading artificial intelligence research lab and a part of TD Bank Group. It focuses on advancing machine learning and deep learning technologies to drive innovation across various sectors including financial services. The company specializes in AI-driven solutions such as predictive modeling, recommendation systems, and natural language processing. With a commitment to cutting-edge research, Layer 6 collaborates with academia and industry to push the boundaries of AI applications.
In the financial services industry, a key challenge lies in managing and retrieving information from vast and heterogeneous knowledge repositories. Furthermore, operational efficiency hinges on automating complex, multi-step workflows. Large language models have the capacity to independently plan, reason, and interact with various tools, which provides a significant opportunity to streamline knowledge management and enhance workflow automation, all while operating within the necessary compliance boundaries.
Through this collaboration, the partner stands to gain strategic benefits. The insights from agent learning in small language models could dramatically improve workflow automation, reducing operational overhead and improving service quality. The project will also help the partner develop in-house expertise on cutting-edge AI techniques, enabling faster and more efficient deployment of language-driven solutions across multiple business units.

Voir la description complète du projet
Superviseur du corps professoral :

Rahul G. Krishnan

Étudiant :

Partenaire :

Layer 6 AI

Discipline :

Computer science

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Building a semi-supervised machine learning model to predict biomolecular condensates

Princess Margaret Cancer Centre belongs to the University Health Network (UHN), Canada’s leading biomedical research organization. The Centre focuses on cancer research across various fields, including genomics, informatics, signaling, health services, and biophysics.
Dr. Kumar’s lab is currently investigating the consequences of genomic alterations in intrinsically disordered regions (IDR). IDRs are present in proteins that undergo liquid-liquid phase separation (LLPS) and form biomolecular condensates [1]. IDRs lack a fixed structure yet play vital roles in cellular function [2]-[4]. Genetic alterations can disrupt biomolecular condensate activity, leading to neurodevelopmental disorders and cancer [5]. Despite their biological significance, IDRs are often overlooked in drug discovery. Current experimental methods to identify IDRs lack throughput. Therefore, this project aims to address these challenges by developing an advanced in-silico approach to predict LLPS proteins.
As a research assistant, the intern will take on this project and contribute to cutting-edge computational research. If successful, this in-silico approach could significantly reduce reliance on costly experimental procedures while accelerating breakthroughs in precision medicine. By enhancing predictive efficiency, this computational technique could open new avenues for drug discovery, enabling the targeted intervention in disordered proteins. Furthermore, identifying key LLPS proteins could provide deeper insights into cancer biology.

Voir la description complète du projet
Superviseur du corps professoral :

Alan Moses;Karthik Kuber

Étudiant :

Partenaire :

University Health Network

Discipline :

Computer science

Secteur :

Health and Related Sciences & Technology

Université :

University of Toronto

Programme :

Accelerate

Scaling Tabular-Timeseries Foundation Models for Large-Scale Financial Data

TD Bank, as a leader in financial services, relies on predictive modeling to improve customer insights, risk management, and fraud detection. However, current machine learning approaches struggle to scale effectively across TD’s vast transactional datasets, leading to challenges in handling heterogeneous financial products, long-term forecasting, and multi-task learning. This project aims to address these challenges by developing scalable tabular-timeseries transformer architectures capable of learning from hundreds of millions of transactions across diverse financial services. By integrating self-supervised learning and multi-task optimization strategies, TD Bank will benefit from:
(1) Improved predictive performance in account acquisition, customer retention, fraud detection, and delinquency
prediction.
(2) Reduced model fragmentation, allowing a single scalable model to handle multiple financial objectives.
(3) Operational efficiency, decreasing computational costs by consolidating several models into a unified framework.
(4) Improved explainability, ensuring compliance with financial regulations and risk assessments.
This research aligns with TD’s commitment to AI innovation and financial technology advancements, providing direct business value through AI-driven decision-making and enhanced risk management.

Voir la description complète du projet
Superviseur du corps professoral :

Scott Sanner

Étudiant :

Partenaire :

Layer 6 AI

Discipline :

Computer science

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Venn 2025 Summer Intern

At Venn, we’re not just building a product—we’re revolutionizing how Canadian businesses operate. As a rapidly growing fintech startup serving over 3,000 Canadian businesses, our ambition is to streamline our internal processes as we scale towards supporting over 10,000 companies. To achieve this, we’re looking for a motivated individual to take ownership of high-impact projects that eliminate manual processes and drive measurable operational efficiency.

Voir la description complète du projet
Superviseur du corps professoral :

Bertrand Malsch

Étudiant :

Partenaire :

Venn

Discipline :

Business

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

Queen's University

Programme :

Business Strategy Internship

Scaling USA-NPN Sampling Design using LLM Agents

This project, part of the Global AI Alliance for Climate Action, aims to improve how the USA National Phenology Network (USA-NPN) collects and balances seasonal plant and animal data. By using large language models (LLMs) and AI-driven workflows, the project will help guide citizen scientists toward underrepresented species and locations, addressing gaps in the current dataset. This will make USA-NPN’s data more complete and useful for tracking climate change impacts. For Vector Institute, this collaboration showcases AI’s role in solving real-world environmental challenges, reinforcing its leadership in responsible AI innovation.

Voir la description complète du projet
Superviseur du corps professoral :

Graham Taylor

Étudiant :

Partenaire :

Vector Institute

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

University of Guelph

Programme :

Business Strategy Internship

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