University of Toronto
Carte

Applied AI Research
That Translates

Carte supports collaborative research designed for real-world impact — bridging AI with civil engineering, healthcare, sustainability, and beyond. We help partners navigate funding and connect with faculty who can solve their specific challenges.

Featured Publications

Carte-supported research solving real engineering challenges — from building science to healthcare to sustainability.

Computer VisionUrban Infrastructure

Single-Image Building Height Estimation Using EfficientNet

A simplified, scalable approach to estimating building heights from single street-level images using deep learning, enabling rapid urban infrastructure assessment.

Deep LearningSustainability

Image-based Prediction of Residential Building Attributes with Deep Learning

Leveraging deep learning to predict residential building characteristics from imagery, supporting sustainable housing analysis and urban planning decisions.

AI SafetyVision-Language Models

FLODA: Harnessing Vision-Language Models for Deepfake Assessment

A novel approach using vision-language models to detect and assess deepfake media, advancing AI safety and digital content authenticity.

Research Portfolio

Collaborative research across 8 engineering departments — with a focus on practical applications and real-world impact.

5 publications
InfrastructureData

A Construction Classification System Database for Understanding Resource Use in Building Construction

A comprehensive database enabling researchers to analyze resource consumption patterns in construction.

Healthcare AIClinical

Utilizing Machine Learning to Evaluate Sex-based Differences in Preoperative Hemoglobin Thresholds

Applying ML algorithms to identify sex-based differences in surgical thresholds for improved patient outcomes.

Air QualityBuilding Science

Evaluation of Fixed and Adaptive Concentration Thresholds for Particle Filter Systems

Research on optimizing air filtration systems through adaptive threshold algorithms for better indoor air quality.

Computer VisionSustainability

Note: Image-based Prediction of House Attributes with Deep Learning

A conference note demonstrating how deep learning models can infer key residential house attributes directly from imagery, laying the groundwork for scalable, data-driven housing and sustainability analysis.

Machine LearningUser BehaviorImbalanced Data

Predicting Interview Engagement in Real-Time Recruitment: Class Imbalance and Behavioral Feature Analysis

Machine learning models for predicting candidate interview engagement on a real-time recruitment platform under extreme class imbalance, highlighting the importance of behavioral and temporal features alongside algorithmic match scores.

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Whether you're a faculty member seeking AI expertise, an industry partner looking to sponsor research, or a student interested in applied AI projects—we're here to help.

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