CARTE Research

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Research Support

Facilitating collaborative applied research in Artificial Intelligence (AI) involving external partners, faculty members and students

CARTE capitalizes on Toronto's position as a world-class research powerhouse in analytics and artificial intelligence (A/AI) and its close proximity to a wide range of industry clusters, from biomedical technology to advanced manufacturing.

We support basic research in analytics and artificial intelligence (A/AI) methodologies that may have broad applications, by driving collaborative research between technical A/AI experts and those in other domains. We catalyze the launch of large-scale multi-investigator research partnerships and provide support to match funds from government sources.

Learn more about our research support services for:

Prospective external partners
Faculty members
University of Toronto students

CARTE Affiliate Faculty Research Portfolio

Professor Chandra Veer Singh uses high throughput simulations, machine learning, and available experimental data to enable design of high entropy alloys for structural applications

Professors Yu Zou and Chi-Guhn Lee are integrating Additive Manufacturing and AI to develop a “self-driving 3D printing system” that senses/predicts defects and corrects processing parameters adaptively

Professor Deepa Kundur is developing an Integrated Co-Simulation and AI-based data analytics platform for smart grid modeling and cybersecurity analysis

Professor Ben Liang applies machine learning to categorize the network traffic and optimally balance the trade-off between accuracy, privacy, cost, and delay

Professors Babak Taati and Andrea Laboni work on ambient monitoring of gait in long-term care facilities to automatically identify residents who are at a high risk of falling

External Partners

Are you currently working in industry, government or the non-profit sector and want to sponsor applied research in your field? We facilitate research collaborations with our faculty affiliates and research staff. We also provide support to leverage funds through government sources such as NSERC Alliance and MITACS . Contact us to learn more. If you have an applied AI research project but don’t have the funding to sponsor research, contact us to learn about opportunities to have upper-year undergraduate students work on these projects as capstone design projects.

Faculty Members

Are you a faculty member conducting research in a specific domain and are looking for AI experts to lead the technical aspects of a research project and provide guidance to your students? Contact us to learn more about our research support services on a cost-recovery basis. Looking for a student research assistant, too? Make sure you ask about our CV bank of Master of Engineering students looking for research project opportunities.

Past collaborations have produced high-quality publications and presentations, including:

Mendell, A. Y., Olson, A. W., & Siegel, J. A. (2022). Evaluation of fixed and adaptive concentration thresholds for particle filter systems. Indoor air, 32(10), e13134.
Huang, W., Olson, A., Khalil, E. B., & Saxe, S. (2022, June). Note: Image-based Prediction of House Attributes with Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (pp. 693-695).
Guven, G., Arceo, A., Bennett, A., Tham, M., Olanrewaju, B., McGrail, M., Isin, K., Olson, A.W. and Saxe, S., 2022. A construction classification system database for understanding resource use in building construction. Scientific Data, 9(1), pp.1-12.
Huang, W., Olson, A., Khalil, E., & Saxe, S. (2022, June 29–July 1). Image-based Prediction of House Attributes with Deep Learning [Poster]. ACM SIGCAS Conference on Computing and Sustainable Societies, Seattle, WA.
Raju, S., Olson, A., Deghan, S., Eisenberg, N., Chan, T.C.Y., and Roche-Nagle, G. (2022, August 18-21). Utilizing machine learning algorithms to evaluate sex-based differences in preoperative hemoglobin thresholds in open vascular surgery [Poster]. SVS 2021 Annual Vascular Meeting, San Diego, CA.

University of Toronto Students

Are you a University of Toronto engineering student looking for technical guidance on your AI research projects? Book a free AI research drop-in clinic with a CARTE senior research associate using your University of Toronto email address.

Are you a University of Toronto engineering student looking to work on applied AI research projects in industry settings? Look no further than current MITACS opportunities for internships in Analytics/AI/Machine Learning (updated frequently).

Are you a University of Toronto Master of Engineering student looking for research project opportunities in Analytic, AI, and Machine Learning? Send us your CV to be added to our CV bank.

CARTE Seed Research Funding

In 2020 and 2021, CARTE funded multi-disciplinary, high-impact projects that connected pairs of faculty researchers with complementary expertise.

2021 CARTE Seed Recipients

1. Exploring the Applications of Natural Language Processing in Engineering Education Research

Chirag Variawa (Institute for Studies in Transdisciplinary Engineering Education and Practice) Sinisa Colic (Department of Mechanical & Industrial Engineering)

2. Analysis of Computer-Aided Design Data: Applying Software Development Principles to Physical Product Design

Alison Olechowski (Department of Mechanical & Industrial Engineering) Shurui Zhou (Department of Electrical & Computer Engineering)

3. Analytics for Selective Mining using Sensor-based Data

Kamran Esmaeili (Department of Civil & Mineral Engineering and Lassonde Institute of Mining) Mariano Consens (Department of Mechanical & Industrial Engineering)

4. Enabling Combined Planning and Reinforcement Learning Robot Behaviours to Automate Structured Tasks

Jonathan Kelly (University of Toronto Institute for Aerospace Studies) Florian Shkurti (University of Toronto Mississauga Computer Science)

2020 CARTE Seed Recipients

1. Autonomous additive manufacturing system (AAMS): a novel in-situ monitoring and closed-loop control process using machine learning

Chi-Guhn Lee (Department of Mechanical & Industrial Engineering)
Yu Zou (Department of Materials Science & Engineering)

2. Closed-loop artificially intelligent fiber-selective peripheral nerve interface for neuroprosthetic applications

Roman Genov (Department of Electrical and Computer Engineering)
José Zariffa (Institute of Biomaterials and Biomedical Engineering)

3. Giving robots a sense of touch: Safe, high-performance robot manipulation combining novel skin-like sensors with high-rate, learning-based feedback control

Xinyu Liu (Department of Mechanical & Industrial Engineering)
Angela Schoellig (University of Toronto Institute for Aerospace Studies)

Projects awarded CARTE Seed funding and co-funded by the Centre for Healthcare Engineering

Selected Publications from CARTE Seed Projects

Xing, W., Lyu, T., Chu, X., Rong, Y., Lee, C.G., Sun, Q. and Zou, Y., 2021. Recognition and classification of single melt tracks using deep neural network: A fast and effective method to determine process windows in selective laser melting. Journal of Manufacturing Processes, 68, pp.1746-1757.
Xing, W., Chu, X., Lyu, T., Lee, C.G., Zou, Y. and Rong, Y., 2022. Using convolutional neural networks to classify melt pools in a pulsed selective laser melting process. Journal of Manufacturing Processes, 74, pp.486-499. · M. ElAnsary, J. Xu, J.S. Fihlo, G. Dutta, L. Long, C. Tejeiro, A. Shoukry, C. Tang, E. Kilinc, J. Joshi, P. Sabetian, S. Unger, J. Zariffa, P. Yoo, R. Genov, “Bidirectional peripheral nerve interface with 64 2nd-order opamp-less ∆∑ ADCs and fully-integrated wireless power/data transmission”, IEEE Journal of Solid-State Circuits, 56(11): 3247-3262, 2021.
M. El Ansary, J. Xu, J. Sales Filho, G. Dutta, L. Long, A. Shoukry, C. Tejeiro, C. Tang, E. Kilinc, J. Joshi, P. Sabetian, S. Unger, J. Zariffa, P. Yoo, R. Genov, “Multi-Modal Peripheral Nerve Active Probe and Microstimulator with On-Chip Dual-Coil Power/Data Transmission and 64 2nd-Order Opamp-Less ΔΣ ADCs”, International Solid-State Circuits Conference, February 13-22 2021, virtual.
R. Zuo, Z. Zhou, B. Ying, and X.Y. Liu, “A soft robotic gripper with anti-freezing ionic hydrogel-based sensors for learning-based object recognition,” IEEE International Conference on Robotics and Automation (ICRA’21), Xi’an, China, May 30-June 5, 2021.
Y. E. Hwang, R. Genov, J. Zariffa, "Reducing convolutional neural network architectures for selective peripheral nerve recording on implantable devices", Society for Neuroscience Annual Meeting, Nov, 8-11, 2021 (virtual).