CARTE Initiatives


Enhancing new and established industries through analytics and AI


The primary goal of CARTE Seed is to bring together a pair of faculty researchers with complementary expertise within FASE to build a high-impact project that will eventually secure large-scale external research funding. CARTE Seed funded projects are substantial initiatives co-funded by the Departments of the respective researchers.

2021 Awards

We are pleased to announce the successful projects for 2021 CARTE Seed Funding.

  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 and Industrial Engineering)

  2.  Analysis of Computer-Aided Design Data: Applying Software Development Principles to Physical Product Design
    Alison Olechowski (Department of Mechanical and Industrial Engineering)
    Shurui Zhou (Department of Electrical and 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 and 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)


Past results

The successful applications for the first round of CARTE Seed funding were announced in March 2020. We congratulate the successful applicants and look forward to seeing the outcomes of their innovative work.

Projects awarded CARTE Seed funding

  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 and Industrial Engineering)
    Yu Zou (Department of Materials Science and 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 and Industrial Engineering)
    Angela Schoellig (University of Toronto Institute for Aerospace Studies)


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

  1. Imitation and reinforcement learning for gait training of lower-limb prosthesis users
    Fae Azhari (Department of Mechanical and Industrial Engineering; Department of Civil & Mineral Engineering)
    Josh Taylor (Department of Electrical and Computer Engineering)

ML Bootcamp

An important goal of CARTE is to advance the incorporation of analytics and AI in research, education and industrial development, and thus contribute to building Toronto’s international reputation as a major hub in this sector. A week-long bootcamp has been designed to broaden the capacity of engineering and other faculty at the University. The first bootcamp took place in August 2019. Please contact CARTE to express interest to participate at the second bootcamp planned for August 2020.


© 2020 Faculty of Applied Science & Engineering