Companies
Our graduates work at these companies (in alphabetical order):
- Algolux
- Amazon
- Aptiv
- AutonomouStuff
- Avidbots
- Boosted.ai
- Borealis AI (2)
- Cerebri.ai
- Gatik (2)
- Google Kitchener-Waterloo
- Hakai Institute
- Humanising Autonomy
- NVidia
- TC Energy
- Qualcomm
- Quantstamp, Inc.
- RoboEye (4)
- Waymo
Theses (36)
2023
-
Mannes, Christopher Gus, "Sparse2SOAP: Domain Adaptation for LiDAR-Based 3D Object Detection", MMath.
-
Rowe, Luke, "FJMP: Factorized Joint Multi-Agent Motion Prediction". MMath.
- Thesis presentation video -
Therien, Benjamin, "Towards Object Re-identification from Point Clouds for 3D MOT", MMath.
2022
- Nguyen, Van Duong (Harry), "Out-of-Distribution Detection for LiDAR-based 3D Object Detection", MASc.
- Gu, Sunsheng, "XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection", MASc.
- Thesis presentation video - Pitropov, Matthew, "LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection", MASc.
- Larter, Scott, "A Hierarchical Pedestrian Behaviour Model to Reproduce Realistic Human Behaviour in a Traffic Environment", MMath
- Thesis presentation video - Sarkar, Atrisha, "Empirical Game Theoretic Models for Autonomous Driving: Methods and Applications", PhD.
- Queiroz, Rodrigo, "Scenario Modeling and Execution for Simulation Testing of Automated-Driving Systems", PhD.
2021
- Kahn, Maximilian, "Dynamic-Occlusion-Aware Risk Identification for Autonomous Vehicles Using Hypergames", MMath.
- Thesis presentation video
2020
- Balakrishnan, Aravind, "Closing the Modelling Gap: Transfer Learning from a Low-Fidelity Simulator for Autonomous Driving", MMath.
- Chen, Henry, "Autonomous Vehicles with Visual Signals for Pedestrians: Experiments and Design Recommendations", MMath.
- Chen, Wei Tao, "Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning", MMath.
- Denouden, Taylor, "An Application of Out-of-Distribution Detection for Two-Stage Object Detection Networks", MMath.
- Gaurav, Ashish, "Safety-Oriented Stability Biases for Continual Learning", MMath.
- Ilievski, Marko, "WiseBench: A Motion Planning Benchmarking Framework for Autonomous Vehicles", MMath.
- Bouchard, Frédéric, "Expert System and a Rule Set Development Method for Urban Behaviour Planning", MMath.
- Valov, Pavel, "Transferring Pareto Frontiers across Heterogeneous Hardware Environments", PhD.
- Vernekar, Sachin, "Training Reject-Classifiers for Out-of-distribution Detection via Explicit Boundary Sample Generation", MMath.
2019
- Angus, Matt, "Towards Pixel-Level OOD Detection for Semantic Segmentation", MMath.
URSA project. - Balasubramanian, Venkateshwaran, "3D Online Multi-Object Tracking for Autonomous Driving", MMath.
- Chao, Edward, "Autonomous Driving: Mapping and Behavior Planning for Crosswalks", MASc.
- Deng, Jian, "MLOD: A multi-view 3D object detection based on robust feature fusion method", MMath.
- Dillen, Nicole, "Passenger Response to Driving Style in an Autonomous Vehicle", MMath.
- Hurl, Braden, "Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets", MMath.
PreSIL project.
TruPercept project. - De Iaco, Ryan, "Motion Planning and Safety for Autonomous Driving", MASc.
- Jhunjhunwala, Aman, "Policy Extraction via Online Q-Value Distillation", MMath.
- Khan, Samin, "Towards Synthetic Dataset Generation for Semantic Segmentation Networks", MASc.
URSA project.
ProcSY project. - Li, Changjian, "Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach", MASc.
- Masud, Zarif, "Switching GAN-based Image Filters to Improve Perception for Autonomous Driving", MMath.
- Phan, Buu Truong, "Bayesian Deep Learning and Uncertainty in Computer Vision", MAsc.
ProcSY project.
2018
- Chandail, Rahul, "Vision Augmented State Estimation with Fault Tolerance", MASc.
- Colwell, Ian, "Runtime Restriction of the Operational Design Domain: A Safety Concept for Automated Vehicles", MASc.
- Liang, Jia Hui (Jimmy), "Machine Learning for SAT Solvers", PhD.
- Zulkoski, Edward, "Understanding and Enhancing CDCL-based SAT Solvers", PhD.
2016
- Sarkar, Atrisha, "Meta-learning Performance Prediction of Highly Configurable Systems: A Cost-oriented Approach", MMath.