Our graduates

Companies

Our graduates work at these companies (in alphabetical order)

Theses (38)

2024

  1. Stewart, Connor Raymond, "Traffic Rule Checking and Validation", MMath.

2023

  1. Mannes, Christopher Gus, "Sparse2SOAP: Domain Adaptation for LiDAR-Based 3D Object Detection", MMath.

  2. Bhattacharyya, Prarthana, "Perception and Prediction in Multi-Agent Urban Traffic Scenarios for Autonomous Driving", PhD.

  3. Rowe, Luke, "FJMP: Factorized Joint Multi-Agent Motion Prediction". MMath.
    - Thesis presentation video

  4. Therien, Benjamin, "Towards Object Re-identification from Point Clouds for 3D MOT", MMath.

2022

  1. Nguyen, Van Duong (Harry), "Out-of-Distribution Detection for LiDAR-based 3D Object Detection", MASc.
  2. Gu, Sunsheng, "XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection", MASc.
    - Thesis presentation video
  3. Pitropov, Matthew, "LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection", MASc.
  4. Larter, Scott, "A Hierarchical Pedestrian Behaviour Model to Reproduce Realistic Human Behaviour in a Traffic Environment", MMath
    - Thesis presentation video
  5. Sarkar, Atrisha, "Empirical Game Theoretic Models for Autonomous Driving: Methods and Applications", PhD.
  6. Queiroz, Rodrigo, "Scenario Modeling and Execution for Simulation Testing of Automated-Driving Systems", PhD.

2021

  1. Kahn, Maximilian, "Dynamic-Occlusion-Aware Risk Identification for Autonomous Vehicles Using Hypergames", MMath.
    - Thesis presentation video

2020

  1. Balakrishnan, Aravind, "Closing the Modelling Gap: Transfer Learning from a Low-Fidelity Simulator for Autonomous Driving", MMath.
  2. Chen, Henry, "Autonomous Vehicles with Visual Signals for Pedestrians: Experiments and Design Recommendations", MMath.
  3. Chen, Wei Tao, "Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning", MMath.
  4. Denouden, Taylor, "An Application of Out-of-Distribution Detection for Two-Stage Object Detection Networks", MMath.
  5. Gaurav, Ashish, "Safety-Oriented Stability Biases for Continual Learning", MMath.
  6. Ilievski, Marko, "WiseBench: A Motion Planning Benchmarking Framework for Autonomous Vehicles", MMath.
  7. Bouchard, Frédéric, "Expert System and a Rule Set Development Method for Urban Behaviour Planning", MMath.
  8. Valov, Pavel, "Transferring Pareto Frontiers across Heterogeneous Hardware Environments", PhD.
  9. Vernekar, Sachin, "Training Reject-Classifiers for Out-of-distribution Detection via Explicit Boundary Sample Generation", MMath.

2019

  1. Angus, Matt, "Towards Pixel-Level OOD Detection for Semantic Segmentation", MMath.
    - URSA project.
  2. Balasubramanian, Venkateshwaran, "3D Online Multi-Object Tracking for Autonomous Driving", MMath.
  3. Chao, Edward, "Autonomous Driving: Mapping and Behavior Planning for Crosswalks", MASc.
  4. Deng, Jian, "MLOD: A multi-view 3D object detection based on robust feature fusion method", MMath.
  5. Dillen, Nicole, "Passenger Response to Driving Style in an Autonomous Vehicle", MMath.
  6. Hurl, Braden, "Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets", MMath.
    - PreSIL project.
    - TruPercept project.
  7. De Iaco, Ryan, "Motion Planning and Safety for Autonomous Driving", MASc.
  8. Jhunjhunwala, Aman, "Policy Extraction via Online Q-Value Distillation", MMath.
  9. Khan, Samin, "Towards Synthetic Dataset Generation for Semantic Segmentation Networks", MASc. 
    - URSA project.
    - ProcSY project.
  10. Li, Changjian, "Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach", MASc.
  11. Masud, Zarif, "Switching GAN-based Image Filters to Improve Perception for Autonomous Driving", MMath.
  12. Phan, Buu Truong, "Bayesian Deep Learning and Uncertainty in Computer Vision", MAsc.
    - ProcSY project.

2018

  1. Chandail, Rahul, "Vision Augmented State Estimation with Fault Tolerance", MASc.
  2. Colwell, Ian, "Runtime Restriction of the Operational Design Domain: A Safety Concept for Automated Vehicles", MASc.
  3. Liang, Jia Hui (Jimmy), "Machine Learning for SAT Solvers", PhD.
  4. Zulkoski, Edward, "Understanding and Enhancing CDCL-based SAT Solvers", PhD.

2016

  1. Sarkar, Atrisha, "Meta-learning Performance Prediction of Highly Configurable Systems: A Cost-oriented Approach", MMath.

Past graduates of Generative Software Development Lab

Twenty eight lab graduates from 2016 and before are listed on GSD Lab Members page.

Their theses are listed on GSD Lab Publications page.