BEng Mechatronics, McMaster University, Ontario, Canada
Research interestsautonomous vehicles perception and object classification deep learning neural nets
Samin works on semantic segmentation of images for autonomous driving.
BSc Electrical and Computer Engineering, University of New Brunswick, New Brunswick, Canada
Research interestsAutonomous vehicles Software and hardware architectures for cyber-physical systems Robotics
BASc Electrical and Computer Engineering, University of Windsor, Ontario, Canada
Rahul works on visual odometry and sensor fusion.
Sarkar, Atrisha, Kate Larson, and Krzysztof Czarnecki. "Generalized dynamic cognitive hierarchy models for strategic driving behavior." arXiv preprint arXiv:2109.09861, 2022 AAAI Conference on Artificial Intelligence (AAAI 2022).
BSc Computer Science, Algorithms and Complexity Theory, University of Calgary, Minor in Pure Mathematics
Matt works on semantic segmentation of images for autonomous driving.
InterestsScalable verification and accelerated learning active deep learning for perception in autonomous driving statistical verification of stochastic and nondeterminsitic systems safe reinforcement learning and formal verification of machine-learned systems verification and optimisation of hybrid and timed systems accelerated simulation for rare event verification accelerated learning for prediction of rare events Behaviour modelling, planning and prediction
MASc student, University of Waterloo (September 2019 - February 2022)
Research engineer, Autonomoose.net (July 2017 - August 2019)
Pitropov, M., Garcia, D.E., Rebello, J., Smart, M., Wang, C., Czarnecki, K. and Waslander, S., 2021. Canadian adverse driving conditions dataset. The International Journal of Robotics Research, 40(4-5), pp.681-690.
Rick Salay, PhD, is a systems engineering researcher with broad expertise related to safety, uncertainty, machine-learning and modeling. He has conducted and led internationally recognized research on these topics with major industrial partners and has published over 75 peer-reviewed papers. For the past 5 years he has worked in the Waterloo Intelligent Systems Engineering Lab at University of Waterloo as part of a team developing innovative approaches to the safety of deep neural network based perception in automated driving systems.
Vahdat Abdelzad is a Postdoctoral Fellow focusing on the safety aspects of machine learning models. He is studying the safety in terms of out of distribution detection for deep neural networks, explainable artificial intelligence, and active learning.
Prior affiliations2015.09 - 2017.12 Postdoctoral Fellow at the Reinforcement Learning and Artificial Intelligence Lab. at the University of Alberta, AB, Canada.
Selected publicationsConstrained RL for safety-critical systems Lee, J., Sedwards, S. & Czarnecki, K. (2021). Uniformly constrained reinforcement learning, submitted to Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS): Special Issue on Multi-Objective Decision Making (MODeM) — under review.