Rick Salay, PhD, is an expert in software modeling research, having published over 40 peer-reviewed papers in the area. Two of these won best paper awards (one of them at International Conference on Software Engineering (ICSE), the top software engineering conference) and another one was nominated for best paper in Requirements Engineering (RE), the top international requirements engineering conference.
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
Antkiewicz, M., M. Kahn, M. Ala, K. Czarnecki, P. Wells, A. Acharya, and S. Beiker, "Modes of Automated Driving System Scenario Testing: Experience Report and Recommendations ", SAE World Congress Experience: SAE, 2020.
MASc student, University of Waterloo (September 2019 - Present)
Research engineer, Autonomoose.net (July 2017 - August 2019)
Pitropov, M., Garcia, D., Rebello, J., Smart, M., Wang, C., Czarnecki, K. and Waslander, S., 2020. Canadian Adverse Driving Conditions Dataset. arXiv preprint arXiv:2001.10117.
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.
A Sarkar, K Czarnecki, "A behavior driven approach for sampling rare event situations for autonomous vehicles", 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)