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.
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BSc Computer Science, Algorithms and Complexity Theory, University of Calgary, Minor in Pure Mathematics
Matt works on semantic segmentation of images for autonomous driving.
- Mmath. - Computer Science, at Univeristy of Waterloo, Ontario, Canada
- B. Sc. A. in Informatics - Distinction Profile, at Université Laval, Québec, Canada
- DEC in Multimedia, at Cégep de Matane, Québec, Canada
Background
I have 5 years of full-time experience in industry in the field of computing and my team has already won an award of best digital production in 2014.
BASc Electrical and Computer Engineering, University of Windsor, Ontario, Canada
Rahul works on visual odometry and sensor fusion.
BSc Electrical and Computer Engineering, University of New Brunswick, New Brunswick, Canada
Research interests
- Autonomous vehicles
- Software and hardware architectures for cyber-physical systems
- Robotics
- PhD Graduate, Technical University of Ilmenau
- MSc Graduate, California State University at Sacramento
- Dipl-Inf Graduate, Technical University of Ilmenau
Krzysztof works on many aspects of autonomous driving, including requirements, architecture, and planning.
Previous positions
- B.Sc. graduate (2017), Department of Mathematics & Computer Science, University of Lethbridge, Canada
Publications
2020
Dillen, N., Ilievski, M., Law, E., Nacke, L. E., Czarnecki, K., & Schneider, O. (2020).
BEng Mechatronics, McMaster University, Ontario, Canada
Research interests
- autonomous vehicles
- perception and object classification
- deep learning neural nets
Samin works on semantic segmentation of images for autonomous driving.
Prior affiliations
- 2015.09 - 2017.12 Postdoctoral Fellow at the Reinforcement Learning and Artificial Intelligence Lab. at the University of Alberta, AB, Canada.
Selected publications
- Constrained RL for safety-critical systems
- Lee, J., Sedwards, S. & Czarnecki, K. (2021).
BEng Automation, University of Science and Technology of China
Changjian works on applying hierarchical reinforcement learning to behavioral planning problem in autonomous driving.
- BSc, Computer Science, University of Waterloo
- MMath, University of Waterloo
Publications
2017
Ross, J., A. Murashkin, J. Hui Liang, M. Antkiewicz, and K.
MASc student, University of Waterloo (September 2019 - February 2022)
Research engineer, Autonomoose.net (July 2017 - August 2019)
Publications
2021
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.
- MSc Computer Science, Federal University of Minas Gerais, Brazil
- MBA Project Management, Fundação Getúlio Vargas, Brazil
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.
Publications
2021
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).
Interests
- Scalable 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
Publications
- BSc, Computer Science, McGill University
- MMath, Computer Science, University of Waterloo
- Ph.D. Candidate, Computer Science, University of Waterloo
- B.Sc., Math and Computer Science, Wilkes University, 2012
Publications
2017
Zulkoski, E., C. Bright, A. Heinle, I. Kotsireas, K. Czarnecki, and V. Ganesh, "Combining SAT Solvers with Computer Algebra Systems to Verify Combinatorial Conjectures", Journal of Automated Reasoning, vol. 58, issue 3, pp.