Profiles

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Vahdat Abdelzad

Postdoctoral Fellow

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.

Matthew Angus

MMath student

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 À¶Ý®ÊÓÆµ, Ontario, Canada
  • B. Sc. A. in Informatics - Distinction Profile, at , Québec, Canada
  • DEC in Multimedia, at , 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.

Rahul Chandail

MASc student

BASc Electrical and Computer Engineering, University of Windsor, Ontario, Canada

Rahul works on visual odometry and sensor fusion.

Ian Colwell

MASc student

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

Krzysztof Czarnecki

Professor of Electrical and Computer Engineering
  • PhD Graduate,
  • MSc Graduate,
  • Dipl-Inf Graduate,

Krzysztof works on many aspects of autonomous driving, including requirements, architecture, and planning.

Marko Ilievski

MMath Candidate

Previous positions

  • B.Sc.Ìýgraduate (2017), , , Canada

Publications

2020

Dillen, N., Ilievski, M., Law, E., Nacke, L. E., Czarnecki, K., & Schneider, O. (2020).

Samin Arman Khan

MASc student

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.

Jaeyoung Lee

Research Associate

Prior affiliationsÌý

  • 2015.09Ìý- 2017.12Ìý 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.Ìý& Czarnecki, K. (2021).

Changjian Li

MASc student

BEng Automation,Ìý

Changjian works on applying hierarchical reinforcement learning to behavioral planning problem in autonomous driving.

Jimmy Liang

PhD Student
  • BSc, Computer Science,Ìý
  • ²Ñ²Ñ²¹³Ù³ó,Ìý

Publications

2017

Ross, J., A. Murashkin, J. Hui Liang, M. Antkiewicz, and K.

Matthew Pitropov

Graduated MASc student

MASc student, University of À¶Ý®ÊÓÆµ (September 2019 - February 2022)

Research engineer, (JulyÌý2017 - 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.ÌýThe International Journal of Robotics Research,Ìý40(4-5), pp.681-690.

Rick Salay

Research associate

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 À¶Ý®ÊÓÆµ Intelligent Systems Engineering Lab at University of À¶Ý®ÊÓÆµ 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. "."ÌýarXiv preprint arXiv:2109.09861, 2022 AAAI Conference on Artificial Intelligence (AAAI 2022).

Sean Sedwards

Research Assistant Professor

Interests

  • Scalable verification and accelerated learning
    • active deep learning for perception in autonomous driving
    • statistical verificationÌýof stochastic and nondeterminsiticÌýsystems
    • safe reinforcement learning and formal verification ofÌýmachine-learned systems
    • verification and optimisation of hybrid and timedÌýsystems
    • accelerated simulation forÌýrare eventÌýverification
    • accelerated learning for prediction of rare events
  • Behaviour modelling, planningÌýand prediction