Patrick M. Pilarski
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2021
Patrick Pilarski is a Canada CIFAR AI Chair and a fellow at Amii, where he also serves as a member of the Board of Directors. He is also an associate professor in the Division of Physical Medicine and Rehabilitation, Department of Medicine and an Adjunct associate professor in the Department of Computing Science at the University of Alberta. He holds a Canada Research Chair in Machine Intelligence for Rehabilitation and is a principal investigator in the Reinforcement Learning and Artificial Intelligence Laboratory, the Bionic Limbs for Improved Natural Control Lab and the Sensory Motor Adaptive Rehabilitation Technology Network at the University of Alberta. Patrick is also a Senior Staff Research Scientist at DeepMind in Edmonton, Alberta.
Pilarski leads the Amii Adaptive Prosthetics Program – an interdisciplinary initiative focused on creating intelligent artificial limbs to restore and extend abilities for people with amputations. As part of this research, he explores new machine learning techniques for sensorimotor control and prediction, including methods for human-device interaction and communication, long-term control adaptation, and patient-specific device optimization. Pilarski has pioneered techniques for rapid cancer and pathogen screening through work on biomedical pattern recognition, robotic micro-manipulation of medical samples, and hand-held diagnostic devices.
Awards
- Best Paper Award, EmeCom Workshop at ICLR, 2022
- Canada Research Chair in Machine Intelligence for Rehabilitation, CRC, 2016
- Senior Member, IEEE, 2016
Relevant Publications
- Mathewson, K. W., Parker, A. S. R., Sherstan, C., Edwards, A. L., Sutton, R. S., & Pilarski, P. M. (2023). Communicative capital: A key resource for human-machine shared agency and collaborative capacity. Neural Computing and Applications, 35(23): 16805–16819.
- Kalinowska, A., Pilarski, P.M., & Murphey, T.D. (2023). Embodied communication: How robots and people communicate through physical interaction. Annual Review of Control, Robotics, and Autonomous Systems. 6 : 205-232.
- Williams, H. E., Shehata, A. W., Dawson, M. R., Scheme, E., Hebert, J. S., & Pilarski, P. M. (2022). Recurrent convolutional neural networks as an approach to position-aware myoelectric prosthesis control. IEEE Transactions on Biomedical Engineering, 69(7): 2243-2255.
Edwards, A.L., Dawson, M. R., Hebert, J. S., Sherstan, C., Sutton, R. S., Chan, K. M., Pilarski, P. M. (2016). « Application of Real-time Machine Learning to Myoelectric Prosthesis Control: A Case Series in Adaptive Switching », Prosthetics & Orthotics International, 40(5):573-581.