Patrick Pilarski, PhD, BASc

Associate Professor and Canada Research Chair, MED Dept ofMedicine - PhysMedRehab


Associate Professor and Canada Research Chair, MED Dept ofMedicine - PhysMedRehab



Short Biography

Dr. Patrick M. Pilarski is a Canada Research Chair in Machine Intelligence for Rehabilitation at the University of Alberta, and an Associate Professor in the Division of Physical Medicine and Rehabilitation, Department of Medicine. Dr. Pilarski is a principal investigator with the Alberta Machine Intelligence Institute (Amii) and the Reinforcement Learning and Artificial Intelligence Laboratory (RLAI). Dr. Pilarski received the B.ASc. in Electrical Engineering from the University of British Columbia in 2004, the Ph.D. in Electrical and Computer Engineering from the University of Alberta in 2009, and completed his postdoctoral training in computing science with Dr. Richard S. Sutton at the University of Alberta. Dr. Pilarski's research interests include reinforcement learning, real-time machine learning, human-machine interaction, rehabilitation technology, and assistive robotics. He leads an interdisciplinary initiative focused on creating intelligent artificial limbs to restore and extend abilities for people with amputations. As part of this research, Dr. Pilarski 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. He has also 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. Dr. Pilarski is the author or co-author of more than 70 peer-reviewed articles, recipient of multiple best paper awards, and is currently supported by provincial, national, and international research grants.


  • B.ASc., Electrical Engineering, UBC, 2004
  • Ph.D., Electrical and Computer Engineering, University of Alberta, 2009


  • Associate Professor, Division of Physical Medicine of Rehabilitation, Department of Medicine, University of Alberta
  • Adjunct Associate Professor, Department of Computing Science, University of Alberta
  • Adjunct Associate Professor, Faculty of Rehabilitation Medicine, University of Alberta
  • Principal Investigator, Alberta Machine Intelligence Institute (Amii), University of Alberta
  • Principal Investigator, Reinforcement Learning and Artificial Intelligence Laboratory (RLAI), University of Alberta
  • Principal Investigator, Bionic Limbs for Improved Natural Control Laboratory (BLINC), University of Alberta
  • Research Affiliate, Glenrose Rehabilitation Hospital, Edmonton



  • Rehabilitation Technology
  • Prosthetics and Artificial Limbs
  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning
  • Assistive Robotics


Adaptive rehabilitation technology, Alleviating motor disabilities, Artificial Intelligence, Assistive robotics, Automated assays, Automation, Autonomous robotics, Biomedical signal processing, consumable meat, Control systems, Data analysis, Data science, Diagnostic technology, E. Coli, Engineering design, Environment modelling, Eye tracking, Food adulterants, Functional outcomes, Lab-on-a-chip, Machine intelligence, Machine learning, Meat screening, Metrics, Microfluidics, Motion Capture, Pathogen detection, pathogen screening, Pathogenic E. Coli detection, Pattern analysis, Prediction learning, Prosthetics, Rehab Technology, rehabilitation, Rehabilitation technology, Reinforcement Learning, Robotic arms, Screening, Sensory feedback, Shared control, Software design, Supplementing motor disabilities, Technology development

Current Research:

Adaptive Rehabilitation Technology

  • Real-time machine learning for artificial limbs and multi-function powered prostheses.
  • Algorithms and adaptive computational techniques that increase patients' ability to customize and control their assistive biomedical devices and environments.
  • Prediction learning to improve users' ability to switch between the modes and functions of assistive devices.
  • Long-term brain-body-machine and brain-computer interaction.

Intelligent Systems and Interfaces

  • Reinforcement learning and artificial intelligence methods for use in complex real-world environments.
  • Human-machine interfaces: theoretical and applied methods for communicating between complex distributed systems.
  • Human instruction and training of machine learning systems.
  • Prediction, representation, and control learning that is grounded in data-dense, real-time sensorimotor experience.
  • Continuous-action actor-critic policy gradient algorithms.

Biomedical Pattern Analysis

  • Model-free interpretation of real-time, multi-signal human biofeedback (for example, myoelectric signals).
  • Outcome measures based on motion capture, eye tracking, and biosignal tracking for prosthetics and other human-machine interfaces.


Prospective Students:

Please note that I am not accepting any new students, staff, or trainees for the foreseeable future. Due to the volume of queries, I apologize that I am not able to respond to requests for information about future supervision opportunities.

Recent Courses

  • CMPUT 607, Applied Reinforcement Learning, Winter 2017
  • CMPUT 609, Reinforcement Learning in Artificial Intelligence, Winter 2015
  • CMPUT 496, Fall 2014
  • CMPUT 605, Fall and Winter 2015, 2016
  • REHAB 599, Fall 2014