Patrick Pilarski, PhD, BASc

Associate Professor and Canada CIFAR AI Chair (Amii), Faculty of Medicine & Dentistry - Deptment of Medicine - PhysMedRehab

Pronouns: he/him

Contact

Associate Professor and Canada CIFAR AI Chair (Amii), Faculty of Medicine & Dentistry - Deptment of Medicine - PhysMedRehab
Email
patrick.pilarski@ualberta.ca

Overview

Area of Study / Keywords

Reinforcement Learning and Decision Making Artificial Intelligence Human-machine Interaction Intelligence Amplification Rehabilitation Technology Neuroprostheses Bionic Limbs


About

Short Biography

Dr. Patrick M. Pilarski is a Canada CIFAR Artificial Intelligence Chair, past Canada Research Chair in Machine Intelligence for Rehabilitation, and an Associate Professor in the Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta. In 2017, Dr. Pilarski co-founded DeepMind's Alberta office, where he continues as a team lead and Senior Staff Research Scientist. He is a Fellow and Vice Board Chair of the Alberta Machine Intelligence Institute (Amii), co-leads the Bionic Limbs for Improved Natural Control (BLINC) Laboratory, and is a principal investigator with the Reinforcement Learning and Artificial Intelligence Laboratory (RLAI) at the University of Alberta. 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 and decision making, artificial intelligence, real-time machine learning, human-machine interaction, intelligence amplification, rehabilitation technology, and assistive robotics. He 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, Dr. Pilarski has developed and made prominent machine learning techniques for continual sensorimotor control and prediction learning on prosthetic devices. These include some of the first published approaches to ongoing user training of upper-limb prosthesis control systems via reinforcement learning, and he pioneered the use of general value functions in prediction learning to continually adapt myoelectric control interfaces in real time. Dr. Pilarski's research programme continues to explore human-device interaction and communication, long-term co-adaptation and joint action between agents, patient-specific device optimization, and constructivism in tightly coupled human-machine interfaces. He has also created 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 award-winning author or co-author of more than 100 peer-reviewed articles, a Senior Member of the IEEE, and has been supported by provincial, national, and international research grants.

Education

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

Positions

  • Senior Staff Research Scientist and Office Co-Lead, DeepMind, Edmonton, Alberta
  • Fellow and Board of Directors, Alberta Machine Intelligence Institute (Amii)
  • Adjunct Associate Professor, Dept. of Computing Science
  • Adjunct Associate Professor, Faculty of Rehabilitation Medicine
  • Principal Investigator, Reinforcement Learning and Artificial Intelligence (RLAI) Laboratory
  • Principal Investigator, Bionic Limbs for Improved Natural Control (BLINC) Laboratory
  • Principal Investigator, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network

Research

Areas

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

Interests

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.

Teaching

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