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

• 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.
  • Tightly coupled interfaces; constructivism in tightly coupled interfaces.
  • Human instruction and training of machine learning systems.
  • Prediction, representation, and control learning that is grounded in data-dense, real-time sensorimotor experience.
  • Intelligence amplification, human-machine joint action, and augmenting human intelligence.
  
  
• 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.
  
  
  Above: Custom robotic hardware, designed by our group with machine intelligence in mind—the Bento Arm with attached HANDi Hand (image thanks to M. R. Dawson).
  

• Artificial intelligence, reinforcement learning, and machine learning.
• Human-machine interfaces, adaptive distributed systems, and cognitive science.
• Cybernetics and intelligence amplification.
• Biomedical image analysis, pattern analysis, data mining, and computer vision.
• Autonomous mobile robotics and rehabilitation robotics.

• Laura Petrich (PhD Student, Dept. Computing Science)
• Annette Lau (MSc Student, Faculty of Rehabillitation Medicine)

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

• Dr. Adam S. R. Parker (PhD, Faculty of Rehabilitation Medicine, 2024)
• Thesis: Learning to Partner: Exploring Real-Time Adaptive Feedback via Temporal-Difference Machine Learning for Improved Human-Prosthesis Collaboration
• Post-graduation: Postdoctoral Fellow at Dept. Computing Science, University of Alberta
• Dr. Heather E. Williams (PhD, Dept. Biomedical Engineering, 2024)
• Thesis: Position-Aware Control of Myoelectric Prostheses
• Post-graduation: Postdoctoral Fellow at Institute of Biomedical Engineering, University of New Brunswick
• Dr. Nadia M. Ady (PhD, Dept. Computing Science, 2023)
• Thesis: Specific Machine Curiosity
• Post-graduation: Postdoctoral Fellow at Helsinki Institute for Information Technology / Aalto University
• Dr. Alex Kearney (PhD, Dept. Computing Science, 2023)
• Thesis: Letting the Agent Take the Wheel: Principles for Constructive and Predictive Knowledge
• Post-graduation: Startup Co-founder (Artificial.Agency); previously Research Scientist at DeepMind (Edmonton)
• Dr. Craig Sherstan (PhD, Dept. Computing Science, 2020)
• Thesis: Representation and General Value Functions
• Post-graduation: Research Scientist at Sony AI
• Dr. Johannes Günther (Postdoctoral Fellow, Dept. Computing Science, 2018-2020)
• Post-training: Research Scientist at Sony AI; previously Research Scientist at the Alberta Machine Intelligence Institute (Amii); Adjunct Professor in the Dept. Computing Science, University of Alberta
• Dr. Kory W. Mathewson (PhD, Dept. Computing Science, 2019)
• Thesis: Humour-in-the-loop: Improvised Theatre with Interactive Machine Learning Systems
• Post-graduation: Research Scientist at DeepMind (Montreal)
• Dylan J. Brenneis (MSc, Dept. Mechanical Engineering, 2019)
• Thesis: Automatic Levelling of a Prosthetic Wrist
• Post-graduation: Research Associate at University of Alberta; previously Roboticist at DeepMind (Edmonton)
• Jaden Travnik (MSc, Dept. Computing Science, 2018)
• Thesis: Reinforcement Learning on Resource Bounded Systems
• Post-graduation: Research Engineer at Sony AI; previously Junior Machine Learning Expert at Eidos Montréal (Square Enix); previously at Sofdesk Inc.
• Gautham Vasan (MSc, Dept. Computing Science, 2017)
• Thesis: Teaching a Powered Prosthetic Arm with an Intact Arm Using Reinforcement Learning
• Post-graduation: PhD student at University of Alberta; previously Machine Learning Researcher at Kindred Systems Inc.
• Vivek Veeriah (MSc, Dept. Computing Science, 2017)
• Thesis: Beyond Clever Hans: Learning From People Without Their Really Trying
• Post-graduation: Research Scientist at DeepMind (London); previously PhD student at the University of Michigan
• Ann L. Edwards (MScRS, Faculty of Rehabilitation Medicine, 2016)
• Thesis: Adaptive and Autonomous Switching: Shared Control of Powered Prosthetic Arms Using Reinforcement Learning
• Post-graduation: Engineer at General Dynamics Canada
• Craig Sherstan (MSc, Dept. Computing Science, 2015)
• Thesis: Prosthetic Arms as Wearable Intelligent Robots
• Post-graduation: PhD student at the University of Alberta


• Annette Lau (Undergraduate Researcher)
• Helen Zhao (Undergraduate Researcher)
• Liam Jack (Undergraduate Researcher)
• Ben Hallworth (Undergraduate Researcher)
• Adam S. R. Parker (Undergraduate Researcher)
• Devin Bradburn (Undergraduate Researcher)
• Dylan Brenneis (Undergraduate Researcher)
• Alexanda Kearney (Undergraduate Researcher)
• Jaden Travnik (Undergraduate Researcher)
• Ann L. Edwards (Undergraduate Researcher)


• Kazuhiro Tsuchiyama, R.P.T. (Visiting Professor, 2019-2020, from Fujita Health University, Japan)
• Dr. Hiroki Tanikawa (Visiting Professor, 2018-2019, from Fujita Health University, Japan)
• Dr. Kei Ohtsuka (Visiting Professor, 2017-2018, from Fujita Health University, Japan)
• Dr. Kenichi Ozaki (Visiting Professor, 2017-2018, from the National Center for Geriatrics Gerontology, Japan)

1. H. E. Williams, “Position-Aware Control of Myoelectric Prostheses,” PhD Thesis, Dept. Biomedical Engineering, University of Alberta, 2024.


2. N. M. Ady, “Specific Machine Curiosity,” PhD Thesis, Dept. Computing Science, University of Alberta, 2023.


3. A. K. Kearney, “Letting the Agent Take the Wheel: Principles for Constructive and Predictive Knowledge,” PhD Thesis, Dept. Computing Science, University of Alberta, 2023.


4. C. Sherstan, “Representation and General Value Functions,” PhD Thesis, Dept. Computing Science, University of Alberta, 2020.


5. K. W. Mathewson, “Humour-in-the-loop: Improvised Theatre with Interactive Machine Learning Systems,” PhD Thesis, Dept. Computing Science, University of Alberta, 2019.


6. D. J. Brenneis, “Automatic Levelling of a Prosthetic Wrist,” MSc Thesis, Dept. Mechanical Engineering, University of Alberta, 2019.


7. J. Travnik, “Reinforcement Learning on Resource Bounded Systems,” MSc Thesis, Dept. Computing Science, University of Alberta, 2018.


8. G. Vasan, “Teaching a Powered Prosthetic Arm with an Intact Arm Using Reinforcement Learning,” MSc Thesis, Dept. Computing Science, University of Alberta, 2017.


9. V. Veeriah, “Beyond Clever Hans: Learning From People Without Their Really Trying,” MSc Thesis, Dept. Computing Science, University of Alberta, 2017.


10. A. L. Edwards, “Adaptive and Autonomous Switching: Shared Control of Powered Prosthetic Arms Using Reinforcement Learning,” MScRS Thesis, Faculty of Rehabilitation Medicine, University of Alberta, 2016.


11. C. Sherstan, “Prosthetic Arms as Wearable Intelligent Robots,” MSc Thesis, Dept. Computing Science, University of Alberta, 2015.

1. Substantial updates to this list coming soon! Apologies. In the meantime, please see publication listing on Google Scholar.


2. A. S. R. Parker, M. R. Dawson, P. M. Pilarski, “Continually Learned Pavlovian Signalling Without Forgetting for Human-in-the-Loop Robotic Control,” NeurIPS 2022 Workshop on Human in the Loop Learning, 2022, 11 pages.


3. A. Kalinowska, E. Davoodi, K. W. Mathewson, T. D. Murphey, P. M. Pilarski, “Towards situated communication in multi-step interactions: Time is a key pressure in communication emergence,” Annual Meeting of the Congitive Science Society (CogSci), 2022, accepted. 6 pages.


4. A. Kearney, A. J. Koop, P. M. Pilarski, “What's a good prediction? Challenges in evaluating an agent's knowledge,” accepted to Adaptive Behavior, 14 pages, 2022.


5. H. E. Williams, J. Guenther, J. Hebert, P. M. Pilarski, A. W. Shehata, “Composite Recurrent Convolutional Neural Networks Offer a Position-Aware Prosthesis Control Alternative While Balancing Predictive Accuracy with Training Burden,” 2022 IEEE 17th International Conference on Rehabilitation Robotics (ICORR), July 25-29, 2022, Rotterdam, The Netherlands, accepted. 6 pages.


6. P. Faridi, J. K. Mehr, D. Wilson, M. Sharifi, M. Tavakoli, P. M. Pilarski, V. K. Mushahwar, “Machine-learned Adaptive Switching in Voluntary Lower-limb Exoskeleton Control: Preliminary Results,” 2022 IEEE 17th International Conference on Rehabilitation Robotics (ICORR), July 25-29, 2022, Rotterdam, The Netherlands, accepted. 6 pages.


7. A. Kalinowska, E. Davoodi, K. W. Mathewson, T. D. Murphey, P. M. Pilarski, “Communication Emergence in a Goal-Oriented Environment: Towards Situated Communication in Multi-Step Interactions,” accepted to The 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2022), June 8-11, 2022, Brown University, Providence, RI, USA. 5 pages.


8. N. M. Ady, R. Shariff, J. Guenther, P. M. Pilarski, “Prototyping three key properties of specific curiosity in computational reinforcement learning,” accepted to the 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2022), June 8-11, 2022, Brown University, Providence, RI, USA. 5 pages.


9. N. J. Wispinski, A. Butcher, C. S. Chapman, M. M. Botvinick, P. M. Pilarski, “Adaptive patch foraging in deep reinforcement learning agents,” accepted to the 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2022), June 8-11, 2022, Brown University, Providence, RI, USA. 5 pages.


10. A. Kalinowska, E. Davoodi, F. Strub, K. Mathewson, T. Murphey, P. M. Pilarski, “Situated Communication: A Solution to Over-communication between Artificial Agents,” 5th Workshop on Emergent Communication (EmeCom) at ICLR 2022, accepted. 9 pages. Best paper award.


11. K. W. Mathewson, P. M. Pilarski, “A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning,” ICLR 2022 Workshop on ML Evaluation Standards, accepted. 5 pages.


12. A. Butcher, M. B. Johanson, E. Davoodi, D. J. A. Brenneis, L. Acker, A. S. R. Parker, A. White, J. Modayil, P. M. Pilarski, “Pavlovian signalling with general value functions in agent-agent temporal decision making,” Adaptive and Learning Agents (ALA) Workshop at AAMAS 2022, 9-10 May 2022, Auckland, NZ, accepted. 9 pages. (PDF).


13. D. J. A. Brenneis, A. S. Parker, M. B. Johanson, A. Butcher, E. Davoodi, L. Acker, M. M. Botvinick, J. Modayil, A. White, P. M. Pilarski, “Assessing human interaction in virtual reality with continually learning prediction agents based on reinforcement learning algorithms: A pilot study,” Adaptive and Learning Agents (ALA) Workshop at AAMAS 2022, 9-10 May 2022, Auckland, NZ, accepted. 9 pages. (PDF)


14. A. Kearney, J. Günther, P. M. Pilarski, “Prediction, Knowledge, And Explainability: Examining The Use of General Value Functions in Machine Knowledge,” Frontiers in Artificial Intelligence 5, 826724, 2022.


15. J. L. Castellanos-Cruz, M. F. Gómez-Medina, M. Tavakoli, P. Pilarski, K. D. Adams, “Preliminary testing of eye gaze interfaces for controlling a haptic system intended to support play in children with physical impairments: Attentive versus explicit interfaces,” Journal of Rehabilitation and Assistive Technologies Engineering, 2022.  https://doi.org/10.1177/20556683221079694.


16. H. E. Williams, A. W. Shehata, M. R. Dawson, E. Scheme, J. S. Hebert, P. M. Pilarski, “Recurrent Convolutional Neural Networks as an Approach to Position-Aware Myoelectric Prosthesis Control,” IEEE Transactions on Biomedical Engineering, 2022 . doi: 10.1109/TBME.2022.3140269. (PDF)


17. A. K. Kearney, A. Koop, J. Günther, P. M. Pilarski, “Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making,” NeurIPS 2021 Workshop: Self-Supervised Learning - Theory and Practice, Dec. 14, 2021. (PDF)


18. D. P. Manage, J. Lauzon, L. M. Pilarski, P. M. Pilarski, L. M. McMullen, “Comparison of a Miniaturized Cassette PCR System with a Commercially Available Platform for Detecting Escherichia coli in Beef Carcass Swabs,” Micromachines, August 2021; 12(8):959. (Published PDF)


19. A. W. Shehata, H. E. Williams, J. S. Hebert, P. M. Pilarski, “Machine Learning for the Control of Prosthetic Arms: Using Electromyographic Signals for Improved Performance,” IEEE Signal Processing Magazine, vol. 38, no. 4, pp. 46-53, July 2021. (Preprint PDF pending)


20. S. A. Stone, Q. A. Boser, T. R. Dawson, A. H. Vette, J. S. Hebert, P. M. Pilarski, C. S. Chapman, “Sub-centimeter 3D gaze vector accuracy on real-world tasks: an investigation of eye and motion capture calibration routines,” In ACM Symposium on Eye Tracking Research and Applications (ETRA '21 Adjunct), Association for Computing Machinery, New York, NY, USA, Article 13, 1–4, 2021. (Published PDF)


21. A. S. R. Parker, P. M. Pilarski, “Position statement: Assistive Technology as Partners Through Machine-Learned Communication,” Workshop on Reinforcement Learning for Humans, Computer, and Interaction (RL4HCI), ACM CHI 2021, May 8, 2021, Yokohama, Japan. ACM, New York, NY, USA, 3 pages. (PDF)


22. H. E. Williams, C. S. Chapman, P. M. Pilarski, A. H. Vette, J. S. Hebert, “Myoelectric prosthesis users and non-disabled individuals wearing a simulated prosthesis exhibit similar compensatory movement strategies,” Journal of NeuroEngineering and Rehabilitation, 18(72): 1-15, 2021. (Published PDF)


23. J. S. Schofield, M. A. Battraw, A. S. R. Parker, P. M. Pilarski, J. W. Sensinger, Paul D. Marasco, “Embodied Cooperation to Promote Forgiving Interactions With Autonomous Machines,” Frontiers in Neurorobotics 15: 661603, 2021. (Published PDF)


24. J. Günther, E. Reichensdörfer, P. M. Pilarski, K. Diepold, “Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison,” PLoS ONE, 15(12): e0243320. December 10, 2020. (Published PDF)


25. M. R. Dawson, H. E. Williams, G. Murgatroyd, J. S. Hebert, P. M. Pilarski, “brachIOplexus: Myoelectric Training Software for Clinical and Research Applications,” Myoelectric Control Symposium (MEC), 2020.


26. B. W. Hallworth, A. W. Shehata, M. R. Dawson, F. Sperle, M. Connan, W. Friedl, B. Vodermayer, C. Castellini, J. S. Hebert, P. M. Pilarski, “A Transradial Modular Adaptable Platform for Evaluating Prosthetic Feedback and Control Strategies,” Myoelectric Control Symposium (MEC), 2020.


27. J. L. Castellanos-Cruz, M. F. Gomez-Medina, M. Tavakoli, P. M. Pilarski, K. Adams, “Comparison of Attentive and Explicit Eye Gaze Interfaces for Controlling Haptic Guidance of a Robotic Controller,” Journal of Medical Robotics Research, Vol. 4, Iss. 03n04, pp. 1950005, 2019. DOI: 10.1142/S2424905X19500053


28. J. Günther, N. M. Ady, A. Kearney, M. R. Dawson, P. M. Pilarski, “Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures,” Frontiers in Robotics and AI, vol. 7, no. 34, 2020, DOI: 10.3389/frobt.2020.00034. (Published copy)


29. C. Sherstan, S. Dohare, J. MacGlashan, J. Gunther, P.M. Pilarski, “Gamma-Nets: Generalizing Value Estimation over Timescale,” Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), February 7-12, New York, New York USA, 2020, 10 pages. (Preprint PDF)


30. A. M. Valevicius, Q. A. Boser, C. S. Chapman, P. M. Pilarski, A. H. Vette, J. S. Hebert, “Compensatory strategies of body-powered prosthesis users reveal primary reliance on trunk motion and relation to skill level,” Clinical Biomechanics, vol. 72, 122–129, 2020.


31. J. S. Hebert, Q. A. Boser, A. M. Valevicius, H. Tanikawa, E. B. Lavoie, A. H. Vette, P. M. Pilarski, C. S. Chapman, “Quantitative Eye Gaze and Movement Differences in Visuomotor Adaptations to Varying Task Demands Among Upper-Extremity Prosthesis Users,” JAMA Network Open 2 (9), 2019, e1911197-e1911197. (PDF)


32. H. E. Williams, C. S. Chapman, P. M. Pilarski, A. H. Vette, J. S. Hebert, “Gaze and Movement Assessment (GaMA): Inter-site Validation of a Visuomotor Upper Limb Functional Protocol,” PLoS One, vol. 14, no. 12, e0219333, 2019. (PDF)


33. D. P. Manage, J. Lauzon, C. M. Jones, P. J. Ward, L. M. Pilarski, P. M. Pilarski, L. M. McMullen, “Detection of pathogenic Escherichia coli on potentially contaminated beef carcasses using cassette PCR and conventional PCR,” BMC microbiology 19 (1), 175, 2019. (PDF)


34. P. M. Pilarski, A. Butcher, M. Johanson, M. M. Botvinick, A. Bolt, A. S. R. Parker, “Learned human-agent decision-making, communication and joint action in a virtual reality environment,” 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 7-10, McGill University, Montreal, Quebec, Canada, 2019, pp. 302-306. (PDF on arXiv)


35. A. Kearney, P. M. Pilarski, “When is a prediction knowledge?,” 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 7-10, McGill University, Montreal, Quebec, Canada, 2019, pp. 231-235. (PDF on arXiv)


36. J. C. Cruz, M. F. Gómez-Medina, M. Tavakoli, P. M. Pilarski, K. Adams, “Predicting the toys that children and participants with cerebral palsy want to reach while controlling a haptic telerobotic system,” Assistive Technology, vol. 31, no. 5, 231, 2019. Conference paper at the Rehabilitation Engineering and Assistive Technology Society of North America (RESNA), RehabWeek 2019, 24-28 June, 2019, Toronto, Canada.


37. M. F. Gómez-Medina, J. C. Cruz, A. M. R. Rincon, P. M. Pilarski, K. Adams, “Comparison between two different prompting conditions when children use a lego robot to perform a set of tasks,” Assistive Technology, vol. 31, no. 5, 231–235, 2019. Conference paper at the Rehabilitation Engineering and Assistive Technology Society of North America (RESNA), RehabWeek 2019, 24-28 June, 2019, Toronto, Canada.


38. A. S. R. Parker, A. L. Edwards, P. M. Pilarski, “Exploring the Impact of Machine-Learned Predictions on Feedback from an Artificial Limb,” 2019 IEEE-RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), 24-28 June, 2019, Toronto, Canada, pp. 1239-1246. (Preprint PDF)


39. D. J. A. Brenneis, M. R. Dawson, H. Tanikawa, J. S. Hebert, J. P. Carey, P M. Pilarski, “The Effect of an Automatically Levelling Wrist Control System,” 2019 IEEE-RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), 24-28 June, 2019, Toronto, Canada, pp. 816-823. (Preprint PDF)


40. H. E. Williams, Q. A. Boser, P. M. Pilarski, C. S. Chapman, A. H. Vette, J. S. Hebert, “Hand Function Kinematics when using a Simulated Myoelectric Prosthesis,” 2019 IEEE-RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), 24-28 June, 2019, Toronto, Canada, pp. 169-174. (Preprint PDF)


41. J. L. Castellanos-Cruz, M. Tavakoli, P. M. Pilarski, K. Adams, “Supporting Play by Applying Haptic Guidance Along a Surface Learnt from Single Motion Trajectories,” 2019 IEEE-RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), 24-28 June, 2019, Toronto, Canada, pp. 175-180. (Preprint PDF)


42. A. M. Valevicius, Q. A. Boser, E. B. Lavoie, C. S. Chapman, P. M. Pilarski, J. S. Hebert, A. H. Vette, “Characterization of normative angular joint kinematics during two functional upper limb tasks,” Gait & Posture, vol. 69, pp. 176–186, March 2019. (Published copy)


43. S. H. Huang, M. Zambelli, Y. Tassa, J. Kay, M. Martins, P. M. Pilarski, R. Hadsell, “Achieving Gentle Manipulation with Deep Reinforcement Learning,” Deep Reinforcement Learning Workshop, NeurIPS 2018 , Montreal, Canada, Dec. 11, 2018, 11 pages. (PDF)


44. S. H. Huang, M. Zambelli, Y. Tassa, J. Kay, M. Martins, P. M. Pilarski, R. Hadsell, “Leveraging Physical Models for Gentle Manipulation,” Modeling the Physical World: Perception, Learning, and Control, NeurIPS 2018 Workshop, Montreal, Canada, Dec. 7, 2018, 4 pages. (PDF)


45. J. Gunther, A. Kearney, M. R. Dawson, C. Sherstan, P. M. Pilarski, “Predictions, Surprise, and Predictions of Surprise in General Value Function Architectures,” Proceedings of the AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy, Arlington, Virginia, October 18-20, 2018, pp. 22–29. (PDF)


46. A. Kearney, A. Koop, C. Sherstan, J. Gunther, R. S. Sutton, P. M. Pilarski, M. E. Taylor, “Evaluating Predictive Knowledge,” Proceedings of the AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy, Arlington, Virginia, October 18-20, 2018, pp. 43–46. (PDF)


47. C. Sherstan, M. C. Machado, P. M. Pilarski, “Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation,” Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), October, 1-5, 2018, Madrid, Spain, pp. 2997-3003. (Preprint PDF)


48. D. J. A. Brenneis, M. R. Dawson, G. Murgatroyd, J. P. Carey, P. M. Pilarski, “Initial Investigation of a Self-Adjusting Wrist Control System to Maintain Prosthesis Terminal Device Orientation Relative to the Ground Reference Frame,” 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), August 26-29, Enschede, The Netherlands, 2018, pp. 756–763.(Preprint PDF)


49. J. L. C. Cruz, M. F. Gomez-Medina, M. Tavakoli, P. M. Pilarski, K. Adams, “Preliminary Testing of a Telerobotic Haptic System and Analysis of Visual Attention During a Playful Activity,” 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), August 26-29, Enschede, The Netherlands, 2018, pp. 1280–1285. (Preprint PDF)


50. G. Vasan, P. M. Pilarski, “Context-Aware Learning from Demonstration: Using Camera Data to Support the Synergistic Control of a Multi-Joint Prosthetic Arm,” 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), August 26-29, Enschede, The Netherlands, 2018, pp. 199–206. (Preprint PDF)


51. E. B. Lavoie, A. M. Valevicius, Q. A. Boser, O. Kovic, A. H. Vette, P. M. Pilarski, J. S. Hebert, C. S. Chapman, “Using synchronized eye and motion tracking to determine high-precision eye-movement patterns during object-interaction tasks,” Journal of Vision, Vol. 18(6): 18, 2018. doi: 10.1167/18.6.18. (Published PDF)


52. C. Sherstan, J. MacGlashan, P. M. Pilarski, “Generalizing Value Estimation over Timescale,” FAIM2018 Workshop on Prediction and Generative Modeling in Reinforcement Learning (ICML/AAMAS), Stockholm, Sweden, July 14, 2018, 6 pages. (Published PDF)


53. C. Sherstan, M. C. Machado, P. M. Pilarski, “Incrementally Added GVFs are Learned Faster with the Successor Representation,” Lifelong Learning: A Reinforcement Learning Approach (LLARLA-2018), FAIM 2018 Workshop (ICML/AAMAS), Stockholm, Sweden, July 14, 2018, 8 pages. (Published PDF, Updated IROS Paper)


54. A.M. Valevicius, Q.A. Boser, L.B. Lavoie, G.S. Murgatroyd, P.M. Pilarski, C.S. Chapman, A. Vette, J. Hebert, “Characterization of normative hand movements during two functional upper limb tasks,” PLoS ONE 13(6): e0199549, 2018. doi:10.1371/journal.pone.0199549. (Published PDF)


55. J.B. Travnik, K.W. Mathewson, R.S. Sutton, P.M. Pilarski, “Reactive Reinforcement Learning in Asynchronous Environments,” Front. Robot. AI 5:79, Jun. 2018. doi: 10.3389/frobt.2018.00079. (Published PDF)


56. D. Hunt, C. Figley, D. P. Manage, J. Lauzon, R. Figley, L. M. Pilarski, L. M. McMullen, and P. M. Pilarski, “Monitoring food pathogens: Novel instrumentation for cassette PCR testing,” PLoS ONE 13(5): e0197100, 2018. https://doi.org/10.1371/journal.pone.0197100 (Online copy, PDF)


57. Q. A. Boser, A. M. Valevicius, E. B. Lavoie, C. S. Chapman, P. M. Pilarski, J. S. Hebert, A. H. Vette, “Cluster-Based Upper Body Marker Models for Three-Dimensional Kinematic Analysis: Comparison with an Anatomical Model and Reliability Analysis,” Journal of Biomechanics, vol. 72, pp. 228-234, 2018. (Published PDF)


58. M. Wininger, P. Artemiadis, C. Castellini, P. M. Pilarski, “Editorial: Peripheral Nervous System-Machine Interfaces (PNS-MI),” Front. Neurorobot. 11:54, 2017. doi: 10.3389/fnbot.2017.00054 (Published Copy)


59. D. J. A. Brenneis, M. R. Dawson, P. M. Pilarski, “Development of the HANDi Hand: An Inexpensive, Multi-Articulating, Sensorized Hand for Machine Learning Research in Myoelectric Control,” Proc. of MEC'17: Myoelectric Controls Symposium, Fredericton, New Brunswick, August 15-18, 2017. (Preprint PDF)


60. J. B. Travnik, P. M. Pilarski, “Representing High-Dimensional Data to Intelligent Prostheses and other Wearable Assistive Robots: A First Comparison of Tile Coding and Selective Kanerva Coding,” Proc. of the 15th IEEE-RAS-EMBS Int. Conference on Rehabilitation Robotics (ICORR 2017), July 17-20, 2017, QEII Centre, London, UK, pp. 1443–1450. (Preprint PDF)


61. G. Vasan, P. M. Pilarski, “Learning from Demonstration: Teaching a Myoelectric Prosthesis with an Intact Limb via Reinforcement Learning,” Proc. of the 15th IEEE-RAS-EMBS Int. Conference on Rehabilitation Robotics (ICORR 2017), July 17-20, 2017, QEII Centre, London, UK, pp. 1457–1464. (Preprint PDF)


62. P. M. Pilarski, J. S. Hebert, “Upper and Lower Limb Robotic Prostheses,” in Robotic Assistive Technologies: Principles and Practice, Eds. P. Encarnacao and A. M. Cook, pp. 99–144. Boca Raton, FL: CRC Press, 2017. ISBN: 978-1-4987-4572-7. (Published PDF) (Preprint PDF)


63. K. W. Mathewson, P. M. Pilarski, “Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception,” accepted to the 2017 AAAI Spring Symposium on Interactive Multi-Sensory Object Perception for Embodied Agents, March 27-29, 2017, Stanford University, USA. arXiv:1701.02369 [cs.HC] (PDF)


64. R. Vega, T. Sajed, K. W. Mathewson, K. Khare, P. M. Pilarski, R. Greiner, G. Sanchez-Ante, J. M. Antelis, “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artificial Intelligence Research vol. 6, no. 1, 37–51, 2017. (PDF)


65. H. van Seijen, A. R. Mahmood, P. M. Pilarski, M. C. Machado, R. S. Sutton, “True Online Temporal-Difference Learning,” Journal of Machine Learning Research, vol. 17, no. 145, pp. 1–40, 2016. (PDF)


66. A. L. Edwards, M. R. Dawson, J. S. Hebert, C. Sherstan, R. S. Sutton, K. M. Chan, P. M. Pilarski, “Application of Real-time Machine Learning to Myoelectric Prosthesis Control: A Case Series in Adaptive Switching,” Prosthetics & Orthotics International. vol. 40, no. 5, 573–581, 2016. doi: 10.1177/0309364615605373. (Published copy) (Preprint PDF)


67. K. W. Mathewson and P. M. Pilarski, “Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning,” 2016 IJCAI Workshop on Interactive Machine Learning, New York, July 9th, 2016. (PDF)


68. V. Veeriah, P. M. Pilarski, R. S. Sutton, “Face valuing: Training user interfaces with facial expressions and reinforcement learning,” 2016 IJCAI Workshop on Interactive Machine Learning, New York, July 9th, 2016. (PDF)


69. C. Sherstan, M. C. Machado, A. White, P. M. Pilarski, “Introspective Agents: Confidence Measures for General Value Functions,” Proc. Ninth Conference on Artificial General Intelligence (AGI-16), New York City, July 16-19, 2016; Published in Lecture Notes in Computer Science (9782), 258–261. Springer. (Preprint PDF)


70. A. L. Edwards, J. S. Hebert, P. M. Pilarski, “Machine Learning and Unlearning to Autonomously Switch Between the Functions of a Myoelectric Arm,” accepted to the 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob2016), June 26-29, 2016, Singapore, pp. 514–521. (Preprint PDF)


71. J. Gunther, P. M. Pilarski, G. Helfrich, H. Shen, K. Diepold, “Intelligent Laser Welding through Representation, Prediction, and Control Learning: An Architecture with Deep Neural Networks and Reinforcement Learning,” Mechatronics, vol. 34, pp. 1–11, March 2016. (Published copy) (Preprint PDF)


72. P. M. Pilarski, R. S. Sutton, K. W. Mathewson, “Prosthetic Devices as Goal-Seeking Agents,” Second Workshop on Present and Future of Non-Invasive Peripheral-Nervous-System Machine Interfaces: Progress in Restoring the Human Functions (PNS-MI), Singapore, Aug. 11, 2015. 4 pages. (PDF)


73. C. Sherstan, J. Modayil, P.M. Pilarski, “A Collaborative Approach to the Simultaneous Multi-joint Control of a Prosthetic Arm,” Proceedings of the 14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), August 11–14, Singapore, 2015, pp. 13–18. (PDF) (Video MPG, MP4)


74. H. van Seijen, A. R. Mahmood, P. M. Pilarski, R. S. Sutton, “An empirical evaluation of True Online TD(lambda),” European Workshop on Reinforcement Learning (EWRL), Lille, France, 10–11 July, 2015, 9 pages. (PDF) (arXiv:1507.00353 [cs.AI])


75. P.M. Pilarski, A.L. Edwards, and K.M. Chan, “Novel Control Strategies for Arm Prostheses: A Partnership Between Man and Machine,” The Japanese Journal of Rehabilitation Medicine, vol. 52, no. 2, pp. 91–95, 2015.


76. C. Sherstan and P.M. Pilarski, “Multilayer General Value Functions for Robotic Prediction and Control,” 2014 IROS Workshop on AI and Robotics, Chicago, USA, Sept. 14, 2014, 6 pages. (PDF)


77. C. Castellini, P. Artemiadis, M. Wininger, A. Ajoudani, M. Alimusaj, A. Bicchi, B. Caputo, W. Craelius, S. Dosen, K. Englehart, D. Farina, A. Gijsberts, S. Godfrey, L. Hargrove, M. Ison, T. A. Kuiken, M. Markovic, P.M. Pilarski, R. Rupp, E.J. Scheme, “Proceedings of the First Workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography,” Frontiers in Neurorobotics, vol. 8, no. 22, Aug. 2014. doi: 10.3389/fnbot.2014.00022. (PDF) (Read Online)


78. A.J. Koop, A. Kearney, M. Bowling, P.M. Pilarski, “Dealing with Changing Contexts in Myoelectric Control,” Proc. of MEC'14: Myoelectric Controls Symposium, Fredericton, New Brunswick, August 18-22, 2014, pp. 117-120. (PDF)


79. A.L. Edwards, M.R. Dawson, J.S. Hebert, R.S. Sutton, K.M. Chan, P.M. Pilarski, “Adaptive Switching in Practice: Improving Myoelectric Prosthesis Performance through Reinforcement Learning,” Proc. of MEC'14: Myoelectric Controls Symposium, Fredericton, New Brunswick, August 18-22, 2014, pp. 69-73. (PDF)


80. M.R. Dawson, C. Sherstan, J.P. Carey, J.S. Hebert, P.M. Pilarski, “Development of the Bento Arm: An Improved Robotic Arm for Myoelectric Training and Research,” Proc. of MEC'14: Myoelectric Controls Symposium, Fredericton, New Brunswick, August 18-22, 2014, pp. 60-64. (PDF)


81. J. Gunther, P.M. Pilarski, G. Helfrich, H. Shen, K. Diepold, “First steps toward an intelligent laser welding architecture using deep neural networks and reinforcement learning,” Procedia Technology, Vol. 15, pp. 474–483, 2014. Also published in Proc. of the 2nd Joint International Conference on System-integrated Intelligence: New Challenges for Product and Production Engineering, July 2–4, Bremen, Germany, 2014, pp. 481–490. (PDF)


82. P.M. Pilarski, “Aligning homeostatic and heterostatic perspectives,” Constructivist Foundations, Issue 9(2): 213–215, 2014. (Published copy—a commentary on a target article by Porr and Di Prodi; target article includes the combined references) (PDF)


83. S. Adamia, M. Bar-Natan, B. Haibe-Kains, P.M. Pilarski, C. Bach, S. Pevzner, T. Calimeri, H. Avet-Loiseau, L. Lode, S. Verselis, E.A. Fox, I. Galinsky, S. Mathews, I. Dagogo-Jack, M. Wadleigh, D.P. Steensma, G. Motyckova, D.J. Deangelo, J. Quackenbush, D.G. Tenen, R. Stone and J.D. Griffin, “NOTCH2 and FLT3 gene mis-splicing are common events in patients with acute myeloid leukemia (AML): new potential targets in AML,” Blood, Vol. 123(18), pp. 2816–2825, 2014. doi:10.1182/blood-2013-02-481507.


84. S. Adamia, B. Haibe-Kains, P. M. Pilarski, M. Bar-Natan, S. Pevzner, H. Avet-Loiseau , Laurence Lode L. Lode, S. Verselis, E. A. Fox, J. Burke, I. Galinsky, I. Dagogo-Jack, M. Wadleigh, D. P. Steensma, G. Motyckova, D. J. DeAngelo, J. Quackenbush, R. Stone, J. D. Griffin, “A Genome-wide aberrant RNA splicing in patients with acute myeloid leukemia identifies novel potential disease markers and therapeutic targets,” Clinical Cancer Research, Vol. 20(5): 1135–45, 2014. doi:10.1158/1078-0432.CCR-13-0956.


85. A.L. Edwards, A. Kearney, M.R. Dawson, R.S. Sutton, and P.M. Pilarski, “Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb,” 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Oct. 25–27, Princeton, New Jersey, USA, 2013. 5 pages. (PDF)(arXiv:1309.4714)


86. S. Adamia, P.M. Pilarski, M. Bar-Natan, R.M. Stone, J.D. Griffin, “Alternative splicing in Chronic Myeloid Leukemia (CML): A novel therapeutic target?” Current Cancer Drug Targets, Vol. 13(7): 735–748, September 2013. (PubMed)


87. S. Grange, K. Chettiar, M. Arestu, P. Pilarski, P. Smitham, M. Loizidou, G. Jell, “Nanotechnology & Medical Devices: Risk, Regulation and 'Meta' Registration,” World Journal of Engineering, Vol. 10(3): 191–198, September 2013. (Published First Page) (PDF)


88. L.M. Pilarski, C. Debes Marun, L. Martin, C.P. Venner, P.M. Pilarski, A.R. Belch, “B Lymphocytes as Cancer Stem Cells in Multiple Myeloma,” Journal of Oncopathology, Vol. 1(2): 11–22, July 2013.


89. P.M. Pilarski, T.B. Dick, and R.S. Sutton, “Real-time Prediction Learning for the Simultaneous Actuation of Multiple Prosthetic Joints,” Proc. of the 2013 IEEE International Conference on Rehabilitation Robotics (ICORR), Seattle, USA, June 24–26, 2013. 8 pages. (PDF)


90. P.M. Pilarski, L. Qi, M. Ferguson-Pell, and S. Grange, “Determining the Time until Muscle Fatigue using Temporally Extended Prediction Learning,” Proc. 18th International Functional Electrical Stimulation Society Conference (IFESS), Donostia-San Sebastian, Spain, June 6–8, pp. 37–40, 2013. (PDF)


91. S. Adamia, P.M. Pilarski, A.R. Belch and L.M Pilarski, “Aberrant Splicing, Hyaluronan Synthases and Intracellular Hyaluronan as Drivers of Oncogenesis and Potential Drug Targets,” Current Cancer Drug Targets, Vol. 13(4): 347–361, May 2013. (PubMed)


92. P.M. Pilarski, M.R. Dawson, T. Degris, J.P. Carey, K.M. Chan, J.S. Hebert, and R.S. Sutton, “Adaptive Artificial Limbs: A Real-time Approach to Prediction and Anticipation,” IEEE Robotics & Automation Magazine, Vol. 20(1): 53–64, March 2013. (Postprint PDF) (Published Version)


93. P.M. Pilarski and R.S. Sutton, “Between instruction and reward: human-prompted switching,” AAAI 2012 Fall Symposium on Robots Learning Interactively from Human Teachers (RLIHT), Nov. 2-4, Arlington, VA, USA, AAAI Technical Report FS-12-07, pp. 46–52, 2012. (PDF)


94. J. Modayil, A. White, P.M. Pilarski, and R.S. Sutton, “Acquiring a broad range of empirical knowledge in real time by temporal-difference learning,” Proc. of the IEEE International Conference on Systems, Man, and Cybernetics, Seoul, Korea, Oct 14-17, 2012, pp. 1903–1910. (PDF)


95. P.M. Pilarski, M.R. Dawson, T. Degris, J.P. Carey, K.M. Chan, J.S. Hebert, and R.S. Sutton, “Towards Prediction-Based Prosthetic Control,” Proc. of the 17th International Functional Electrical Stimulation Society Conference 2012 (IFESS), Sept. 9-12, Banff, Canada, pp. 26–29, 2012. (PDF)


96. L. Qi, M. Ferguson-Pell, S. Grange, P.M. Pilarski, “Pushrim kinetics and coordination patterns of shoulder muscles during surface and incline wheelchair propulsion,” Proc. of the 17th International Functional Electrical Stimulation Society Conference 2012 (IFESS), Sept. 9-12, Banff, Canada, pp. 151–153, 2012. (PDF)


97. J. Modayil, A. White, P.M. Pilarski, and R.S. Sutton, “Acquiring diverse predictive knowledge in real time by temporal-difference learning,” Proc. of the International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems (ERLARS), Montpellier, France, Aug. 27-28, 2012. 8 pages. (PDF)


98. T. Degris, P.M. Pilarski, and R.S. Sutton, “Model-Free Reinforcement Learning with Continuous Action in Practice,” Proc. of the 2012 American Control Conference (ACC), June 27-29, 2012, Montreal, Canada, pp. 2177–2182, 2012. (PDF)


99. P.M. Pilarski, M.R. Dawson, T. Degris, J.P. Carey, and R.S. Sutton, “Dynamic Switching and Real-time Machine Learning for Improved Human Control of Assistive Biomedical Robots,” Proc. of the 4th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), June 24-27, Roma, Italy, pp. 296–302, 2012. (PDF)


100. T. Degris, P.M. Pilarski, R.S. Sutton, “Apprentissage par Renforcement sans Modèle et avec Action Continue,” 7èmes Journées Francophones Planification, Décision, et Apprentissage pour la conduite de systèmes (JFPDA 2012), LORIA, Nancy, France, 22–23 mai 2012, 11 pages. (PDF)


101. A.R. Mahmood, R.S. Sutton, T. Degris, and P.M. Pilarski, “Tuning-Free Step-Size Adaptation,” Proc. of the 37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 25-30, Kyoto, Japan, pp. 2121–2124, 2012. (PDF)


102. P.M. Pilarski, M.R. Dawson, T. Degris, F. Fahimi, J.P. Carey, and R.S. Sutton, “Online Human Training of a Myoelectric Prosthesis Controller via Actor-Critic Reinforcement Learning,” Proc. of the 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), June 29-July 1, Zurich, Switzerland, pp. 134-140, 2011. (PDF)


103. R.S. Sutton, J. Modayil, M. Delp, T. Degris, P.M. Pilarski, A. White, D. Precup, “Horde: A Scalable Real-time Architecture for Learning Knowledge from Unsupervised Sensorimotor Interaction,” Proc. of 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2-6, Taipei, Taiwan, pp. 761-768, 2011. (PDF)


104. P.M. Pilarski and C.J. Backhouse, “Towards robust cellular image classification: theoretical foundations for wide-angle scattering pattern analysis,” Biomedical Optics Express, Vol. 1(4): 1225-1233, 2010. http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-1-4-1225 (PDF)


105. L.M. Pilarski, P.M. Pilarski, A. Belch, “Multiple Myeloma may include microvessel endothelial cells of malignant origin,” Leukemia and Lymphoma, Vol. 51(4): 592-597, 2010.


106. P.M. Pilarski, X.T. Su, D.M. Glerum, and C.J. Backhouse, “Computational analysis of mitochondrial placement and aggregation effects on wide-angle cell scattering patterns,” Proceedings of SPIE, Vol. 7187: 71870J (12 pages), 2009. http://dx.doi.org/10.1117/12.809730 (PDF)


107. S. Adamia, P.M. Pilarski, A.R. Belch and L.M. Pilarski, “Genetic abnormalities in Waldenstrom's Macroglobulinemia,” Clin. Lymphoma and Myeloma, Vol. 9(1): 30-32, 2009.


108. S. Adamia, A.A. Reichert, H. Kuppusamy, J. Kriangkum, A. Ghosh, J.J. Hodges, P.M. Pilarski, S.P. Treon, M.J. Mant, T. Reiman, A.R. Belch, and L.M. Pilarski, “Inherited and acquired variations in the hyaluronan synthase 1 (HAS1) gene may contribute to disease progression in multiple myeloma and Waldenstrom macroglobulinemia,” Blood, Vol. 112(13): 5111-5121, 2008. (PDF)


109. P.M. Pilarski, X. Su, D. M. Glerum, and C. J. Backhouse, “Rapid simulation of wide-angle scattering from mitochondria in single cells,” Optics Express, Vol. 16(17): 12819-12834, 2008. http://www.opticsinfobase.org/abstract.cfm?URI=oe-16-17-12819 (PDF)


110. L.M. Pilarski, E. Baigorri, M.J. Mant, P.M. Pilarski, P. Adamson, H. Zola, A.R. Belch, “ Multiple myeloma includes phenotypically defined subsets of clonotypic CD20+ B cells that persist during treatment with Rituximab,” Clinical Medicine: Oncology, Vol. 2: 275-287, 2008. http://la-press.com/article.php?article_id=659 (PDF)


111. V. J. Sieben, C. S. Debes-Marun, P. M. Pilarski, G. V. Kaigala, L. M. Pilarski, and C. Backhouse, “FISH and chips: chromosomal analysis on microfluidic platforms”, IET Nanobiotechnology, Vol. 1(3): 27-35, 2007. (PDF)


112. P.M. Pilarski and C.J. Backhouse, “A method for cytometric image parameterization,” Optics Express, Vol. 14(26): 12720-12743, 2006. http://www.opticsinfobase.org/abstract.cfm?URI=oe-14-26-12720 (PDF) (MOV)


113. A. R. Prakash, S. Adamia, V. Sieben, P. M. Pilarski, L. M. Pilarski, and C. J. Backhouse, "Small Volume PCR in PDMS Biochips with Integrated Fluid Control and Vapour Barrier," Sensors and Actuators B: Chemical, Vol. 113(1): 398-409, 2006. (PDF)
     
     
114. P.M. Pilarski, S. Adamia, and C. J. Backhouse, “An adaptable microvalving system for on-chip polymerase chain reactions,” J. Immunological Methods, Vol. 305(1): 48-58, 2005. (PDF)
     
     
115. T. Mirzayans, N. Parimi, P. Pilarski, C. Backhouse, L. Wyard-Scott, and P. Musilek, “A swarm-based system for object recognition,” Neural Network World, Vol. 15(3): 243-255, 2005. (PDF)
     
     
116. Pilarski, L. M., Adamia, S., Maxwell, C. A., Pilarski, P.M., Reiman, T., Belch, A. R. “Hyaluronan Synthases and RHAMM as Synergistic Mediators of Malignancy in B Lineage Cancers,” in Hyaluronan: Structure, Metabolism, Biological Activities, Therapeutic Applications, Chapter 4, pages 329-338, Editors: E.A. Balazs and V.C. Hascall, Matrix Biology Institute, Edgewater, New Jersey, USA, 2005. (PDF)


117. Pilarski, L.M., Reiman, T., Pilarski, P.M., Orr, F.W., Belch, A.R., “The Malignant Hierarchy in Multiple Myeloma: Relationships Between Malignant Cells and Bone Disease.” Bone Metastasis and Molecular Mechanisms, Chapter 7, pp. 109-138, Editors: Singh, G. and Orr, F.W., Kluwer Academic Publishers, 2004.


118. L.M. Pilarski, S. Adamia, P.M. Pilarski, R. Prakash, J. Lauzon, and C.J. Backhouse,“Improved diagnosis and monitoring of cancer using portable microfluidics platforms,” Proc. of the 2004 International Conference on MEMS, NANO and Smart Systems (ICMENS'04), pp. 340-343, Editors: Badawy, W. and Moussa, W., Banff, Canada, 2004.

1. P. M. Pilarski, A. Butcher, E. Davoodi, M. B. Johanson, D. J. A. Brenneis, A. S. R. Parker, L. Acker, M. M. Botvinick, J. Modayil, A. White. “The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents,” arXiv:2203.09498 [cs.AI], 2022. (PDF)


2. A. Butcher, M. B. Johanson, E. Davoodi, D. J. A. Brenneis, L. Acker, A. S. R. Parker, A. White, J. Modayil, P. M. Pilarski, “Pavlovian signalling with general value functions in agent-agent temporal decision making,” arXiv:2201.03709 [cs.AI], 2022. (PDF).


3. D. J. A. Brenneis, A. S. Parker, M. B. Johanson, A. Butcher, E. Davoodi, L. Acker, M. M. Botvinick, J. Modayil, A. White, P. M. Pilarski, “Assessing human interaction in virtual reality with continually learning prediction agents based on reinforcement learning algorithms: A pilot study,” arXiv:2112.07774 [cs.AI], 2021. (PDF)


4. S. A. Stone, Q. A. Boser, T. R. Dawson, A. H. Vette, J. S. Hebert, P. M. Pilarski, C. S. Chapman, “Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture," bioRxiv, 2021. (PDF)


5. A. Kearney, A. Koop, P. M. Pilarski, “What's a Good Prediction? Challenges in evaluating an agent’s knowledge,” invited paper to the ICLR NERL21 workshop: A Roadmap to Never-ending Reinforcement Learning, 7 May 2021, ICLR Virtual, arXiv:2001.08823 [cs.AI], 2021 (building on “What's a Good Prediction? Issues in Evaluating General Value Functions Through Error,” arXiv:2001.08823v1, 2020). (PDF)


6. K. Kudashkina, P. M. Pilarski, R. S. Sutton, “Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI,” arXiv:2008.12095 [cs.AI], 2020. (PDF)


7. C. Sherstan, S. Dohare, J. MacGlashan, J. Gunther, P.M. Pilarski, “Gamma-Nets: Generalizing Value Estimation over Timescale,” arXiv:1911.07794 [cs.LG], 22 pages, 2019. (PDF)


8. J. Gunther, N. M. Ady, A. Kearney, M. R. Dawson, P. M. Pilarski, “Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures,” arXiv:1908.05751 [cs.LG], 15 pages, 2019. (PDF)


9. J. Gunther, E. Reichensdorfer, P. M. Pilarski, K. Diepold, “General Dynamic Neural Networks for explainable PID parameter tuning in control engineering: An extensive comparison,” arXiv:1905.13268 [cs.LG], 10 pages, 2019. (PDF)


10. P. M. Pilarski, A. Butcher, M. Johanson, M. M. Botvinick, A. Bolt, A. S. R. Parker, “Learned human-agent decision-making, communication and joint action in a virtual reality environment,” arXiv:1905.02691 [cs.AI], 5 pages, 2019. (PDF)


11. A. Kearney, P. M. Pilarski, “When is a prediction knowledge?,” arXiv:1904.09024 [cs.LG]. 5 pages, 2019. (PDF)


12. H. E. Williams, C. S. Chapman, P. M. Pilarski, A. H. Vette, J. S. Hebert, “Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol,” bioRxiv 681437; doi: https://doi.org/10.1101/681437. (PDF)


13. S. H. Huang, M. Zambelli, J. Kay, M. Martins, Y. Tassa, P. M. Pilarski, R. Hadsell, “Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning,” arXiv:1903.08542 [cs.RO], 10 pages, 2019. (PDF)


14. A. Kearney, V. Veeriah, J. B. Travnik, P. M. Pilarski, R. S. Sutton, “Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning,” arXiv:1903.03252 [cs.LG], 38 pages, 2019. (PDF)


15. A. Kearney, V. Veeriah, J. B. Travnik, R. S. Sutton, P. M. Pilarski, “TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent,” arXiv:1804.03334 [cs.LG] (arXiv): 9 pages, 2018 (v1 from May 19, 2017).


16. C. Sherstan, M. C. Machado, P. M. Pilarski, “Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation,” arXiv:1803.09001 [cs.LG] (arXiv): 6 pages, 2018.


17. J.B. Travnik, K.W. Mathewson, R.S. Sutton, P.M. Pilarski, “Reactive Reinforcement Learning in Asynchronous Environments,” arXiv:1802.06139 [cs.AI] (arXiv): 11 pages, 2018.


18. P. M. Pilarski, R. S. Sutton, K. W. Mathewson, C. Sherstan, A. S. R. Parker, A. L. Edwards, “Communicative Capital for Prosthetic Agents,” arXiv:1711.03676 [cs.AI] (arXiv): 33 pages, 2017.


19. K. W. Mathewson, P. M. Pilarski, “Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback,” arXiv:1703.01274 [cs.AI] (arXiv): 10 pages, 2017.


20. P.M. Pilarski, “You, Tomorrow: The future of the human body in the next 100 years,” New Trail, Vol. 71, No. 3, Winter 2015, pp. 22–24. (Online Copy)


21. A.S.R. Parker, A.L. Edwards, P.M. Pilarski, “Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings,” arXiv:1408.1913 [cs.AI], 7 pages, 2014. (PDF)(arXiv)


22. P.M. Pilarski, “Intelligent Artificial Limbs,” Alberta ICT Magazine, 2nd Edition, pp. 28--30, 2012. (PDF) (Read the Magazine)


23. P.M. Pilarski, “The Nuts and Bolts of Learning,” Alberta ICT Magazine, 2nd Edition, pp. 31, 2012. (PDF) (Read the Magazine)

1. P.M. Pilarski, “Computational Analysis of Wide-Angle Light Scattering from Single Cells,” Ph.D. Thesis, University of Alberta, 2009. http://hdl.handle.net/10048/774 (PDF)

1. Wininger, M., Artemiadis, P., Castellini, C., Pilarski, P., eds. (2018). Peripheral Nervous System-Machine Interfaces, 2nd Edition. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-490-7.

1. A. Kearney, A. Koop, J. Guenther, P. M. Pilarski, “What Should I Know? Using Meta Descent for Predictive Feature Discovery in a Single Stream of Experience,” accepted to The 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2022), June 8-11, 2022, Brown University, Providence, RI, USA. (Abstract and poster.)


2. M R. Dawson, A S. Parker, H E. Williams, A W. Shehata, J S. Hebert, C. Chapman, P M. Pilarski, “Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses,” International Conference on Rehabilitation Robotics (ICORR), RehabWeek, 2021. (Abstract and Poster)


3. H E. Williams, P. Faridi, A W. Shehata, J S. Hebert, P M. Pilarski, “Position-Aware Myoelectric Prosthetic Control Using Recurrent Convolutional Neural Networks and Transfer Learning,” International Conference on Rehabilitation Robotics (ICORR), RehabWeek, 2021. (Abstract and Poster)


4. A. S. R. Parker, A. L. Edwards, P. M. Pilarski, “Machine-Learned Predictions Assisting Human Control of an Artificial Limb,” 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 7-10, McGill University, Montreal, Quebec, Canada, 2019. (Abstract and Poster)


5. C. Sherstan, J. MacGlashan, S. Dohare, P. M. Pilarski, “Gamma-nets: Generalizing Value Functions over Timescale,” 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 7-10, McGill University, Montreal, Quebec, Canada, 2019. (Abstract and Poster)


6. M. R. Dawson, P. M. Pilarski, “Real-time Demonstration of Adaptive Switching: An Application of General Value Function Learning to Improve Myoelectric Prosthesis Performance,” 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 7-10, McGill University, Montreal, Quebec, Canada, 2019. (Abstract and Poster)


7. J. Guenther, A. K. Kearney, N. M. Ady, C. Sherstan, M. R. Dawson, P. M. Pilarski, “GVFs: General Value Freebies,” 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), July 7-10, McGill University, Montreal, Quebec, Canada, 2019. (Abstract and Poster)


8. Q. Boser, T. R. Dawson, E. Lavoie, A. Valevicius, P. M. Pilarski, A. Vette, C. Chapman, J. Hebert, “Characterizing the Eye Gaze Behaviour of Body-powered Prosthesis Users, ” The International Society for Prosthetics and Orthotics, RehabWeek 2019, 24-28 June, 2019, Toronto, Canada. (Abstract and Oral Presentation)


9. J. Castellanos Cruz, M. F. Gómez Medina, M. Tavakoli, P. M. Pilarski, K. Adams, “Predicting the toys that children and participants with cerebral palsy want to reach while controlling a haptic telerobotic system,” Rehabilitation Engineering and Assistive Technology Society of North America (RESNA), RehabWeek 2019, 24-28 June, 2019, Toronto, Canada. (Abstract and Poster)


10. M. F. Gómez Medina, J. Castellanos Cruz, A. M. Rios Rincon, P. M. Pilarski, K. Adams, “Comparison between two different prompting conditions when children use a Lego robot to perform a set of tasks,” Rehabilitation Engineering and Assistive Technology Society of North America (RESNA), RehabWeek 2019, 24-28 June, 2019, Toronto, Canada. (Abstract and Poster)


11. J. Gunther, A. Kearney, N. M. Ady, M. R. Dawson, P. M. Pilarski, “Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm,” Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019, IFAAMAS, pp. 1967–1969. (Extended abstract and poster.) (PDF)


12. Late 2017 and 2018 contributions still to be added.


13. J. Hebert, E. Lavoie, Q. Boser, A. Valevicius, A. Vette, P. M. Pilarski, C. Chapman, “3D-Gaze and Movement: a novel metric of visual attention to measure upper limb prosthetic function,” Proc. of MEC'17: Myoelectric Controls Symposium, Fredericton, New Brunswick, August 15-18, 2017. (Oral presentation and abstract.)


14. A. Valevicius, Q. Boser, E. Lavoie, C. Chapman, P. M. Pilarski, A. Vette and J. Hebert, “Kinematic Insights from a Novel Gaze and Movement Metric for Upper Limb Function: Normative and Prosthetic Comparison,” Proc. of MEC'17: Myoelectric Controls Symposium, Fredericton, New Brunswick, August 15-18, 2017. (Poster and abstract.)


15. J. Ventura, N. Ady, P. M. Pilarski, “An Exploration of Artificial Curiosity and Reinforcement Learning in a Simple Robot,” WISEST Poster Session, University of Alberta, 2017. (Poster)


16. J. B. Travnik, P. M. Pilarski, “Effective, Time-Efficient State Representations for Human-Machine Decision Making,” 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), June 11-14, The University of Michigan, Ann Arbor, Michigan, USA, 2017. (Spotlight oral presentation, poster and abstract.)


17. C. Sherstan, M. C. Machado, J. Travnik, A. White, G. Vasan, P. M. Pilarski, “Confident Decision Making with General Value Functions,” 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), June 11-14, The University of Michigan, Ann Arbor, Michigan, USA, 2017. (Poster and abstract.)


18. A. Kearney, V. Veeriah, J. Travnik, R. Sutton, P. M. Pilarski, “Every step you take: Vectorized Adaptive Step-sizes for Temporal-Difference Learning,” 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), June 11-14, The University of Michigan, Ann Arbor, Michigan, USA, 2017. (Poster and abstract.)


19. N. M. Ady, P. M. Pilarski, “Comparing Reinforcement Learning Methods for Computational Curiosity through Behavioural Analysis,” 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), June 11-14, The University of Michigan, Ann Arbor, Michigan, USA, 2017. (Poster and abstract.)


20. K. W. Mathewson, P. M. Pilarski, “Concurrent Human Control and Feedback Shaping for Robot Training with Actor-Critic Reinforcement Learning,” 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), June 11-14, The University of Michigan, Ann Arbor, Michigan, USA, 2017. (Poster and abstract.)


21. G. Vasan, P. M. Pilarski, “Mirrored Bilateral Training of a Myoelectric Prosthesis with a Non-Amputated Arm via Actor-Critic Reinforcement Learning,” 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), June 11-14, The University of Michigan, Ann Arbor, Michigan, USA, 2017. (Oral presentation, poster and abstract)


22. N. M. Ady, P. M. Pilarski, “Unifying Curious Reinforcement Learners,” Designing for Curiosity: An Interdisciplinary Workshop, ACM CHI Conference on Human Factors in Computing Systems (CHI 2017), Denver, CO, USA, May 6-11, 2017. (Extended abstract and poster.)


23. N. M. Ady, P. M. Pilarski, “Domains for Investigating Curious Behaviour in Reinforcement Learning Agents,” Women in Machine Learning (WiML) Workshop 2016, NIPS 2016, Barcelona, Spain, Dec. 05, 2016. (Poster and abstract.)


24. E. B. Lavoie, E. A. Crockett, O. Kovic, A. M. Valevicius, Q. A. Boser, P. M. Pilarski, A. H. Vette, J. S. Hebert, C. S. Chapman, “Establishing normative eye movement patterns during upper-limb functional tasks,” Neuroscience 2016: Society for Neuroscience Conference, San Diego Convention Center, San Diego, CA, November 12-16, 2016. (Poster and abstract.)


25. P. M. Pilarski, R. S. Sutton, A. L. Edwards, C. Sherstan, K. W. Mathewson, A. S. R. Parker, and J. S. Hebert, “Towards strong prosthetic machine intelligence,” Cybathlon Symposium 2016, Zurich, Switzerland, October 6, 2016. (Poster and abstract.)


26. Boser Q, Valevicius A, Lavoie E, Chapman CS, Pilarski PM, Hebert JS, Vette AH, “Comparison Of Anatomical And Cluster-Based Upper Body Marker Models,” 40th Annual Meeting of the American Society of Biomechanics. Raleigh, North Carolina. August 2-5, 2016.


27. Valevicius A, Boser Q, Lavoie E, Vette AH, Chapman CS, Pilarski PM, Hebert JS, “Normative Kinematic Data For Two Functional Upper Limb Tasks,” 40th Annual Meeting of the American Society of Biomechanics. Raleigh, North Carolina. August 2-5, 2016.


28. P. M. Pilarski, Craig sherstan, “Steps Toward Knowledgeable Neuroprostheses,” accepted to the Proceedings of the 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob2016), June 26-29, 2016, Singapore, pp. 220. (Extended abstract and poster.)


29. D. P. Manage, J. Lauzon, P. Ward, P. M. Pilarski, L. M. Pilarski, L. M. McMullen, “Cassette PCR for Rapid Detection of pathogenic Escherichia coli in Meat,” Biodefense World Summit, June 27, 2016, Baltimore, MD. (Poster, abstract, talk.)


30. P. A. Toniolo, P. M. Pilarski, C. Bach, J.D. Griffin, S. Adamia, “MicroRNAs as potential therapeutic agents for AML: Targeting the AML1-ETO Oncogene by pre-miR-520 and-373,” Proceedings of the AACR 106th Annual Meeting, April 18-22, Philadelphia, PA, 2015; and Cancer Research 75 (15 Supplement), pp. 3972-3972.


31. C. Sherstan, J. Modayil, P.M. Pilarski, “Direct Predictive Collaborative Control of a Prosthetic Arm,” poster, talk, and abstract in the Second Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Edmonton, Alberta, Canada on June 7-10, 2015. (Abstract) (Poster) (Talk)


32. A.L Edwards, M.R. Dawson, J.S. Hebert, C. Sherstan, R.S. Sutton, K.M. Chan, P.M. Pilarski, “Progress Toward the Shared Control of a Prosthetic Arm,” poster and abstract in the Second Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Edmonton, Alberta, Canada on June 7-10, 2015. (Abstract) (Poster)


33. M.R. Dawson, C. Sherstan, J.P. Carey, J.S. Hebert, P.M. Pilarski, “Development of the Bento Arm: An Improved Robotic Arm for Myoelectric Training and Research,” Glenrose Rehabilitation Hospital 2014 Spotlight on Research Breakfast and Symposium, Edmonton, Oct. 15, 2014. (Poster)


34. A. Kearney, A. Koop, M. Bowling, P.M. Pilarski, “Partition Tree Learning for Improved Control of Myoelectric Prosthetics,” 8th Annual Workshop for Women in Machine Learning, Co-located with NIPS, Lake Tahoe, Nevada, Dec. 05, 2013. (Oral and poster presentation.)


35. A.S.R. Parker, P.M. Pilarski, “Intelligent Communication and Control of Prosthetic Limbs,” 3rd Annual Undergraduate Research Symposium (University of Alberta, Edmonton, Alberta, Canada, November 22, 2013, abstract and poster presentation).


36. S. Adamia, C. Bach, P.M. Pilarski, M. Wadleigh, D.P. Steensma, G. Motyckova, D.J. DeAngelo, R.M. Stone, D.G. Tenen, J.D. Griffin, “Aberrant Splicing in Patients with AML is Associated with Over-Expression of Specific Splicing Factors,” Proceedings of the 55th ASH Annual Meeting and Exposition, New Orleans, LA, December 7–10, 2013 and in Blood, October 21, 2013, vol. 122, no. 21, pp. 3749. (Abstract)


37. A.S.R. Parker, P.M. Pilarski, “Enhanced Human-Machine Communication during Prosthesis Use,” 2013 Glenrose Rehabilitation Hospital Spotlight on Research Symposium (Edmonton, Alberta, Canada, October 23, 2013, abstract and poster presentation). (Poster)


38. P.M. Pilarski, C. Debes-Marun, P. Dumais, A. Belch, and L.M. Pilarski, “Development of an Automated Microfluidic FISH Platform for Point-of-Care Risk Stratification of Multiple Myeloma,” Proceedings of the XIVth International Myeloma Workshop (Kyoto, Japan, April 3–7, 2013, abstract and poster) and in Clinical Lymphoma Myeloma and Leukemia (supplement), Vol. 13, Supplement 1, pp. 178–179. (Poster)


39. J.S. Hebert, K.M. Chan, J. Olson, P.M. Pilarski, and M.R. Dawson, “A Novel Targeted Sensory Reinnervation Method to Improve Function of Myoelectric Prostheses after Arm Amputation,” 2012 ACRM-ASNR Annual Conference: Progress in Rehabilitation Research (Vancouver, Canada, October 9-13, 2012; abstract and 90 minute symposium session).


40. P.M. Pilarski, M.R. Dawson, T. Degris, F. Fahimi, J. Carey, R.S. Sutton, "Real-time Machine Learning in Rehabilitation Robotics for Adaptable Artificial Limbs," 2011 Glenrose Rehabilitation Hospital Research Symposium (Edmonton, Alberta, Canada, November 2, 2011, abstract and poster presentation). (Poster)


41. P.M. Pilarski, M.R. Dawson, T. Degris, F. Fahimi, J. Carey, R.S. Sutton, "Real-time Machine Learning in Rehabilitation Robotics for Adaptable Artificial Limbs," Tech Futures Summit 2011: Deploying Innovation (Banff, Canada, August 28-30, 2011, abstract and poster presentation). (Poster)


42. T. Degris, P.M. Pilarski, J. Modayil, R.S. Sutton, M.J. Cimolini, and J. Raso, "An Encouraging Mobile Robot in the Glenrose Rehabilitation Hospital," 7th International Congress on Industrial and Applied Mathematics, ICIAM 2011 (Vancouver, Canada, July 18?22, 2011, poster presentation.)


43. P.M. Pilarski, T. Degris, and R.S. Sutton “Small-Timescale Reinforcement Learning for Power Management on a Mobile Robot,”2010 Alberta Power Industry Consortium Power and Energy Innovation Forum (Edmonton, Canada, Nov. 4, 2010, invited poster presentation).


44. J. Modayil, P.M. Pilarski, A. White, T. Degris, and R.S. Sutton, "Off-Policy Knowledge Maintenance for Robots," Robotics: Science and Systems Workshop—Towards Closing the Loop: Active Learning for Robotics (Zaragoza, Spain, June 27-30, 2010, 2 page extended abstract and poster). (Extended Abstract)


45. P.M. Pilarski, M.R. Dawson, T. Degris, F. Fahimi, J. Carey, and R.S. Sutton, "Continuous Actor-Critic Methods for Adaptive Prosthetics," in proceedings of the 2010 MITACS / CORS Joint Annual Conference (Edmonton, Alberta, May 25-28, 2010; abstract and oral presentation).


46. A. Mahmood, T. Degris, P.M. Pilarski, and R.S. Sutton, "Robust Step-size Adaptation for Online Optimization," in proceedings of the 2010 MITACS/CORS Joint Annual Conference (Edmonton, May 25-28, 2010, abstract, poster, and oral presentation).


47. P.M. Pilarski, X.T. Su, D.M. Glerum, and C.J. Backhouse, "Disease Characterization Using Wide-Angle Light Scattering Patterns", in proceedings of the iCORE Alberta Electrical and Computer Engineering Graduate Research Symposium (University of Alberta, Edmonton, 22 June 2009, abstract and poster).


48. S. Adamia, A. Reichert, A. Ghosh, J. Hodges, P. Pilarski, S. Treon, M.J. Mant, T. Reiman, A.R. Belch, and L.M. Pilarski, "Gerraline and somatic mutations in the hyaluronan synthase-1 (HAS1) gene may contribute to oncogenesis in multiple myeloma (MM) and Waldenstrom's macroglobulinemia (WM)," Blood, Vol. 110(11): 733A-734A, 2007.


49. Pilarski, P. M., Sieben, V. J., Marun, C. D. & Backhouse, C. J., "Computer vision for fish screening in myeloma", in proceedings of the XIth International Myeloma Workshop (Kos 2007, poster), and Haematologica - The Hematology Journal, Vol. 92: 108-108, 2007.
    
    
50. Sieben, V. J., Debes-Marun, C., Pilarski, P. M.; Kaigala, G. K., Pilarski, L. M. & Backhouse, C. J., "Microchips for optimized fish screening in myeloma," in proceedings of the XIth International Myeloma Workshop (Kos 2007, oral), and Haematologica - The Hematology Journal, Vol. 92: 64-65, 2007.
    
    
51. V. J. Sieben, C. S. Debes-Marun, P. M. Pilarski, G. V. Kaigala, L. M. Pilarski, and C. Backhouse, "FISH AND CHIPS: Chromosomal analysis utilizing microfluidic platforms," in proceedings of Nanotechnology in Biomedicine (Keystone) (Tahoe City, California, USA, 2007, poster).
    
    
52. P.M. Pilarski, V.J. Sieben, C. Debes Marun, and C.J. Backhouse, "An artificial intelligence system for detecting abnormal chromosomes in malignant lymphocytes," proceedings of the iCORE Alberta Electrical and Computer Engineering Graduate Research Symposium (University of Alberta, Edmonton, 04 May 2007, abstract & poster).


53. Debes Marun CS, Sieben V, Pilarski PM, Reiman T, Belch AR, Pilarski LM. "FISH and Chips: Novel Point of Care Technology to Detect Chromosomal Abnormalities", Blood 108: 971a, 2006.
    
    
54. Adamia S, Hodges J, Pilarski PM, Teron S, Mant MJ, Reiman T, Belch AR, Pilarski LM. "Accumulation of inherited and acquired mutations in hyaluronan synthase 1 gene may contribute to oncogenesis in multiple myeloma and Waldenstrom's macroglobulinemia", Blood 108: 980a, 2006.
    
    
55. Pilarski, P.M., Sieben, V.J., Debes Marun, C., Backhouse, C.J., "An Artificial Intelligence System for Detecting Abnormal Chromosomes in Malignant Lymphocytes",  in proceedings of the Candian Society for Immunology conference (Halifax, Nova Scotia, 2006, oral, abstract, and poster).
    
    
56. Pilarski, P.M., Siu, N., Iverson, L., "Email Redirected: A User-centric Reinvention of Email Clients", in proceedings of the Advanced Systems Institute of British Columbia ASI Exchange 2004 (Vancouver , Canada, 2004, abstract and poster) .
    
    
57. Pilarski, P.M., Prakash, R., Adamia, S., Kaler, K.V.I.S., Backhouse, C.J. " Microvalving in Genetic Analysis Systems", poster for the International Conference on MEMS, Nano and Smart Systems (IEEE Conference) (Banff, AB, 2003).
    
    
58. Adamia, S., Pilarski, P.M., Lauzon, J., Backhouse, C.J., Pilarski, L.M. "Microfluidics Platforms for Molecular Monitoring of Human Cancer", in proceedings of the Alberta Cancer Board Annual Research Meeting ( Banff , AB, 2003, abstract and poster).
    
    
59. Adamia, S., Footz, T., Prakash, R., Pilarski, P.M., Pilarski, L.M., Backhouse, C.J. "Microfluidics platforms for genetic analysis", in proceedings of the American Society for Microbiology Bio-, Micro-, Nanosystems (New York, 2003, abstract and poster).
    
    
60. Adamia, S., Pilarski, P.M., Prakash, R., Lauzon, J., Backhouse, C.J. and Pilarski, L.M. "Microsystems and Cancer: Improved Detection of Disease Related Genes in Myeloma Patients Using Microfluidics Platforms", in proceedings of the Annual Meeting of the American Society of Hematology (San Diego, CA, 2003, abstract) and Blood 102 (11): 2528, 2003.
    
    

• CMPUT 607, Applied Reinforcement Learning, Winter 2018


• 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

• LABMP 590: Technology and the Future of Medicine


• "The Promise and Perils of AI (Part I): Introduction to Machine Intelligence," Sept. 12, 2013. (PDF) (Video)


• "The Promise and Perils of AI (Part II): Assistance, Augmentation, and Interfaces," Sept. 24, 2013. (PDF) (Video)


• "The Promise and Perils of AI (Part IV): Towards Transhumanism," Feb. 25, 2014. (PDF) (Video)


• "The Promise and Perils of AI (Part IV): Automating Life," Feb. 24, 2015. (PDF) (Video)


• "Pokemon Go and why AI Needs Poetry" discussion, and video of whole "Promise and Perils of AI (Part I): Introduction to Machine Intelligence" teaching session, Sept. 13, 2016. (PDF)

• “Artificial Intelligence and You: The Science and Future of Thinking Machines,” Learn @ Your Library Talk Series, Strathcona County Library, Sherwood Park, Alberta, Jan. 28 to Feb. 11, 2024. (PDFs: Day 1: Intro to Artificial Intelligence and Machine Learning; Day 2: Super-Powered Chatbots, Day 3: Becoming Bionic.


• “How to Train Your (Bionic) Body Parts,” Jasper Dark Sky Festival, Jasper, Alberta, Oct. 21, 2023. (PDF)


• “Redefining the Relationship Between Humans and their Assistive Technologies via Machine Learning and Artificial Intelligence,” ACRM Tech Talks Webinar Series, Aug. 30, 2023.


• “Artificial Intelligence is Changing the World. How Might it Change Education?” ATLE Spring Symposium, Edmonton, May 2, 2023. (PDF)


• “Limbs that Keep Learning: Constructivism in Human-Prosthesis Interaction,” MEC22, Fredericton, New Brunswick, Aug 11, 2022. (PDF)


• “Techniques from Reinforcement Learning in Bionic Medicine,” AI4Good Lab, Edmonton/Toronto/Montreal (Virtual), May 30, 2022. (PDF)


• “Artificial Intelligence in Bionic Medicine,” AI Week, Edmonton, Alberta, May 25, 2022. (PDF)


• “The Emerging Role of Machine-Learned Feedback in Neuroprostheses,” SMART Talks series, Edmonton, Alberta, May 13, 2022. (PDF)


• “Constructivism in Tightly Coupled Human-Machine Interfaces,” HARL (Human-aligned Reinforcement Learning for Autonomous Agents and Robots) Workshop at IEEE ICDL, August 27, 2021. (PDF)


• “Artificial Intelligence in Medicine: Definitions, Implications, and Future Impact,” Medical Grand Rounds, Dept.Medicine / Alberta Health Services, Edmonton, AB, Oct 30, 2020. (PDF)


• “(for once) On Time,” Amii AI Meetup, Edmonton, AB, Oct 15, 2020. (PDF)


• “Applied RL on Robots (Or, actually: Intelligence Amplification, Prediction Precedes Control in Research, and Please Stay Awake on the Last Day),” Deep Learning and Reinforcement Learning Summer School 2019 (DLRLSS), Edmonton, August 2, 2019. (PDF)


• “The Role of Predictions in Human-Prosthesis Joint Action,” Autonomy and Intelligence in Robotic Rehabilitation and Assistive Technologies, 2019 RehabWeek Workshop, Toronto, June 24, 2019. (PDF)


• “The Role of Predictions in Joint Action,” RLAI Tea Time Talk, Edmonton, June 19, 2019. (PDF, including backup slides with an assistive robotics context)


• "Artificial Intelligence in Medicine," Invited talk for the UofA Alumni Association Educated Luncheon, Edmonton, Alberta, Feb. 13, 2019. (PDF)


• "Artificial Intelligence in Thinking, Moving, and Perceiving," Invited talk for the Alberta MS Network Retreat, Banff, Alberta, Nov 23, 2018. (PDF)


• "Artificial Intelligence in Rehabilitation," Invited talk for the Canadian Association of Physical Medicine and Rehabilitation Annual Scientific Meeting, Whitehorse, Yukon, June 1, 2018. (PDF)


• “Advances in Machine Intelligence for Assistive Technologies,” Invited talk for the Spinal Cord Injury Community-Research Engagement Symposium, Calgary, Dec. 12, 2017.


• "Smarter Parts: Intelligence, Learning, and Communication in Human-Prosthesis Interaction," Psychology Departmental Seminar, University of Alberta, Edmonton, Nov.24, 2017. (PDF)


• “Gazing into the future of upper limb rehabilitation: The relevance of outcome measurement,” P. M. Pilarski & J. Hebert, invited talk, Campus Alberta Neuroscience Symposium 2017, Calgary, AB, Oct. 26, 2017.


• “Making the Most of Data from Brain-Body-Machine Interfaces,” Lunchalytics, Edmonton, Sept 20, 2017.


• “Measuring the Future of Brain-Body-Machine Interfaces: An Initial Position,” P. M. Pilarski, invited talk, American Orthotic & Prosthetic Association (AOPA) World Congress, Symposium on Multi-Scale Integration in Upper Limb Prosthetics, September 9th, 2017, Las Vegas, NV, USA.


• "Reinforcement Learning (without the math)," Invited workshop for the 2017 Student Developer Conference, Startup Edmonton, Shaw Conference Centre, Edmonton, Alberta, March 25, 2017. (PDF, Worksheet)


• “Reinforcement Learning Algorithms for Acquiring and Using Predictive Knowledge,” invited seminar speaker, for IBM Research - Zurich, Ruschlikon, Switzerland. Oct. 7, 2016.


• "Communicative Capital: Bridging the Gap between Users and their Assistive Technology," Invited talk for the Workshop on Advanced Intelligent Mechatronics for Neuromuscular Rehabilitation and Recovery Assessment in the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM2016), Banff, Alberta, Canada, July 12-15, 2016.


• “Knowledge,” RLAI Tea Time Talk, Edmonton, June 13, 2016. (PDF)


• "Machine Learning (and why you should care)," keynote, 2016 Student Developer Conference, Shaw Conference Centre, Edmonton, Alberta, March 12, 2016.


• "Smart Parts and Better Bionic Bodies", invited talk for Dark Matters: Robots, IMAX Theatre, Telus World of Science, Edmonton, March 10, 2016.


• “Building Communicative Capital during Human-Machine Collaboration,” invited talk for The 10th Barbados Workshop on Reinforcement Learning, Bellairs Research Institute, Barbados, April 25-28, 2016. (PDF)


• (missing data here ... please stand by)


• “Solving Artificial Intelligence (AI) with help from Intelligence Amplification (IA),” invited talk for The 9th Barbados Workshop on Reinforcement Learning, Bellairs Research Institute, Barbados, April 27-30, 2015. (PDF)


• “Knowledgeable Artificial Limbs,” Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico, Nov. 14, 2014. (Video seminar.)


• “Automating Life: the automated past, present, and future of human life on earth,” invited opening talk for the Ernest C. Manning Foundation Celebration of Innovation, Telus World of Science, Edmonton, AB, Canada, Oct. 25, 2014. (PDF)


• “Novel Control Strategies for Arm Prostheses - a Partnership between Man and Machine,” K. M. Chan & P.M. Pilarski, invited keynote for the 51st Japanese Association of Rehabilitation Medicine 2014 Annual Scientific Meeting, Nagoya, Japan, 5 June 2014.


• “Recent Advances in Upper-Limb Prosthetic Technology,” P.M. Pilarski, invited seminar for the International Society for Prosthetics and Orthotics (ISPO), Prosthetic & Orthotic Department, Queen Elizabeth Hospital, Hong Kong, China, 31 May 2014.


• “Knowledgeable neuroprostheses: machine intelligence for the improved control of next-generation artificial limbs,” invited seminar for Centre for Neuroscience Seminar Series, Edmonton, AB, Canada, Feb. 03, 2014.


• “Learning and Using Contextual Information in the Control of Assistive Devices,” P.M. Pilarski, invited presentation for the 2013 International Conference on Rehabilitation Robotics, 1st Workshop on Present and Future of Non-invasive Peripheral-Nervous-System-Machine Interfaces (Seattle, USA, June 26, 2013). (PDF)


• “Real-time Control with Temporally Extended Predictions: A Sensorimotor Approach to Planning?,” P.M. Pilarski, invited presentation for The 8th Barbados Workshop on Reinforcement Learning (Bellairs Research Institute, Barbados, April 27–May 3, 2013). (PDF)


• “Prediction and Control with Real-time Machine Learning,” P.M. Pilarski, invited seminar for the DBR Technical Series, Schlumberger Canada Ltd. (DBR Technology Centre, Edmonton, Alberta, June 19, 2012). (PDF)


• “Towards Intelligent Artificial Limbs and other Miraculous Thinking Machines,” P.M. Pilarski, invited talk for TEDxEdmonton 2012 (Edmonton, Alberta, June 9, 2012). (PDF)(Watch the Video)


• “Collaborative Algorithms for Human-Machine Interaction,” P.M. Pilarski, invited presentation for The 7th Barbados Workshop on Reinforcement Learning (Bellairs Research Institute, Barbados, April 15-18, 2012). (PDF)


• “Real-time Machine Learning for Assistive Medical Robotics,” P.M. Pilarski, invited seminar for the International Seminar Series on Biomedical Engineering, Dept. of Medical System Engineering (Chiba University, Japan, Nov. 18, 2011). (PDF)


• “Reinforcement Learning Artificial Intelligence in Rehabilitation for Adaptive Prostheses,” P.M. Pilarski, invited presentation, ICE: The Tech Conference; Alberta Council of Technologies / Alberta ICT Council Machine Learning Conference (Edmonton, Alberta, Nov. 3, 2010). (PDF)


• “Reinforcement Learning Artificial Intelligence for Robotic Adaptation to New Environments,” P.M. Pilarski, invited presentation, ICE: The Tech Conference; Alberta Council of Technologies / Alberta ICT Council Machine Learning Conference (Edmonton, Alberta, Nov. 3, 2010). (PDF)


• Panel member for "Off-loading Intelligence—When Machines Decide," Alberta Council of Technologies / Alberta ICT Council Machine Learning Forum (Edmonton, June 1, 2010).


• "Feature and Variable Selection: Context for Representation Learning in RL," P.M. Pilarski, invited presentation/tutorial, The 5th Barbados Workshop on Reinforcement Learning (Bellairs Research Institute, Holetown, Barbados, April 6-9, 2010). (PDF)


• "Machine Learning and Image Analysis for Lab-on-a-Chip Medical Diagnostics", P.M. Pilarski, invited seminar for Department of Computing Science Artificial Intelligence Seminar Series (University of Alberta, September 2009).
  
  
• "Computational intelligence and computer vision for use with emerging micro­ and nano­technology devices", P.M. Pilarski, invited seminar for the Reykjavik University School of Computer Science and School of Science and Engineering (Reykjavik University, Iceland, October 14, 2008).


• "A New Look at Rapid Medical Diagnostics", P.M. Pilarski, invited presentation for the University of Alberta Senate (Univeristy of Alberta, January 2008).
  
  
• "Nanobiotechnology: lab-on-a-chip for rapid medical diagnostics", P.M. Pilarski, invited talk, Operating Room Nurses of Alberta Annual Conference (Red Deer, Alberta, Oct 25, 2007).
  
  
• "Nanobiotechnology", P. M. Pilarski, V.J. Sieben, G.V. Kaigala, The Alberta Clinical Engineering Society Telehealth Educational Series (University of Alberta, Edmonton, 21 February 2006).
  
  
• "A Computational Method for Rapid Cellular Identification", P. M. Pilarski, 1st Annual Electrical and Computer Engineering Graduate Students' Association Conference (University of Alberta, Edmonton, 27 January 2006).
  
  
• "Cellular Level Computing:  rapid medical diagnostics for cancerous cell identification", P. M. Pilarski, invited research presentation, Netera Days: Exploring New Tools For Research (University of Alberta, Edmonton, 12 October 2005).
  
  
• "Thinking Small:  An Introduction to Nanotechnology", P. M. Pilarski, invited presentation for FGSR Outreach - Peace River Highschool (University of Alberta, Edmonton, June 2005).
  
  
• "Ants Marching: Swarm-Based Feature Characterization", P. M. Pilarski, invited research presentation, Facility for Advanced Computational Intelligence and Applications (University of Alberta, Edmonton, November 2004).
  
  
• "Intelligent Microvalving in Genetic Analysis Systems", P. M. Pilarski, invited research presentation, Cross Cancer Institute (Edmonton, July 2003).