Biography
Dr. Ali Nasr is a highly accomplished engineer and researcher with a diverse and impressive background in the field of Intelligent Autonomous Software and Real-Time Embedded Control System Engineer. Driven by an insatiable curiosity for innovation and technology, Dr. Nasr continues to make significant strides in the engineering. His impressive knowledge and expertise have not only contributed to cutting-edge research but also inspired and enriched the academic experiences of future engineers and researchers. His achievements, expertise, and contributions to academia and industry are what truly matter and determine his success, regardless of considerations of race, gender, or sexual orientation, which are mainly prioritized in academic recruitment.
Dr. Nasr received his PhD degree from the Department of Systems Design Engineering, with a specialization in biomechatronics engineering, at the University of Waterloo and the Waterloo Artificial Intelligence Institute. His PhD research focused on 1) Scalable musculoskeletal model for dynamic simulations of body movement, 2) physics-based computer simulation of human-robot interaction, 3) biomechnaical signal processing for autonomous control of human-robot, 4) optimal design of active-passive shoulder exoskeletons, and 5) real-time assist-as-needed hierarchical control of wearable robots and practical implementation of human-in-the-loop. Prior to this, he pursued his passion for mechatronics engineering by earning a Master of Science degree from K.N. Toosi University of Technology in Iran in 2016. His academic journey began with a Bachelor of Science in Mechanical Engineering from Isfahan University of Technology in 2013.
As a Postdoctoral Research Associate, Dr. Nasr was involved in groundbreaking projects showcasing his abilities in designing and implementing platform software for advanced application features. His dedication to education was also evident through his role as a Graduate Teaching Assistant, where he made significant contributions to the academic community, recognized and appreciated by both students and faculty.
Throughout his academic journey, Dr. Nasr has been honored with several prestigious awards, including the Best Paper Award in the Area of Multibody Dynamics from the American Society of Mechanical Engineers (ASME), the 3rd place award at the University of Waterloo Artificial Intelligent GRADflix, 1st Place Award at the Graduate Student Symposium of Systems Design Engineering, and the best paper award in the 7th International Conference of Control, Dynamic Systems, and Robotics. His research findings have been published in reputable journals and presented at esteemed conferences worldwide.
Dr. Nasr is currently serving as a visiting researcher (volunteer), where he continues to contribute to the academic community in various capacities. He actively follows up with academic journals for publication reviews related to his PhD thesis. Moreover, he volunteers his time to assist students with their projects, providing guidance and support to enhance their learning experience and foster a collaborative academic environment. Dr. Nasr's commitment to knowledge sharing and academic excellence remains unwavering, reflecting his dedication to advancing research and education in his field.
Publications with Uiversity of Waterloo Affiliation
Scientific Journal Papers
[12] Bell S, Nasr A, and McPhee J. (2024) “General muscle torque generator model for a two degree-of-freedom shoulder joint,” ASME, Journal of Biomechnacal Engineering, pp.1-8
[11] Nasr A and McPhee J. (2024) “Scalable musculoskeletal model for dynamic simulations of lower body movement,” Taylor & Frances, Computer Methods in Biomechanics and Biomedical Engineering, pp.1‑27
[10] Nasr A, Dickerson CR, and McPhee J. (2023) “Experimental study of fully‑passive, fully‑active, and active‑passive upper‑limb exoskeleton efficiency: An assessment of lifting tasks,” MDPI, Sensors, 24 (1) p.63
[9] Nasr A, Bell S, Whittaker RL, Dickerson CR, McPhee J. (2023) “Robust machine learning mapping of sEMG signals to future actuator commands in biomechatronic devices,” Springer, Journal of Bionic Engineering, pp.1‑18
[8] Nasr A, Hunter J, Dickerson CR, and McPhee J. (2023) “Evaluation of a machine learning‑driven active‑passive upper limb exoskeleton robot: Experimental human‑in‑the‑loop study,” Cambridge University Press, Wearable Technologies, 4 p.e13
[7] Nasr A, Hashemi A, and McPhee J. (2023) “Scalable musculoskeletal model for dynamic simulations of upper body movement,” Taylor & Frances, Computer Methods in Biomechanics and Biomedical Engineering, pp.1‑32
[6] Nasr A, Bell S, and McPhee J. (2023) “Optimal design of active‑passive shoulder exoskeletons: A computational modeling of human‑robot interaction,” Springer, Multibody System Dynamics, 57 pp.73–106
[5] Febrer‑Nafria M, Nasr A, Ezati M, Brown P, Font‑Llagunes JM, McPhee J. (2022) “Predictive multibody dynamic simulation of human neuromusculoskeletal systems: A review,” Springer, Multibody System Dynamics, pp.1‑41
[4] Nasr A, Hashemi A, and McPhee J. (2022) “Model‑based mid‑level regulation for assist‑as‑needed hierarchical control of wearable robots: A preliminary study of human‑robot adaptation,” MDPI, Robotics, 11 (1) p.20
[3] Nasr A, Ferguson S, and McPhee J. (2022) “Model‑based design and optimization of passive shoulder exoskeletons,” ASME, Journal of Computational and Nonlinear Dynamics, 17 (5) p.051004
[2] Nasr A, Inkol KA, Bell S, and McPhee J. (2021) “InverseMuscleNET: Alternative machine learning solution to static optimization and inverse muscle model,” Frontiers, Computational Neuroscience, 15 p.759489
[1] Nasr A, Bell S, He J, Whittaker RL, Jiang N, Dickerson CR, and McPhee J. (2021) “MuscleNET: Mapping electromyography to kinematic and dynamic biomechanical variables by machine learning,” IOP, Journal of Neural Engineering, 18 p.0460d3
Scientific Conferance Abstracts
[11] Haraguchi N, Nasr A, Inkol KA, Hase K, and McPhee J. (2023) “Human and passive lower‑limb exoskeleton interaction analysis: Computational study with dynamics simulation using nonlinear model predictive control,” In Proceedings of the Society of Instrument and Control Engineers Annual Conference, Tsu, Japan
[10] McPhee J and Nasr A. (2023) “Multibody system dynamics: A fundamental tool for biomechatronic System design,” In Proceedings of the 11th European Community on Computational Methods in Applied Science Thematic Conference on Multibody Dynamics (ECCOMAS), Lisbon, Portugal
[9] Nasr A and McPhee J. (2023) “Computational evaluation of exoskeleton controllers with a scalable biomechatronics model,” In Proceedings of 8th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE), Paris, France
[8] Nasr A and McPhee J. (2022) “Multibody constrained dynamic modelling of human‑exoskeleton: Toward optimal design and control of an active‑passive wearable robot,” In Proceedings of the 6th International Conference on Multibody System Dynamics (IMSD-ACMD), New Dehli, India
[7] Nasr A and McPhee J. (2022) “Biarticular MuscleNET: A machine learning model of biarticular muscles,” In Proceedings of the North American Congress of Biomechanics (NACOB), Ottawa, Canada
[6] Nasr A, Ferguson S, and McPhee J. (2021) “Model‑based design and optimization of passive shoulder exoskeletons,” In Proceedings ofthe ASME 2021 International Design Engineering Technical Conferences Computers and Information in Engineering Conference (IDETC-CIE), Virtual
[5] Nasr A, Laschowski B, and McPhee J. (2021) “Myoelectric control of robotic leg prostheses and exoskeletons: A review,” In Proceedings of the ASME 2021 International Design Engineering Technical Conferences Computers and Information in Engineering Conference (IDETC-CIE), Virtual
[4] Nasr A, He J, Jiang N, and McPhee J. (2021) “Optimum filtering feature and manipulation steps of raw sEMG signal processing in application of muscle learning‑mathematical modeling, ” In Proceedings of the 45th American Society of Biomechanics Annual Conference (ASB), Virtual
[3] Nasr A, He J, Jiang N, and McPhee J. (2020) “Activation torque estimation of muscles by forward neural networks (Forward‑MuscleNET) for sEMG‑based control of assistive robots,” In Proceedings of the 7th International Conference of Control, Dynamic Systems, and Robotics (CDSR), Virtual, p.146
[2] Nasr A and McPhee J. (2020) “Control‑oriented muscle torque (COMT) model for EMG‑based control of assistive robots,” In Proceedings of the 7th International Conference of Control, Dynamic Systems, and Robotics (CDSR), Virtual, p.144
[1] Nasr A, Arami A, and McPhee J. (2019) “Optimal cost function for predicting upper‑limb movement with external load,” In Proceedings of the 16th annual Ontario Biomechanics Conference (OBC), Nottawasaga, Canada
Thesis
[1] Nasr A. (2022) “Design, Dynamics, and Control of Active-passive Upper-limb Exoskeleton Robots,” PhD Thesis, University of Waterloo, Waterloo ON Canada
Under Review
[5] Nasr A, Inkol KA, and McPhee J. (2024) “Comparison of assist‑as‑needed computed‑torque and model predictive control of robotic upper‑limb exoskeletons: An experimental and computational study of human‑robot interaction,” Cambridge University Press, Wearable Technologies
[4] Nasr A, Inkol KA, and McPhee J. (2024) “Safety in wearable robotic exoskeletons: Design, control, and testing guidelines,” ASME, Journal of Mechanisms and Robotics
[3] Nasr A, Zhu K, and McPhee J, “Synthetic human motion data to support 3D pose, motion, kinetic, and muscle estimation,” Springer, Multibody System Dynamics
[2] Nasr A and McPhee J. (2024) “Simulation of human dynamic data: Toward markerless 3d human pose, torque, and muscle estimation,” In Proceedings of the 19th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE), Vancouver, Canada
[1] Zhu K, Nasr A, Wong A, and McPhee J, “3D human pose and torque estimation from monocular video", In Proceedings of the 4th Workshop on Physics Based Vision meets Deep Learning in Conjunction with CVPR2024.
Under Preparation
[1] Nasr A and McPhee J, “Harnessing biomechanical energy in upper‑limb exoskeletons: Efficient energy regeneration,”
• Email: a.nasr@uwaterloo.ca
• Publication List (Google Scholar)
• CV/Resume (Linkedin)
