Faculty Directory
Todd Murphey

Professor of Mechanical Engineering


2145 Sheridan Road
Tech B286
Evanston, IL 60208-3109

847-467-1041Email Todd Murphey


Neuroscience and Robotics Lab (NxR)


Mechanical Engineering


Master of Science in Robotics Program

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Ph.D. Control and Dynamical Systems, California Institute of Technology, Pasadena, CA

B.S. Mathematics (summa cum laude), University of Arizona, Tucson, AZ

Research Interests

Professor Murphey's research focuses on computational methods in data-driven control, information theory in physical systems, and embodied intelligence.  Example projects include robotic exploration using electrosense, robotic exploration using mechanical contact, human-in-the-loop control, and shared control for rehabilitation/assistive devices.  

Significant Recognition

  • National Science Foundation CAREER award (2006)
  • Defense Science Study Group 2014-2015

Significant Professional Service

  • Editor for IEEE Transactions on Robotics (2014-2018)
  • Member: Air Force Scientific Advisory Board (2019-present)

Selected Publications


    K. Fitzsimons, A. M. Acosta, J. Dewald, and T. D. Murphey, “Ergodicity reveals assistance and learning in physical human robot interaction,” Science: Robotics, vol. 4, no. 29, p. eaav6079, 2019.

    I. Abraham and T. D. Murphey, “Active learning of dynamics for data-driven control using Koopman operators,” IEEE Transactions on Robotics, 2019.


    W. Savoie, T. A. Berrueta, Z. Jackson, A. Pervan, R. Warkentin, S. Li, T. D. Murphey, K. Wiesenfeld, and D. I. Goldman, “A robot made of robots: emergent transport and control of a smarticle ensemble,” Science: Robotics, 2019.

    T. Berrueta, A. Pervan, K. Fitzsimons, and T. Murphey, “Dynamical system segmentation for information measures in motion,” IEEE Robotics and Automation Letters, 2019.

    E. Tzorakoleftherakis and T. D. Murphey, “Iterative sequential action control for stable, model-based control of nonlinear systems,” IEEE Transactions on Automatic Control, 2019.

    G. Mamakoukas, M. Maciver, and T. D. Murphey, “Feedback synthesis for underactuated systems using sequential second-order needle variations,” International Journal of Robotics Research, 2019.

    I. Abraham and T. Murphey, “Decentralized ergodic control: Distribution-driven sensing and exploration for multi-agent systems,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 2987–2994, 2018.

    A. Mavrommati, E. Tzorakoleftherakis, I. Abraham, and T. D. Murphey, “Real-time area coverage and target localization using receding-horizon ergodic exploration,” IEEE Transactions on Robotics, vol. 34, no. 1, 2018.


    M. A. MacIver, L. Schmitz, U. Mugan, T. D. Murphey, and C. D. Mobley, “A massive increase in visual range preceded the origin of terrestrial vertebrates,” Proceedings of the National Academy of Science (PNAS), vol. 114, no. 12, pp. E2375–E2384, 2017.


    E. Tzorakoleftherakis, T. D. Murphey, and R. A. Scheidt, “Augmenting sensorimotor control using goal-aware vibrotactile stimulation during reaching and manipulation behaviors,” Experimental Brain Research, vol. 234, no. 8, pp. 2403–2414, 2016.

    T. Caldwell and T. D. Murphey, “Sufficient descent and backtracking for optimal mode scheduling,” Nonlinear Analysis: Hybrid Systems, vol. 21, pp. 59–83, 2016.

In the Classroom

Professor Murphey developed the Coursera Massive Open Online Course (MOOC) "Everything Is the Same: Modeling Engineered Systems" and had over 18,000 students enroll in Autumn, 2013. The course was based on one of the core undergraduate classes in systems analysis (EA3), a class he has innovated through development of classroom experiments. Moreover, he has developed the ME 314 Machine Dynamics course, focusing on the application of variational analysis to simulation and design of mechanisms. He has additionally developed ME 454, an introduction to numerical methods in optimal control. In all these courses, Professor Murphey focuses on project-based learning.