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Miriam Zacksenhouse

Miriam Zacksenhouse

Head of Brain-Computer Interfaces for Rehabilitation and Sensory-Motor Integration Labs
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Education

  • 1977 – B.Sc., in Mathematics and Physics, The Hebrew University of Jerusalem, Israel
  • 1980 – B.Sc., in Mechanical Engineering, Technion – I.I.T.
  • 1982 – M.Sc., in Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Mass. USA
  • 1993 – Ph.D., in Electrical & Computer Engineering, Rice University, Houston, Texas USA

Research Interests

  • Machine Learning for Robotic Control, (Reinforcement learning, Impedance control, Robotic assembly, Walking robots).
  • Brain Computer Interfaces (BCIs) (EEG-based BCIs, Invasive BCIs, Signal processing of neural data, Neural correlated of errors).
  • Computational Motor control
  • Biologically inspired control of dynamic walking bipedal robots
  • Central Pattern Generators (CPGs)

Guest Appointments

  • Visiting research scientist, Center for Neuro-engineering, Dept of Biomedical Engineering, Duke University, Durham, USA. (2003-2004).
  • Postdoctoral research associate, Center for Higher Brain Functions, Weizmann Institute of Science (1994-1995)
  • Postdoctoral research associate, department of Electrical & Computer Engineering, Rice University, Houston, TX (1993-1994)

Industrial Experience

  • Principal Engineer and project leader, Artificial Intelligence branch, Lockheed Engineering and Science Company, Houston, TX (1987-1993)
  • Engineer and project leader, NL – Industries/Technology Systems, Houston, TX (1982-1986)

Public Professional Activities

Honors and Awards

  • Research Award from the Miriam and Aaron Gutwirth Science – based Industries Center, Technion (2000)
  • Annie and Charles Corrin Academic Lectureship Award, Technion (1997)
  • Samuel and Esther August Academic Lectureship (1996)

Selected Publications

  • Wallace, D.M., M. Benyamini, M., M. S. Wilsey, P. G. Patil, C. A. Chestek and M. Zacksenhouse, Error detection and correction in intracortical brain-computer interfaces controlling two finger groups, J. of Neural Engineering, 20(4) 046037, 2023.
  • Kozlovsky, S., E. Newman, and M. Zacksenhouse, Reinforcement Learning of Impedance Policies for Peg-in-Hole Tasks: Role of Asymmetric Matrices. IEEE Robotics and Automation Letters, 7(4), 10898-10905, 2022.
  • Spector O. and M. Zacksenhouse, Learning Contact-Rich Skills Using Residual Admittance Policy. IROS 2021 (IEEE/RSJ Int. Conf. on Intelligent Robots and Systems), Virtual Conf., Sep. 2021.
  • Benyamini M., I. Demchenko and M. Zacksenhouse, Error related EEG potentials evoked by visuo-motor rotations. Brain Research, 1769: 147606, 2021.
  • Schallheim I. and M. Zacksenhouse, Policy gradient optimization of controllers for natural dynamic mono-pedal gait. Bioinspiration & Biomimetics, 15(3): 036010. https://doi.org/10.1088/1748-3190/ab782a, 2020.
  • Gindin I., M. Benyamini, and M. Zacksenhouse, Effects of model inaccuracies on reaching movements with intermittent control, PLoS-ONE 14(10): e0224265; https://doi.org/10.1371/journal.pone.0224265, Oct. 2019.
  • Sidorov E. and M. Zacksenhouse, Lyapunov based estimation of the Basin of Attraction of Poincar’e maps with applications to limit cycle walking. Nonlinear Analysis: Hybrid Systems, 33: 179-194, 2019.
  • Hartston R., R. Yakar, R. Katz and M. Zacksenhouse, Implementation of a Natural Dynamic Controller on an Under-actuated Compass-Biped Robot, In 2019 IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), pp:2273-2278, IEEE, Macau, China, Nov. 2019.
  • Buki E., R. Katz, M. Zacksenhouse, and I. Schlesinger, Vib-bracelet: a passive absorber for attenuating forearm tremor. Medical & Biological Engineering & Computing, 4:1-8, 2017.
  • Demchenko, I., R. Katz, H. Pratt, and M. Zacksenhouse, Distinct electroencephalographic responses to disturbances and distractors during continuous reaching movements. Brain Research, 1652, 178-187, 2016.
  • Spitz J., A. Evstrachin, and M. Zacksenhouse, Minimal feedback to a rhythm generator improves the robustness to slope variations of a compass biped, Bioinspiration & Biomimetics, 10(5) 056005, 2015.
  • Benyamini M. and M. Zacksenhouse, Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces, Frontiers in Systems Neuroscience, 9:71, http://dx.doi.org/10.3389/fnsys.2015.00071, 2015.
  • Spitz J., E. Sidorov, and M. Zacksenhouse, Humanoids can take advantage of crab-walking gaits, Int. J. Humanoid Robots 12(1), 1550004:1-23, DOI: 10.1142/S0219843615500048, 2015.
  • Machine Learning for Robotic Control
  • Invasive Brain Machine Interfaces (BMIs)
  • Non-invasive Brain Computer Interfaces (BCIs)
  • Computational Motor control
  • Neural oscillators and control of rhythmic movements
  • Biologically inspired control of dynamic walking of bipedal robots
  • Central Pattern Generators (CPGs)
Are you interested in learning the profession of the future?
Faculty of Mechanical Engineering, Technion - Israel Institute of Technology, Haifa

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