Meeko Oishi

Meeko Oishi
Assistant Professor

Contact Information:
(505) 277-0299
oishi@unm.edu
faculty website

Degrees: Ph.D. in Mechanical Engineering, with Ph.D. Minor in Electrical Engineering, Stanford University, 2004.
M.S. in Mechanical Engineering, Stanford University, 2000.
B.S.E. in Mechanical Engineering, Princeton University, 1998

Prior to joining UNM, Dr. Oishi held postdoctoral positions at Sandia National Laboratories and the National Ecological Observatory Network (NEON), and a faculty position in Electrical and Computer Engineering at the University of British Columbia at Vancouver. She is the recipient of a Peter Wall Institute Early Career Scholar Award, the Truman Postdoctoral Fellowship in National Security Science and Engineering, and a Science and Technology Policy Fellowship at the National Academies.

Dr. Oishi's current research is in providing guarantees of safety and performance in cyberphysical systems through careful design of controllers and user-interfaces (for systems that are not fully automated). Techniques her research group has developed have been applied to aircraft flight management systems, automated anesthesia delivery, and most recently to collaborative control of powered wheelchairs.

Dr. Oishi also focuses on characterization of motor performance in Parkinson’s disease. Linear dynamical systems have been shown to be effective models of manual pursuit tracking, and system parameters such as damping ratio and natural frequency potential biomarkers. Her work on high-fidelity characterization of motor processes, and correlation of these models with observable brain processes (e.g., through fMRI or EEG data) can provide insight into compensatory mechanisms in the brain in Parkinson’s disease.

Notable publications:
* “Hybrid system verification: Application to user-interface design,” M. Oishi, I. Mitchell, A. Bayen, and C. Tomlin. IEEE Transactions on Control Systems Technology, p. 229-244, Vol. 16, No. 2, March 2008.
* “Assessing manual pursuit tracking in Parkinson's disease via linear dynamical systems,” M. Oishi, P. TalebiFard, and M. J. McKeown. Annals of Biomedical Engineering, vol. 39, no. 8, p. 2263-2273, August 2011.