Dr. Moorman specializes in the study of perception, behavior, and decision making. She has a background in cognitive and neuroscience and has conducted studies involving motor control, and visual and auditory processing. She has expertise with behavioral, psychophysical, electrophysiological, and computational research methods. She has applied her expertise to the analysis of a wide variety of issues including human interaction with consumer products, adequacy and effectiveness of warnings, child safety, smoke alarm effectiveness, and driver behavior.
In her Ph.D. work Dr. Moorman designed and conducted studies of brain-computer interface systems for direct neuro-prosthetic control of movement, with the ultimate goal of developing interventions to treat paralysis due to spinal cord injury and neuromuscular disease. She has authored a number of peer-reviewed scientific articles on motor control and brain-computer interface technology.
Dr. Moorman has expertise in behavioral research methods, experimental design, and quantitative data analysis. She has performed qualitative and quantitative analysis of data of large, nationally representative samples of consumer-product related injury and fatality databases and of consumer complaint databases. She is proficient in the Python programming language and has performed quantitative analysis on large data sets using basic statistical and machine learning methods.
Dr. Moorman is also experienced in the written and visual communication of complex scientific and technical concepts and held a lead editorial position at UC Berkeley’s award-winning popular science magazine.
CREDENTIALS & PROFESSIONAL HONORS
- Ph.D., Neuroscience, University of California, Berkeley, 2015
- B.S., Brain and Cognitive Science, Massachusetts Institute of Technology (MIT), 2008
Orsborn AL, Moorman HG, Overduin SA, Shanechi MM, Dimitrov DF, Carmena JM. Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control. Neuron 2014; 82:1380–1393.
Shanechi MM, Orsborn AO, Moorman HG, Gowda S, Carmena JM. High-Performance Brain-Machine Interface Enabled by an Adaptive Optimal Feedback-Controlled Point Process Decoder. In the Proceedings of the 36th Annual International Conference of the IEEE EMBS on Neural Engineering 2014.
Dangi S, Gowda S, Moorman HG, Orsborn AL, So K, Shanechi M, Carmena JM. Continuous Closed-Loop Decoder Adaptation with a Recursive Maximum Likelihood Algorithm Allows for Rapid Performance Acquisition in Brain-Machine Interfaces. Neural Comput 2014; 26:1811–1839.
Gowda S, Orsborn AL, Overduin SA, Moorman HG, Carmena JM. Designing Dynamical Properties of Brain-Machine Interfaces to Optimize Task-Specific Performance. IEEE Trans Neural Syst Rehabil Eng 2014; 22:911–920.
Ajemian R, D'Ausilio A, Moorman H, Bizzi E. A Theory for How Sensorimotor Skills are Learned in Noisy Neural Circuits. PNAS 2013; 110(52):E5078-87.
Dangi S, Orsborn AL, Moorman HG, Carmena JM. Design and Analysis of Closed-Loop Decoder Adaptation Algorithms for Brain-Machine Interfaces. Neural Comput 2013; 25:1693–1731.
Orsborn AL, Dangi S, Moorman HG, Carmena JM. Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions. IEEE Trans Neural Syst Rehabil Eng 2012; 20:468–477.
Orsborn AL, Dangi S, Moorman HG, Carmena JM. Exploring time-scales of closed-loop decoder adaptation in brain-machine interfaces. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEEEng Med Biol Soc Conf 2011; 2011:5436–5439.
Ajemian R, D'Ausilio A, Moorman H, Bizzi E. Why professional athletes need a prolonged period of warm-up and other peculiarities of human motor learning. J Mot Behav 2010; 42(6):381-8.
Moorman HG, Gowda S, Carmena JM. Neural control strategies in a closed-loop brain-machine interface with a 4 degree of freedom redundant actuator. Poster presentation, Society for Neuroscience annual meeting, Washington, D.C., November 2014.