Experimental systems
Designing and running movement experiments with real-time feedback, visual displays, and synchronized measurement.
Applied work
Applied methods for movement studies.
This is the practical side of my work: experiment control, signal processing, modeling, and analysis code. I focus on building modular, stateless, and resuable analysis code.
Signal processing
Working with EMG, force, acceleration, kinematic, and behavioral signals from movement tasks.
Modeling and analysis
Using statistical and computational models to interpret sensory uncertainty, adaptation, and behavioral variability.
Reproducible workflows
Organizing analysis code, notes, and figures so results can be checked later.
Methods
The tools affect the experiment.
Programming, hardware interfacing, and modeling matter because they shape what can be measured.
Working areas
Experimental control
Python, MATLAB, PsychoPy, NI-DAQmx, Spike2, and custom task logic for human movement experiments.
Movement and physiology data
Electromyography, force transducers, accelerometers, force plates, and biomechanical signal processing.
Analysis and modeling
Data cleaning, visualization, inferential statistics, Bayesian reasoning, state-space models, and cue-combination models.