My main passion is in discovering statistical trends in data. In the future, I hope my analytics will serve the public good. But at this stage, my outreach is limited to education and entertainment, so I always try to couple my analyses with open code and data. Some of my projects have included:
- Burritos in San Diego   
- Poster popularity at the Society for Neuroscience Meeting  
- Currency trading  
In addition to those projects, I’m also interested in:
- Data visualization [Tableau vizs]
- Writing miscellaneous Python tutorials tangentially related to data analysis   
- Probability and math   
Neuroscience research (CV)
- Oscillation shape. (Cole & Voytek 2017) Currently, almost all frequency analysis approaches implement Fourier techniques. However, this presumes a sinusoidal basis whereas neural oscillations are commonly nonsinusoidal. We are developing methods to extract information from the waveform shape.
- Parkinson’s Disease. (Cole et al., 2016) This preprint presents an argument that neural activity in the motor cortex of Parkinson’s Disease patients is hypersynchronous.
- Phase-amplitude coupling (PAC) is a phenomenon in neural electrophysiology in which oscillations at two different frequencies are coupled to one another. Along with post-doc Erik Peterson, we created a library for estimating PAC (Python) (MATLAB)
- Filtering neural signals and processing oscillation amplitude (Lecture, Problem set)
- Calculating phase and coherence in neural signals (Lecture, Problem set)
- Spectral analysis tutorials (Voytek Lab) - Our lab releases tutorials (with IPython Notebook & Binder) on the techniques we use to analyze neural signals
- Empirical mode decomposition (EMD) tutorial - A decomposition method to separate a temporal signal into its component oscillations, an alternative to Fourier analysis