Sean G. Carver's Research Interests

Much of my current research involves projects related to the statistical analysis of models using simulated data.

• Overlapping software projects: KLI_R (R/Github/Git) and KLI (Python/Bitbucket/Mercurial). These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.

Future Conference Proceeding: I will present this work at the Joint Statistical Meeting (JSM) in Baltimore, August 2017.

• Baseball: how many innings must be played by model of the Baltimore Orioles (fitted from actual Orioles home games) to reject the model that the New York Yankees are playing. This statistic provides an interpretable way of quantifying the similarity of models.

Student collaborator (just graduated, but still working with me) Rebeca Berger.

Future Conference Proceeding: I will present this work at JSM, Baltimore, August 2017.

• Motor Control: specifically continuation tapping. In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by Normal and Inverse Gaussian distributions, both were better than the Laplace distribution. Contrary to this finding, the only relevant study in the literature we discovered, a review paper, reported that inter-tap intervals have Laplace distribution. I am working with Daniel Scanlan to explore through simulations, when models of continuation tapping produce data that fit the Inverse Gaussian/Normal distributions (these two are almost identical at our parameters) and when they produce data that fit the Laplacian distribution.

Student Collaborators: (current) Daniel Scanlan, (former) Wasim Ashshowaf, and (former) Alexander Spinos

Future Paper: Daniel and I plan to submit this work for publication, probably in PLoS One.

• Ion Channels in Neuroscience: Ion channels provide much of the molecular basis for neural signaling. Models of ion channels are continuous time Markov chains with hidden states, far more complicated than any of the applications above. Much of the KLI in python code involves these models of ion channels.