Illumina

Hi Kimberly,

PNAS paper on Alzheimer's Disease genomics & Machine Learning paper

Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted PNAS paper), that we briefly looked over on Tuesday: http://bit.ly/forillumina. (n.b. you asked about functional mapping of the new implicated SNPs; slides 165-181 use FUMA to explore, a bit, this relationship).

IDOT Comprehensive Truck Study

Here is a draft of the comprehensive report I made for the Illinois Department of Transportation Truck Study. Again this study might not be relevant other than to illustrate how I coped with messy data, and some interesting data viz. e.g. below is showing how, for a particular highway, traffic volume (z-axis) has changed between 1975-2015 (year) especially entering Chicago (milepost 1-40). It also shows the pavement quality rating as the colored topographic plot (pins represent major reconstructions). 

Blog & Wiki

Here is a link to my blog, where I mostly talk about data science stuff. Also feel free to browse this wiki, where I post a variety of resources, like how to model Brownian Motion).

Misc. Project Video Clips

I have experience generating simulations based on biological measurements (e.g. molecular kinetics, diffusion coefficients, association/dissociation rates). Here's one showing the diffusion of receptors along the surface of a dendritic membrane. Those receptors can enter and exit dendritic spine areas that are simulating actin polymerization.

1. First I create a 3D mesh dendritic segments using Fenics-Dolfin in Python
2. I then worked out the dynamics for actin polymerization in dendritic spines
3. Then I worked on a simulation of receptors diffusing along a 'ruffled' membrane
4. Finally I put all of these components together to show how dendritic spines capture receptors using actin filaments

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I created a program that is now used by all the labs in the Center for Neural Circuits, that imports a video of neural activity (GCaMP activity), and automatically finds ROIs, based on a number of segmentation techniques. It then isolates that activity of each ROI for further processing:

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Here's another app that I made, that's become popular in the neuro department - it's a tool to quantify neuron morphology (youtube_clip)

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I also have experience working with DAQs to interface software and hardware:

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This one is a little silly, but if you've ever wanted to get the raw data from a graph in a published paper, I made a program that will parse data from an image of a scatter plot and create a table of the raw data:

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I created a program that recorded user behavior as they interacted with our iPhone/Android app. It would record the current app screen and also tracked a user's finger (via a sticker) as they interacted with the app. This allowed us to determine, for example, if they kept tapping on regions of the screen that were not clickable. This let us optimize the app in a number of ways to make it more user friendly:

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