I studied the brain through big data and high dimensional mathematics. I improved my data practices from Brainhack events and the ReproNim/INCF fellowship, now I hope to make some contributions outside of academia.
This website is built from the personal jekyll theme. You can click on the following links to star and fork the original repo to replicate the website for your own.
The examples displayed here are taken from Kevin Murphy’s probabilistic machine learning Colab notebook. I found the code to be very instructive when paired with the math formulations, and thought I’d expand the logic here for people hoping to explore probabilistic modeling with tensorflow-probability.
Linear regression is a basic curve fitting technique using a straight line model to approximate the data trend. However, when the data is noisy, scientists need to understand the assumptions and quantify the reliability of their curve fitting methods. A probabilistic approach to modeling enables the quantification of uncertainty, but implementing this...
Became a data scientist!
Finished the Ph.D!
Attended Neuromatch Deep Learning!
Organized Brainhack NY!
Settled back home in NYC!
Became a ReproNim/INCF Fellow!
Attended Neurohackademy in Seattle!
Moved to San Francisco!
Drop me an email about cool data projects or questions about my work!