Thanks for visiting!

This site is intended to help explain who I am and who I want to be.

A little bit about me

My name is David(Dave) P. Van Anda. I’m currently based in New Jersey and am a graduate student at Indiana University Bloomington studying Data Science with a particular interest in Machine Learning and Complex Systems.

I also work full-time at a textile manufacturer where I program knitting machines. My biggest accomplishment there has been to build a GUI using Java that converts a .bmp into encoded text complete with machine knitting instructions and translated into a unique industry-specific programming language. (https://github.com/d141/Barcode-12.10.18) The GUI is used almost continuously on a daily basis and maintained regularly.
My next project is going to be building a tool that uses Machine Learning for this same process but beginning at a much earlier stage.

Check out my Twitterbot that tweets alerts about our local healthcare system (https://github.com/d141/Twitter-Bots). This is a solution for my Mom who was constantly refreshing pages waiting for appointments to become available. UPDATE: I took down this account, it required quite a bit of maintenance to make sure that the html was being read and updated correctly. It was gaining followers pretty quickly and I don’t have the time to make sure that it’s running reliably enough for people to depend on it for something as import as vaccinations. I had fun with the project though and plan to make a good_vibes_bot. I’ll make a stream that scans for certain negative keywords or sentiments and the bot will tweet positivity in response. The world needs this bot.

Graduate Coursework

Applied Machine Learning I526 with Dr. James Shanahan – Logistic Regression and regularization. Decision trees and pruning, implementation of decision trees. Support vector machines and making them work in practice. Boosting – implementing different boosting methods with decision trees. Using the algorithms for several tasks – how to set up the problem, debug, select features and develop the learning algorithm. Unsupervised learning – k-means, PCA, hierarchical clustering. Implementing the clustering algorithms. Parallelizing the learning algorithms. 

Network Science I606 with Dr. Santo Fortunato – Models and algorithms used in network science. Programming for the analysis of networks of various types and for simulating the dynamics of processes running on them, like epidemic spreading and opinion dynamic

Statistics S520 with Dr. Jianyu Wang – Discrete and continuous random variables, estimation, hypothesis testing, 1- and 2-sample location problems, ANOVA, and linear regression

MOOCs and Certificates

Neural Networks and Deep Learning – deep learning.ai (Coursera)
How Google Does Machine Learning – Google (Coursera)
Python 3 – University of Michigan (Coursera)
Mathematics for Machine Learning – Imperial College of London (Coursera)

Books I’m currently reading

  • The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors by Matthew O. Jackson

What else is on my mind?

  • Fragility of the power grid
  • Ranked-choice voting
  • CEFR Goethe-Zertifikat B1