Papers

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, Network Science, and Complex Systems.

I’m a Research Fellow at IU and working on a project at the Kelley School of Business. My role is modeling and analyzing social contagion in corporate/professional environments.

I also work full-time at a textile manufacturer where I program 3-D knitting machines. I’m currently working on rebuilding my Java image processing application in Python. It takes a bitmap as input. The output is a folder of files written in machine code that the knitting machines can understand. It’s much faster and more comprehensive than the Java version. Depending on the bitmap, the process is now faster by more than 90%. Since I am in charge of creating these machine code tech packs, I’ve just about programmed myself out of a job. In addition to this, my role includes using technology to build systems for general process improvement (internal and customer facing)

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

Data Visualization DS590 with Dr. YY Ahn – Understand, explain, and manipulate different types of data, analyze them by applying exploratory visualization techniques, and create explanatory web-based visualizations. Evaluate the effectiveness of data visualizations based on the principles of human perception, design, types of data, and visualization techniques.

Natural Language Processing DS590 with Dr. Olga Scrivner – Domain-specific NLP techniques for data analysis featuring Healthcare, Banking, Marketing, Customer Service, and Technology domains.

Social Media Mining I639 with Dr. Ali Ghazinejad – Hands-on experience in mining social data for social meaning extraction (with a focus on sentiment analysis, due to the special importance of this task in various real world applications such as those related to market intelligence) using automated methods (e.g., natural language processing [NLP] and machine learning technologies). Read, discuss, and critique claims and findings from contemporary research related to SMM. Address practical issues related to building tools to mine social media.  

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)
• Anatomy: The Life of a Cell – HACC (iTunes U)
• Introduction to Complexity – Santa Fe Institute
• Mathematics for Machine Learning – Imperial College of London (Coursera)

What I’m Reading Right Now

• Flow My Tears, the Policeman Said by Philip K. Dick
• A History of Japan by RHP Mason

Follow my 2021 Reading Challenge! I’m ahead of schedule…it looks like I’ll reach my goal of 45 books!

Podcasts I Like Right Now

• The Jim Rutt Show
• Macro Voices
• Useful Idiots
• Complexity
• The TWIML AI Podcast