Daniel Ting

About

My interests lie in developing novel statistical methods. In particular, I am interested in data sketching and sampling which lies in the intersection of statistics and databases. These are methods to summarize big data into memory efficient summarizations that can still answer a broad set of questions. I also have strong interests in the analysis and design of experiments and machine learning. My visualization oriented ML research is in manifold learning and non-linear dimensionality reduction where I study the mathematical limit operators implied by existing methods and how to design new operators and, hence, new methods.

I received my PhD in Statistics at UC Berkeley under the supervision of Michael Jordan. My PhD work focused on non-parametric Bayesian cluster models and semi-supervised/manifold learning. I also worked on data privacy for my MSc at Carnegie Mellon under Stephen Fienberg. Before Tableau, I was a core data scientist at Facebook primarily working on experimentation.