alicez at dato dot com
I currently work at Dato (formerly known as GraphLab). At Dato, we are developing a fast and scalable machine learning platform for Big Data analytics. Prior to Dato/GraphLab, I was a researcher in the Machine Learning Group at Microsoft Research, Redmond. Before joining Microsoft, I was a postdoc at Carnegie Mellon University's Auton Lab and the Parallel Data Lab. I received my B.A. and Ph.D. degrees from U. C. Berkeley.
I am interested in making machine learning easy to use. Up to now, applying machine learning to data analysis has required close collaborations between domain experts who understand the data and machine learning experts who understand the algorithms. The problem is that it is easy to scale up the size of the data, but much harder to scale up the number of experts. My research focuses on easing the dependence on expertise by making learning algorithms more automated, their outputs more interpretable, and the labeling tasks simpler. In the past, I have worked on using machine learning to diagnose ailing computer systems and software. Some of the lessons learned from that domain continues to drive my research today.
I had a feeling once about mathematics -- that I saw it all. Depth beyond depth was revealed to me -- the Byss and the Abyss. I saw -- as one might see the transit of Venus or even the Lord Mayor's Show -- a quantity passing through infinity and changing its sign from plus to minus. I saw exactly why it happened and why tergiversation was inevitable -- but it was after dinner and I let it go. -- Winston Churchill