alicez at gmail dot com
I specialize in research and development of machine learning methods, tools, and applications. Currently, I lead the optimization team on Amazon's Ad Platform. Outside of work, I'm writing a book on feature engineering. Previously, I worked at Dato (formerly known as GraphLab), where I led the Toolkits team and helped with marketing and user outreach efforts. I had also worked as a researcher in the Machine Learning Group at Microsoft Research, Redmond, and as a postdoc at Carnegie Mellon University's Auton Lab and the Parallel Data Lab. I received B.A.s in Mathematics and Computer Science and a Ph.D. in Electrical Engineering from U. C. Berkeley in Prof. Michael Jordan's lab.
When not working, I meditate daily and practice yoga occasionally. I'm a co-organizer of the Seattle Data/Analytics/Machine Learning MeetUp.
I am interested in making machine learning easy to use. Machine learning applications always require close collaborations between domain experts who understand the data and machine learning experts who understand the algorithms. The problem with this setup 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