Founder & CEO at Matroid
Adjunct Professor at Stanford
At Stanford I am within ICME, conducting research and teaching courses targeting doctorate students.
I focus on
Machine Learning, Distributed Computing, and Discrete Applied Mathematics.
I have served on the Technical Advisory Board of Microsoft and Databricks.
Created CME 323: Distributed Algorithms and Optimization   
Teaching CME 305: Discrete Mathematics and Algorithms    
Won Best Paper Award runner-up at KDD 2016
Built the Matroid Scaled Machine Learning Conference
News in Bloomberg TV, Bloomberg, GigaOm, MIT Technology Review, TechCrunch, Mashable, and many others
Interview: On the Evolution of Machine Learning, from Linear Models to Neural Networks [oreilly report]
Book: TensorFlow for Deep Learning
Profile: The Wall Street Journal | See more press.
FusionNet: 3D Object Classification Using Multiple Data Representations [pdf] [blog], NIPS 2016
Matrix Computations and Optimization in Apache Spark [pdf], KDD 2016
MLlib: Machine Learning in Apache Spark [arxiv], JMLR 2015
Dimension Independent Similarity Computation
[press], JMLR 2014
Estimate of Shaking Intensity by Combining Earthquake Characteristics with Tweets
[full], NCEE 2014
On the Precision of Social and Information Networks
[pdf] [slides], COSN 2013
WTF: The Who to Follow Service at Twitter
[pdf], WWW 2013
A Uniqueness Theorem for Clustering
[slides], UAI 2009