Mike Procopio
Senior Software Engineer and Machine Learning Researcher
Background
I'm a Machine Learning researcher and Senior Software Engineer at Google in Boulder, Colorado. I currently work on Google Drive on teams that develop features such as file versions, drag and drop upload, the Google Drive SDK (including Version 2 of the SDK), and Apps in Google Drive. Interested in working for Google? Have a look at current job openings!
I received my Ph.D. in Machine Learning from the Computer Science Department at the University of Colorado at Boulder in 2007. (Here's an article that summarizes my doctoral research.) My research interests include machine learning classification and ensemble methods, computer vision, robotics, and search. I also like to dabble in this and that, e.g., Braitenberg Vehicles.
My resume (PDF).
Information and downloads for the Hand-Labeled DARPA LAGR Datasets.
I can be contacted through Google+, my LinkedIn profile, or at mprocopio@gmail.com. To be extra hip, you can also email me at mike@procop.io.
New! My new book, Instant Google Drive Starter, was recently published and is now available on Amazon.com.
Selected Publications (view in Google Scholar)
False Event Screening Using Data Mining in Historical Archives
Estimation of Arrival Times from Seismic Waves: a Manifold-Based Approach
Coping with Imbalanced Training Data for Improved Terrain Prediction in Autonomous Outdoor Robot Navigation
SAPLE: Sandia Advanced Personnel Locator Engine
not available
Image Data Processing for Integrated Circuit Analysis
Generalized BadRank with Graduated Trust
Terrain Segmentation with On-line Mixtures of Experts for Autonomous Robot Navigation
Using machine learning to improve the efficiency and effectiveness of automatic nuclear explosion monitoring systems
Exploring the limits of waveform correlation event detection as applied to the 1994 Northridge earthquake aftershock sequence
Learning Terrain Segmentation with Classifier Ensembles for Autonomous Robot Navigation in Unstructured Environments
Learning in Dynamic Environments with Ensemble Selection for Autonomous Outdoor Robot Navigation
An Experimental Analysis of Classifier Ensembles for Learning Drifting Concepts Over Time in Autonomous Outdoor Robot Navigation
Long-Term Learning Using Multiple Models for Outdoor Autonomous Robot Navigation
Using Binary Classifiers to Augment Stereo Vision for Enhanced Autonomous Robot Navigation
Benefits of Synchronous Collaboration Support for an Application-Centered Analysis Team Working on Complex Problems: a Case Study
