I'm so thankful for such an awesome job!

Senior Member of Technical Staff, Oracle Cloud @ From December 2015 to Present (1 month) Greater Seattle AreaSenior Software Engineer, Google Cloud @ I'm working on Google Managed VMs, which is a differentiating feature of Google Cloud. It combines the best features of both Infrastructure as a Service (Iaas) and Platform as a Service (Paas). My work involves building the underlying infrastructure including docker runtime, control plane (including deployment), and two most important features: health checking (including AutoRecovery) and AutoScaling, see: https://developers.google.com/appengine/docs/managed-vms/config From April 2013 to December 2015 (2 years 9 months) Greater Seattle AreaSoftware Development Engineer, Kindle and AWS @ Demand Forecasting If a new product is released, it does not have history of demand and thus is very hard to forecast. I tackled the regression problem of forecasting, i.e. given ASIN attributes, forecast demand in the first 20 weeks of its life. I used random forest to solve this problem. The challenges are how to make it scale large data set, how to do feature selection using random forest, and how to optimize cost function using random forest. I’m the winner of the machine learning hackend 2012 for this track. Kindle eBook Content Quality I first built a platform to do quality check for all in-coming eBooks, and large scale backfilling and quality monitoring. This system has processed millions of eBooks. Secondly, I improved the language classifier by using wikipedia data. By mentoring my intern, we not only improved the language coverage, but also improved the recognition accuracy. Thirdly, I built a system to estimated the number of pages in Kindle eBooks. The system involves several Amazon teams. I designed and iterated several versions of the algorithms to estimate the number of pages, and implemented the system to integrate with other teams. The estimated number of pages has become a critical feature for one the retail website of Kindle eBooks. I also mentored an intern to do photo and line-art classification with high accuracy. Fourthly, I designed and built a service to do typo detection. Kindle eBook Bulk Ingestion I worked on converting physical books to Kindle eBooks. Most of my work is related to image processing and OCR components of the Kindle eBook bulk ingestion pipeline, for example, image segmentation, image deskew, image despeckle, image enhancement, image page alignment, OCR with Abbyy, Nuance, and Tesseract engines. Silk Browser in AWS I also work in Amazon silk browser team in AWS to make browsing faster. It involved the AWS cloud computing and the Android device development. From September 2010 to April 2013 (2 years 8 months) Greater Seattle AreaSoftware Engineer, Video Content Analysis @ YouTube Video Classication Google Landmark Recognition http://googleblog.blogspot.com/2009/06/new-landmark-in-computer-vision.html Google Celebrity Recognition: http://www.informationweek.com/news/internet/google/showArticle.jhtml?articleID=217600713 From February 2007 to September 2010 (3 years 8 months)

postdoc, computer science @ National University of Singapore From 2004 to 2007 phd, computer vision & multimedia retrieval @ Zhejiang University From 1999 to 2004 Ming Zhao is skilled in: Machine Learning, Computer Vision, Distributed Systems, Algorithms, Image Processing, Android, Python, Cloud Computing, Amazon Web Services (AWS), Software Engineering, Java, C++, C