ESP Biography
ANTON ANDRYEYEV, Engineer on Machine Translation project at Google
Major: Computer Science, BA Mathematics College/Employer: University of Waterloo / Google Inc. Year of Graduation: 2006 

Brief Biographical Sketch:
I have graduated from the University of Waterloo with a degree in Mathematics (major in Computer Science). Throughout my undergrad career I have always been drawn to studies of Artificial Intelligence and probabilistic/statistical approaches to machine learning (i.e. a computer learning tasks usually done by humans). I joined Google in 2006 and have worked on their Machine Translation project. This project has created the technology behind http://translate.google.com and allowed Google to offer automatic translation between 40+ different languages. Machine Translation is a fascinating area of Artificial Intelligence. However, it's but a single example of how probability and statistics can be used to solve very complicated problems in practice. There's a vast number of tasks out there that we can teach computers to do well. The question is "how?" Past Classes(Clicking a class title will bring you to the course's section of the corresponding course catalog)C327: Solving Problems with Probabilistic/Statistical Algorithms in Splash! Spring 2009 (Apr. 04  05, 2009)
Ever wondered how Amazon recommends you books, or how Netflix suggests movies, or how Google translates between 40+ different languages without any human intervention?
Behind all these systems are algorithms based on statistics and probability. In this class you will learn the fundamentals of probability theory and, most importantly, how it applies to modeling uncertainty in realworld problems.
After learning the basics, you will get a very close look at how simple probability concepts can be applied to solving a very complex task of Machine Translation (i.e. translating text from one language to another).
Briefly about your teachers: Ignacio and Anton have worked on Googleâ€™s Machine Translation project for almost 3 years. This technology is made public via http://translate.google.com and currently enables millions of users to read foreign content in their native language.
C156: Solving Problems with Probabilistic/Statistical Algorithms in Splash! Fall 2008 (Oct. 18, 2008)
Ever wondered how Amazon recommends you books, or how Netflix suggests movies, or how Google translates between 35 different languages without any human intervention?
Behind all these systems are algorithms based on statistics and probability. In this class you will learn the fundamentals of probability theory and, most importantly, how it applies to modeling uncertainty in realworld problems.
After learning the basics, you will get a very close look at how simple probability concepts can be applied to solving a very complex task of Machine Translation (i.e. translating text from one language to another).
Briefly about your teachers: Ignacio and Anton have worked on Google's Machine Translation project for almost 3 years. This technology is made public via http://translate.google.com and currently enables millions of users to read foreign content in their native language.
