Research happens across all of Google, and affects everything we do.
Research at Google is unique. Because so much of what we do hasn't been done before, the lines between research and development are often very blurred. This hybrid approach allows our discoveries to affect the world, both through improving Google products and services, and through the broader advancement of scientific knowledge.Google's Hybrid Approach to Research
Scalable K-Means by ranked retrieval
Proceedings of the 7th ACM international conference on Web search and data mining, ACM, New York, NY, USA (2014), pp. 233-242
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova
Proceedings of the 21st ACM Conference on Computer and Communications Security, ACM, Scottsdale, Arizona (2014) (to appear)
Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing
Ashish Gupta, Fan Yang, Jason Govig, Adam Kirsch, Kelvin Chan, Kevin Lai, Shuo Wu, Sandeep Dhoot, Abhilash Kumar, Ankur Agiwal, Sanjay Bhansali, Mingsheng Hong, Jamie Cameron, Masood Siddiqi, David Jones, Jeff Shute, Andrey Gubarev, Shivakumar Venkataraman, Divyakant Agrawal
Latest from the blog
Supporting MOOC Research at Carnegie Mellon University
In a multi-year program made possible through a Google Focused Research Award, Carnegie Mellon University researchers will tap data-driven approaches to improve learning as part of a new effort to unlock the educational potential of massive online open courses (MOOCs). The Google-sponsored research plan includes the development of techniques for automatically analyzing and providing feedback on student work, for creating social ties between learners and for designing MOOCs that are effective for students with a variety of cultural backgrounds. We're excited to support CMU's efforts to make online courses much more engaging, benefitting both students and educators around the world.
- Slav Petrov
- Natural Language
- New York, NY
Working on problems that are at the intersection of natural language processing and machine learning, Staff Research Scientist Slav Petrov actively investigates the application of syntactic analysis to the domains of machine translation and question answering. Most recently, Dr. Petrov has worked on the addition of wildcard and morphology queries to the Google Books Ngram Viewer, while also teaching an ongoing statistical natural language processing course at NYU. A native of Sofia, Bulgaria, Dr. Petrov obtained a MS in Computer Science from the Free Universtiy of Berlin and his PhD at UC Berkeley before joining Google Research in the New York office in 2009.
September 1 - 5
From September 1st - 5th, Hangzhou, China will host the 40th International Conference on Very Large Databases (VLDB 2014). As a premier forum for data management and database researchers, VLDB will also draw vendors, practitioners, application developers, and users. As a Gold Sponsor, Google will be on hand to take part in the conversation surrounding the latest issues in the main technological cornerstones of emerging applications, including data management, database, and information systems research. If you plan to attend VLDB, come visit the Google booth to learn more about the latest research results in these fields from the Googlers in attendance.