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
The Performance Cost of Shadow Stacks and Stack Canaries
Thurston H.Y. Dang, Petros Maniatis, David Wagner
Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security (ASIACCS), ACM (2015), pp. 555-566
Swapsies on the Internet: First Steps towards Reasoning about Risk and Trust in an Open World
Sophia Drossopoulou, James Noble, Mark S. Miller
Tenth Workshop on Programming Languages and Analysis for Security (PLAS 2015), ACM
Improving User Topic Interest Profiles by Behavior Factorization
Zhe Zhao, Zhiyuan Cheng, Lichan Hong, Ed H. Chi
Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2015), pp. 1406-1416
Latest from the blog
Google and UCSB partner on Quantum Computing Hardware Initiative
John Martinis and his team at UC Santa Barbara has joined the Quantum Artifical Intelligence team at Google in a hardware initiative to design and build new quantum information processors based on superconducting electronics. With an integrated hardware group, the Quantum AI team will now be able to implement and test new designs for quantum optimization and inference processors based on recent theoretical insights as well as our learnings from the D-Wave quantum annealing architecture. Google will continue to collaborate with D-Wave scientists and to experiment with the "Vesuvius" machine at NASA Ames which will be upgraded to a 1000 qubit "Washington" processor.
- Tushar Chandra
- Distributed Systems and
- Machine Learning
- Mountain View, CA
Tushar Chandra is a Principal Engineer at Google Research and a co-lead for the Sibyl project, a large-scale Machine Learning platform widely used within Google. Tushar received his Ph.D. in Computer Science from Cornell University in 1993 and joined IBM Research thereafter, where he worked on distributed systems projects such as Reliable Scalable Cluster Technology, Gryphon, and Oceano. Tushar joined Google in 2004, where he has worked on Bigtable, a distributed system for managing structured data, as well as a platform for fault tolerance. He was a joint winner of the 2010 ACM-EATCS Edsger W. Dijkstra Prize in Distributed Computing.
June 7 - 12
In June, Boston hosts the 2015 Conference on Computer Vision and Pattern Recognition (CVPR 2015), the premier annual computer vision event comprising the main CVPR conference and several co-located workshops and short courses. As a Platinum Sponsor and leader in computer vision research, Google will have a strong presence at CVPR 2015, with many Googlers presenting publications, and hosting tutorials and workshops. Stop by the Google booth and chat with our researchers about projects involving image/video annotation and enhancement, 3D analysis and processing, development of semantic similarity measures for visual objects, and more!