We perform fundamental research in algorithms, markets, optimization, and graph analysis, and use it to deliver solutions to challenges across Google’s business.
About the team
Our team comprises multiple overlapping research groups working on graph mining, large-scale optimization, and market algorithms. We collaborate closely with teams across Google, benefiting Ads, Search, YouTube, Play, Infrastructure, Geo, Social, Image Search, Cloud and more. Along with these collaborations, we perform research related to algorithmic foundations of machine learning, distributed optimization, economics, data mining, and data-driven optimization. Our researchers are involved in both long-term research efforts as well as immediate applications of our technology.
Examples of recent research interests include online ad allocation problems, distributed algorithms for large-scale graph mining, mechanism design for advertising exchanges, and robust and dynamic pricing for ad auctions.
Team focus summaries
Our mission is to build the most scalable library for graph algorithms and analysis and apply it to a multitude of Google products. We formalize data mining and machine learning challenges as graph problems and perform fundamental research in those fields leading to publications in top venues. Our algorithms and systems are used in a wide array of Google products such as Search, YouTube, AdWords, Play, Maps, and Social.
Our mission is to develop large-scale, distributed, and data-driven optimization techniques and use them to improve the efficiency and robustness of infrastructure and machine learning systems at Google. We achieve such goals as increasing throughput and decreasing latency in distributed systems, or improving feature selection and parameter tuning in machine learning. To do this, we apply techniques from areas such as combinatorial optimization, online algorithms, and control theory. Our research is used in critical infrastructure that supports products such as Search and Cloud.
Our mission is to discover all the world’s places and to understand people’s interactions with those places. We accomplish this by using ML to develop deep understanding of user trajectories and actions in the physical world, and we apply that understanding to solve the recurrent hard problems in geolocation data analysis. This research has enabled many of the novel features that appear in Google geo applications such as Maps.
Structured information extraction
Our mission is to extract salient information from templated documents and web pages and then use that information to assist users. We focus our efforts on extracting data such as flight information from email, event data form the web, and product information from the web. This enables many features in products such as Google Now, Search, and Shopping.
Search and information retrieval
Our mission is to conduct research to enable new or more effective search capabilities. This includes developing deeper understanding of correlations between documents and queries; modeling user attention and product satisfaction; developing Q&A models, particularly for the “next billion Internet users”; and, developing effective personal search models even when Google engineers cannot inspect private user input data.
Medical knowledge and learning
Our mission is offer a premier source of high-quality medical information along your entire online health journey. We provide relevant, targeted medical information to users by applying advanced ML on Google Search data. Examples of technologies created by this team include Symptom Search, Allergy Prediction, and other epidemiological applications.
We hosted a workshop that sparked new ideas for academics and Googlers in the area of algorithms and optimization, while also giving our academic participants an opportunity to see what Google has been working on.
Running a large-scale web service, such as content hosting, necessarily requires load balancing and we believe we have a found a way to mitigate the possibility of doing so with sub-optimal load balancing on many servers.
This post presents the distributed algorithm we developed which is more applicable to large instances.
The inspiration for this paper comes from studying social networks and the importance of addressing privacy issues in analyzing such networks.
We held this Workshop and invited several leading academics in these fields to meet with researchers and engineers at Google to discuss current algorithmic and game theoretic challenges in design.