Blog

The latest news from Google AI

Google University Research Awards

Wednesday, January 28, 2009

Posted by Juan Vargas, University RelationsFor most people, the word "Google" evokes associations of Internet search, free apps, and advertising--a far cry from our roots in research and academia. Google, however, has not lost sight of that fundamental relationship. Many of our offices (or "campuses") are located near local universities, and we maintain a working environment many consider to be collegiate: informal, collaborative, and home to expert lectures not only about science and technology, but also about literature, the economy, world peace, green energy, fitness, etc.
But these aspects are only one component of our enduring partnership with academia. Given the unique technical challenges we face, from the beginning we've often recognized the need for a strong relationship with the research community. One of the key ways we interact with this community is through the Google Research Awards Program.
Started in 2005, the program seeks to identify and support leading-edge research in strategic areas of engineering and computer science. Professors from universities worldwide submit proposals three times per year that are evaluated by a team of Google engineers and scientists.
Like Google itself, the program is global in scope: in the most recent round of submissions, nearly a third came from outside of the United States. During that round, we received 149 proposals and we ultimately decided to fund about a third of the projects.
A challenging aspect of running an awards program is making the funding decisions. We want to ensure that every proposal is reviewed by the best experts in the field. Luckily, we're fortunate to have many people at Google with the right expertise who help us make those decisions.
As Alfred Spector, VP for Research, explains, "The winning proposals not only get financial support from Google, but also receive the extra benefit of being assigned a Google liaison who maintains a special relation with the professor during the life of the award, contributes to the research, and ensures that the outcomes are valuable to Google and academia." One example of this is the recent paper featured on the Google Research home page, by Phil Long and Rocco Servedio from Columbia University titled "Random Classification Noise Defeats All Convex Potential Boosters."
The following are some highlights from other recently-funded projects:
Social Networks Research Through CourseRankHector Garcia-Molina, Stanford UniversityHector Garcia-Molina and his team at Stanford are pursuing research on social networks and web usability by using and expanding CourseRank, a course evaluation and recommendation system for the Stanford community. As of June 2008, CourseRank has over 6,700 users and over 134,000 course evaluations. In addition to providing a service for Stanford students, it provides useful data to learn about social networks. Dr. Garcia-Molina plans to use CourseRank to investigate problems such as spam, trust, recommendations with complex objects, and the nature of social interactions. You can see a video with student testimonials and a demo here.

Energy-Efficient Storage Architectures for Data CentersSudhanva Gurumurthi, Mircea Stan, University of VirginiaDrs. Gurumurthi and Stan from the University of Virgina have developed a new disk drive architecture called "Intra-Disk Parallelism" that they hope will reduce the energy consumption of data center storage systems by over 60% while providing high performance to applications. This architecture extends conventional disk drives by allowing it to handle multiple I/O requests in parallel and by provisioning additional hardware resources to enhance the parallel capabilities even further. Details of their research were published at the 2008 International Symposium on Computer Architecture (ISCA ). The conference paper has also been selected to appear in the IEEE Micro special issue on Top Picks from Computer Architecture Conferences.

Finding Better Spoken Dialog System MetricsMaxine Eskenazi, Carnegie Mellon UniversityWhen the number of calls to spoken dialog systems mounts into the thousands, or millions, it is impossible to listen to every call. Prof. Eskenazi and her team at CMU are studying new metrics that could improve the performance of those systems. Dr. Eskenazi hopes this research will help illuminate how best to cope with advances in tuned speech recognition and new synthetic voices.

Interested in learning more about the Google University Relations and our Research Awards Program? Please visit our web site where scholars can learn more and submit proposals.

Labels

accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube

Feed

Give us feedback in our Product Forums.

Google
Privacy
Terms

Blog

Google University Research Awards

Labels

Archive

Feed