Tag Archives: Research Awards

Announcing the 2019 Google Faculty Research Award Recipients



In Fall 2019, we opened our annual call for the Google Faculty Research Awards, a program focused on supporting the world-class technical research in Computer Science, Engineering and related fields performed at academic institutions around the world. These awards give Google researchers the opportunity to partner with faculty who are doing impactful research, additionally covering tuition for a student.

This year we received 917 proposals from ~50 countries and over 330 universities, and had the opportunity to increase our investment in several research areas related to Health, Accessibility, AI for Social Good, and ML Fairness. All proposals went through an extensive review process involving 1100 expert reviewers across Google who assessed the proposals on merit, innovation, connection to Google’s products/services and alignment with our overall research philosophy.

As a result of these reviews, Google is funding 150 promising proposals across a wide range of research areas, from Machine Learning, Systems, Human Computer Interaction and many more, with 26% of the funding awarded to universities outside the United States. Additionally, 27% of our recipients this year identified as a historically underrepresented group within technology. This is just the beginning of a larger investment in underrepresented communities and we are looking forward to sharing our 2020 initiatives soon.

Congratulations to the well-deserving recipients of this round's awards. More information on our faculty funding programs can be found on our website.

Source: Google AI Blog


Google Faculty Research Awards 2018



We just completed another round of the Google Faculty Research Awards, our annual open call for proposals on computer science and related topics, such as quantum computing, machine learning, algorithms and theory, natural language processing and more. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 910 proposals covering 40 countries and over 320 universities. After expert reviews and committee discussions, we decided to fund 158 projects. The subject areas that received the most support this year were human computer interaction, machine learning, machine perception, and systems.

Congratulations to the well-deserving recipients of this round's awards. More information on how to apply for the next round will be available at the end of the summer on our website. You can find award recipients from previous years here.

Source: Google AI Blog


Announcing the Google Cloud Platform Research Credits Program



Scientists across nearly every discipline are researching ever larger and more complex data sets, using tremendous amounts of compute power to learn, make discoveries and build new tools that few could have imagined only a few years ago. Traditionally, this kind of research has been limited by the availability of resources, with only the largest universities or industry partners able to successfully pursue these endeavors. However, the power of cloud computing has been removing obstacles that many researchers used to face, enabling projects that use machine learning tools to understand and address student questions and that study robotic interactions with humans, among many more.

In order to ensure that more researchers have access to powerful cloud tools, we’re launching Google Cloud Platform (GCP) research credits, a new program aimed to support faculty in qualified regions who want to take advantage of GCP’s compute, analytics, and machine-learning capabilities for research. Higher education researchers can use GCP research credits in a multitude of ways — below are just three examples to illustrate how GCP can help propel your research forward.

Andrew V. Sutherland, a computational number theorist and Principal Research Scientist at the Massachusetts Institute of Technology, is one of a growing number of academic researchers who has already made the transition and benefited from GCP. His team moved his extremely large database to GCP because “we are mathematicians who want to focus on our research, and not have to worry about hardware failures or scaling issues with the website.”

Ryan Abernathey, Assistant Professor of Earth and Environmental Sciences, Ocean and Climate Physics at the Lamont-Doherty Earth Observatory at Columbia University, used Google Cloud credits through an NSF partnership and, with his team, developed an open-source platform to manage the complex data sets of climate science. The platform, called Pangeo, can run Earth System Modeling simulations on petabytes of high-resolution, three-dimensional data. “This is the future of what day-to-day science research computing will look like,” he predicts.

At the Stanford Center for Genomics and Personalized Medicine (SCGPM), researchers using GCP and BigQuery can now run hundreds of genomes through a variant analysis pipeline and get query results quickly. Mike Snyder, director of SCGPM, notes, “We’re entering an era where people are working with thousands or tens of thousands or even million genome projects, and you’re never going to do that on a local cluster very easily. Cloud computing is where the field is going.”

The GCP research credits program is open to faculty doing cutting-edge research in eligible countries. We’re eager to hear how we can help accelerate your progress. If you’re interested, you can learn more on our website or apply now.

Announcing the Google Cloud Platform Research Credits Program



Scientists across nearly every discipline are researching ever larger and more complex data sets, using tremendous amounts of compute power to learn, make discoveries and build new tools that few could have imagined only a few years ago. Traditionally, this kind of research has been limited by the availability of resources, with only the largest universities or industry partners able to successfully pursue these endeavors. However, the power of cloud computing has been removing obstacles that many researchers used to face, enabling projects that use machine learning tools to understand and address student questions and that study robotic interactions with humans, among many more.

In order to ensure that more researchers have access to powerful cloud tools, we’re launching Google Cloud Platform (GCP) research credits, a new program aimed to support faculty in qualified regions who want to take advantage of GCP’s compute, analytics, and machine-learning capabilities for research. Higher education researchers can use GCP research credits in a multitude of ways — below are just three examples to illustrate how GCP can help propel your research forward.

Andrew V. Sutherland, a computational number theorist and Principal Research Scientist at the Massachusetts Institute of Technology, is one of a growing number of academic researchers who has already made the transition and benefited from GCP. His team moved his extremely large database to GCP because “we are mathematicians who want to focus on our research, and not have to worry about hardware failures or scaling issues with the website.”

Ryan Abernathey, Assistant Professor of Earth and Environmental Sciences, Ocean and Climate Physics at the Lamont-Doherty Earth Observatory at Columbia University, used Google Cloud credits through an NSF partnership and, with his team, developed an open-source platform to manage the complex data sets of climate science. The platform, called Pangeo, can run Earth System Modeling simulations on petabytes of high-resolution, three-dimensional data. “This is the future of what day-to-day science research computing will look like,” he predicts.

At the Stanford Center for Genomics and Personalized Medicine (SCGPM), researchers using GCP and BigQuery can now run hundreds of genomes through a variant analysis pipeline and get query results quickly. Mike Snyder, director of SCGPM, notes, “We’re entering an era where people are working with thousands or tens of thousands or even million genome projects, and you’re never going to do that on a local cluster very easily. Cloud computing is where the field is going.”

The GCP research credits program is open to faculty doing cutting-edge research in eligible countries. We’re eager to hear how we can help accelerate your progress. If you’re interested, you can learn more on our website or apply now.

Source: Google AI Blog


Google Faculty Research Awards 2017



We’ve just completed another round of the Google Faculty Research Awards, our annual open call for proposals on computer science and related topics such as machine learning, machine perception, natural language processing, and quantum computing. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 1033 proposals covering 46 countries and over 360 universities. After expert reviews and committee discussions, we decided to fund 152 projects. The subject areas that received the most support this year were human computer interaction, machine learning, machine perception, and systems. Here are a few observations from this round:
  • There was a 17% increase in the total number of proposals received
  • There was a 87% increase in the number of proposals from Asia Pacific universities
  • Proposals focused on Computational Neuroscience increased 53%
  • Proposals focused on Quantum Computing more than doubled this round
Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (September 2018 deadline), please visit our website for more information. You can find award recipients from previous years here.

Google Faculty Research Awards 2017



We’ve just completed another round of the Google Faculty Research Awards, our annual open call for proposals on computer science and related topics such as machine learning, machine perception, natural language processing, and quantum computing. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 1033 proposals covering 46 countries and over 360 universities. After expert reviews and committee discussions, we decided to fund 152 projects. The subject areas that received the most support this year were human computer interaction, machine learning, machine perception, and systems. Here are a few observations from this round:
  • There was a 17% increase in the total number of proposals received
  • There was a 87% increase in the number of proposals from Asia Pacific universities
  • Proposals focused on Computational Neuroscience increased 53%
  • Proposals focused on Quantum Computing more than doubled this round
Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (September 2018 deadline), please visit our website for more information. You can find award recipients from previous years here.

Source: Google AI Blog


Google Research Awards 2016



We’ve just completed another round of the Google Research Awards, our annual open call for proposals on computer science and related topics including machine learning, machine perception, natural language processing, and security. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 876 proposals covering 44 countries and over 300 universities. After expert reviews and committee discussions, we decided to fund 143 projects. Here are a few observations from this round:


Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is September 30th), please visit our website for more information.

An Update on fast Transit Routing with Transfer Patterns



What is the best way to get from A to B by public transit? Google Maps is answering such queries for over 20,000 cities and towns in over 70 countries around the world, including large metro areas like New York, São Paulo or Moscow, and some complete countries, such as Japan or Great Britain.
Since its beginnings in 2005 with the single city of Portland, Oregon, the number of cities and countries served by Google’s public transit directions has been growing rapidly. With more and larger regions, the amount of data we need to search in order to provide optimal directions has grown as well. In 2010, the search speed of transit directions made a leap ahead of that growth and became fast enough to update the result while you drag the endpoints. The technique behind that speed-up is the Transfer Patterns algorithm [1], which was created at Google’s engineering office in Zurich, Switzerland, by visiting researcher Hannah Bast and a number of Google engineers.

I am happy to report that this research collaboration has continued and expanded with the Google Focused Research Award on Next-Generation Route Planning. Over the past three years, this grant has supported Hannah Bast’s research group at the University of Freiburg, as well as the research groups of Peter Sanders and Dorothea Wagner at the Karlsruhe Institute of Technology (KIT).

From the project’s numerous outcomes, I’d like to highlight two recent ones that re-examine the Transfer Patterns approach and massively improve it for continent-sized networks: Scalable Transfer Patterns [2] and Frequency-Based Search for Public Transit [3] by Hannah Bast, Sabine Storandt and Matthias Hertel. This blogpost presents the results from these publications.

The notion of a transfer pattern is easy to understand. Suppose you are at a transit stop downtown, call it A, and want to go to some stop B as quickly as possible. Suppose further you brought a printed schedule book but no smartphone. (This sounded plausible only a few years ago!) As a local, you might know that there are only two reasonable options:
  1. Take a tram from A to C, then transfer at C to a bus to B.
  2. Take the direct bus from A to B, which only runs infrequently.
We say the first option has transfer pattern A-C-B, and the second option has transfer pattern A-B. Notice that no in-between stops are mentioned. This is very compact information, much less than the actual schedules, but it makes looking up the schedules significantly faster: Knowing that all optimal trips follow one of these patterns, you only need to look at those lines in the schedule book that provide direct connections from A to C, C to B and A to B. All other lines can safely be ignored: you know you will not miss a better option.

While the basic idea of transfer patterns is indeed that simple, it takes more to make it work in practice. The transfer patterns of all optimal trips have to be computed ahead of time and stored, so that they are available to answer queries. Conceptually, we need transfer patterns for every pair of stops, because any pair could come up in a query. It is perfectly reasonable to compute them for all pairs within one city, or even one metro area that is densely criss-crossed by a transit network comprising, say, a thousand stops, yielding a million of pairs to consider.

As the scale of the problem increases from one metro area to an entire country or continent, this “all pairs” approach rapidly becomes expensive: ten thousand stops (10x more than above) already yield a hundred million pairs (100x more than above), and so on. Also, the individual transfer patterns become quite repetitive: For example, from any stop in Paris, France to any stop in Cologne, Germany, all optimal connections end up using the same few long-distance train lines in the middle, only the local connections to the railway stations depend on the specific pair of stops considered.

However, designated long-distance connections are not the only way to travel between different local networks – they also overlap and connect to each other. For mid-range trips, there is no universally correct rule when to choose a long-distance train or intercity bus, short of actually comparing options with local or regional transit, too.

The Scalable Transfer Patterns algorithm [2] does just that, but in a smart way. For starters, it uses what is known as graph clustering to cut the network into pieces, called clusters, that have a lot of connections inside but relatively few to the outside. As an example, the figure below (kindly provided by the authors) shows a partitioning of Germany into clusters. The stops highlighted in red are border stops: They connect directly to stops outside the cluster. Notice how they are a small fraction of the total network.
The public transit network of Germany (dots and lines), split into clusters (shown in various colors). Of all 251,763 stops, only 10,886 (4.32%) are boundary stops, highlighted as red boxes. Click here to view the full resolution image.[source: S. Storandt, 2016]
Based on the clustering, the transfer patterns of all optimal connections are computed in two steps.

In step 1, transfer patterns are computed for optimal connections inside each cluster. They are stored for query processing later on, but they also accelerate the search through a cluster in the following step: between the stops on its border, we only need to consider the connections captured in the transfer patterns.

The next figure sketches how the transit network in the cluster around Berlin gets reduced to much fewer connections between border stations. (The central station stands out as a hub, as expected. It is a border station itself, because it has direct connections out of the cluster.)
The cluster of public transit connections around Berlin (shown as dots and lines in light blue), its border stops (highlighted as red boxes), and the transfer patterns of optimal connections between border stops (thick black lines; only the most important 111 of 592 are shown to keep the image legible). This cuts out 96.15% of the network (especially a lot of the high-frequency inner city trips, which makes the time savings even bigger). Click here to view the full resolution image. [source: S. Storandt, 2016]
In step 2, transfer patterns can be computed for the entire network, that is, between any pair of clusters. This is done with the following twists:

  • It suffices to consider trips from and to boundary stops of any cluster; the local transfer patterns from step 1 will supply the missing pieces later on.
  • The per-cluster transfer patterns from step 1 greatly accelerate the search across other clusters.
  • The search stops exploring any possible connection between two boundary stops as soon as it gets worse than a connection that sticks to long-distance transit between clusters (which may not always be optimal, but is always quick to compute).

The results of steps 1 and 2 are stored and used to answer queries. For any given query from some A to some B, one can now easily stitch together a network of transfer patterns that covers all optimal connections from A to B. Looking up the direct connections on that small network (like in the introductory example) and finding the best one for the queried time is very fast, even if A and B are far away.

The total storage space needed for this is much smaller than the space that would be needed for all pairs of stops, all the more the larger the network gets. Extrapolating from their experiments, the researchers estimate [2] that Scalable Transfer Patterns for the whole world could be stored in 30 GB, cutting their estimate for the basic Transfer Patterns by a thousand(!). This is considerably more powerful than the “hub station” idea from the original Transfer Patterns paper [1].

The time needed to compute Scalable Transfer Patterns is also estimated to shrink by three orders of magnitude: At a high level, the earlier phases of the algorithm accelerate the later ones, as described above. At a low level, a second optimization technique kicks in: exploiting the repetitiveness of schedules in time. Recall that finding transfer patterns is all about finding the optimal connections between pairs of stops at any possible departure time.

Frequency-based schedules (e.g., one bus every 10 minutes) cause a lot of similarity during the day, although it often doesn’t match up between lines (e.g., said bus runs every 10 minutes before 6pm and every 20 minutes after, and we seek connections to a train that runs every 12 minutes before 8pm and every 30 minutes after). Moreover, this similarity also exists from one day to the next, and we need to consider all permissible departure dates.

The Frequency-Based Search for Public Transit [3] is carefully designed to find and take advantage of repetitive schedules while representing all one-off cases exactly. Comparing to the set-up from the original Transfer Patterns paper [1], the authors estimate a whopping 60x acceleration of finding transfer patterns from this part alone.

I am excited to see that the scalability questions originally left open by [1] have been answered so convincingly as part of this Focused Research Award. Please see the list of publications on the project’s website for more outcomes of this award. Besides more on transfer patterns, they contain a wealth of other results about routing on road networks, transit networks, and with combinations of travel modes.

References:

[1] Fast Routing in Very Large Public Transportation Networks Using Transfer Patterns
by H. Bast, E. Carlsson, A. Eigenwillig, R. Geisberger, C. Harrelson, V. Raychev and F. Viger
(ESA 2010). [doi]

[2] Scalable Transfer Patterns
by H. Bast, M. Hertel and S. Storandt (ALENEX 2016). [doi]

[3] Frequency-based Search for Public Transit
by H. Bast and S. Storandt (SIGSPATIAL 2014). [doi]

Google Research Awards: Fall 2015



We have just completed another round of the Google Research Awards, our annual open call for proposals on computer science and related topics including machine learning, speech recognition, natural language processing, and computational neuroscience. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.

This round we received 950 proposals, an increase of 18% over last round, covering 55 countries and over 350 universities. After expert reviews and committee discussions, we decided to fund 151 projects. This round we increased our support of machine learning projects increased by 71% from last round. Physical interfaces and immersive experiences, a relatively new area for the Google Research Awards, saw a 19% increase in the number of submitted proposals.

Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is October 15), please visit our website for more information. Please note that we are now moving to an annual cycle.

Announcing the Google Internet of Things (IoT) Technology Research Award Pilot



Over the past year, Google engineers have experimented and developed a set of building blocks for the Internet of Things - an ecosystem of connected devices, services and “things” that promises direct and efficient support of one’s daily life. While there has been significant progress in this field, there remain significant challenges in terms of (1) interoperability and a standardized modular systems architecture, (2) privacy, security and user safety, as well as (3) how users interact with, manage and control an ensemble of devices in this connected environment.

It is in this context that we are happy to invite university researchers1 to participate in the Internet of Things (IoT) Technology Research Award Pilot. This pilot provides selected researchers in-kind gifts of Google IoT related technologies (listed below), with the goal of fostering collaboration with the academic community on small-scale (~4-8 week) experiments, discovering what they can do with our software and devices.

We invite you to submit proposals in which Google IoT technologies are used to (1) explore interesting use cases and innovative user interfaces, (2) address technical challenges as well as interoperability between devices and applications, or (3) experiment with new approaches to privacy, safety and security. Proposed projects should make use of one or a combination of these Google technologies:
  • Google beacon platform - consisting of the open beacon format Eddystone and various client and cloud APIs, this platform allows developers to mark up the world to make your apps and devices work smarter by providing timely, contextual information.
  • Physical Web - based on the Eddystone URL beacon format, the Physical Web is an approach designed to allow any smart device to interact with real world objects - a vending machine, a poster, a toy, a bus stop, a rental car - and not have to download an app first.
  • Nearby Messages API - a publish-subscribe API that lets you pass small binary payloads between internet-connected Android and iOS devices as well as with beacons registered with Google's proximity beacon service.
  • Brillo & Weave - Brillo is an Android-based embedded OS that brings the simplicity and speed of mobile software development to IoT hardware to make it cost-effective to build a secure smart device, and to keep it updated over time. Weave is an open communications and interoperability platform for IoT devices that allows for easy connections to networks, smartphones (both Android and iOS), mobile apps, cloud services, and other smart devices.
  • OnHub router - a communication hub for the Internet of Things supporting Bluetooth® Smart Ready, 802.15.4 and 802.11a/b/g/n/ac. It also allows you to quickly create a guest network and control the devices you want to share (see On.Here).
  • Google Cloud Platform IoT Solutions - tools to scale connections, gather and make sense of data, and provide the reliable customer experiences that IoT hardware devices require.
  • Chrome Boxes & Kiosk Apps - provides custom full screen apps for a purpose-built Chrome device, such as a guest registration desk, a library catalog station, or a point-of-sale system in a store.
  • Vanadium - an open-source framework designed to make it easier to develop, secure, multi-device user experiences, with or without an Internet connection.
Check out the Ubiquity Dev Summit playlist for more information on these platforms and their best practices.

Please submit your proposal here by February 29th in order to be considered for a award. Proposals will be reviewed by researchers and product teams within Google. In addition to looking for impact and interesting ideas, priority will be given to research that can make immediate use of the available technologies. Selected proposals will be notified by the end of March 2016. If selected, the award will be subject to Google’s terms, and your use of Google technologies will be subject to the applicable Google terms of service.

To connect our physical world to the Internet is a broad and long-term challenge, one we hope to address by working with researchers across many disciplines and work practices. We are looking forward to the collaborative opportunity provided by this pilot, and learning about innovative applications you create for these new technologies.



1 The same eligibility conditions as for the Faculty Research Award Program apply - see here.