Announcing Google’s 2015 Global PhD Fellows

Posted by Michael Rennaker, Google University Relations

In 2009, Google created the PhD Fellowship program to recognize and support outstanding graduate students doing exceptional research in Computer Science and related disciplines. Now in its seventh year, our fellowship programs have collectively supported over 200 graduate students in Australia, China and East Asia, India, North America, Europe and the Middle East who seek to shape and influence the future of technology.

Reflecting our continuing commitment to building mutually beneficial relationships with the academic community, we are excited to announce the 44 students from around the globe who are recipients of the award. We offer our sincere congratulations to Google’s 2015 Class of PhD Fellows!

Australia

• Bahar Salehi, Natural Language Processing (University of Melbourne)
• Siqi Liu, Computational Neuroscience (University of Sydney)
• Qian Ge, Systems (University of New South Wales)

China and East Asia

• Bo Xin, Artificial Intelligence (Peking University)
• Xingyu Zeng, Computer Vision (The Chinese University of Hong Kong)
• Suining He, Mobile Computing (The Hong Kong University of Science and Technology)
• Zhenzhe Zheng, Mobile Networking (Shanghai Jiao Tong University)
• Jinpeng Wang, Natural Language Processing (Peking University)
• Zijia Lin, Search and Information Retrieval (Tsinghua University)
• Shinae Woo, Networking and Distributed Systems (Korea Advanced Institute of Science and Technology)
• Jungdam Won, Robotics (Seoul National University)

India

• Palash Dey, Algorithms (Indian Institute of Science)
• Avisek Lahiri, Machine Perception (Indian Institute of Technology Kharagpur)
• Malavika Samak, Programming Languages and Software Engineering (Indian Institute of Science)

Europe and the Middle East

• Heike Adel, Natural Language Processing (University of Munich)
• Thang Bui, Speech Technology (University of Cambridge)
• Victoria Caparrós Cabezas, Distributed Systems (ETH Zurich)
• Nadav Cohen, Machine Learning (The Hebrew University of Jerusalem)
• Josip Djolonga, Probabilistic Inference (ETH Zurich)
• Jakob Julian Engel, Computer Vision (Technische Universität München)
• Nikola Gvozdiev, Computer Networking (University College London)
• Felix Hill, Language Understanding (University of Cambridge)
• Durk Kingma, Deep Learning (University of Amsterdam)
• Massimo Nicosia, Statistical Natural Language Processing (University of Trento)
• George Prekas, Operating Systems (École Polytechnique Fédérale de Lausanne)
• Roman Prutkin, Graph Algorithms (Karlsruhe Institute of Technology)
• Siva Reddy, Multilingual Semantic Parsing (The University of Edinburgh)
• Immanuel Trummer, Structured Data Analysis (École Polytechnique Fédérale de Lausanne)
• Margarita Vald, Security (Tel Aviv University)

North America

• Waleed Ammar, Natural Language Processing (Carnegie Mellon University)
• Justin Meza, Systems Reliability (Carnegie Mellon University)
• Nick Arnosti, Market Algorithms (Stanford University)
• Osbert Bastani, Programming Languages (Stanford University)
• Saurabh Gupta, Computer Vision (University of California, Berkeley)
• Masoud Moshref Javadi, Computer Networking (University of Southern California)
• Muhammad Naveed, Security (University of Illinois at Urbana-Champaign)
• Aaron Parks, Mobile Networking (University of Washington)
• Kyle Rector, Human Computer Interaction (University of Washington)
• Riley Spahn, Privacy (Columbia University)
• Yun Teng, Computer Graphics (University of California, Santa Barbara)
• Carl Vondrick, Machine Perception, (Massachusetts Institute of Technology)
• Xiaolan Wang, Structured Data (University of Massachusetts Amherst)
• Tan Zhang, Mobile Systems (University of Wisconsin-Madison)
• Wojciech Zaremba, Machine Learning (New York University)

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Monitoring the Worlds Forests with Global Forest Watch

Posted by Crystal Davis, Director of Global Forest Watch, the World Resources Institute, and Dave Thau, Developer Advocate, Google Earth Engine

Cross-posted at the Google Lat Long Blog

By the time we find out about deforestation, it’s usually too late to take action.

Scientists have been studying forests for centuries, chronicling the vital importance of these ecosystems for human society. But most of us still lack timely and reliable information about where, when, and why forests are disappearing.

This is about to change with the launch of Global Forest Watch—an online forest monitoring system created by the World Resources Institute, Google and a group of more than 40 partners. Global Forest Watch uses technologies including Google Earth Engine and Google Maps Engine to map the world’s forests with satellite imagery, detect changes in forest cover in near-real-time, and make this information freely available to anyone with Internet access.

By accessing the most current and reliable information, everyone can learn what’s happening in forests around the world. Now that we have the ability to peer into forests, a number of telling stories are beginning to emerge.

Global forest loss far exceeds forest gain

Pink = tree cover loss

Blue = Tree cover gain

According to data from the University of Maryland and Google, the world lost more than 500 million acres of forest between 2000 and 2012. That’s the equivalent of losing 50 soccer fields’ worth of forests every minute of every day for the past 13 years! By contrast, only 0.8 million km2 have regrown, been planted, or restored during the same period.


The United States’ most heavily forested region is made up of production forests

Pink = tree cover loss Blue = Tree cover gain

The Southern United States is home to the nation’s most heavily forested region, making up 29 percent of the total U.S. forest land. Interestingly, the majority of this region is “production forests.” The mosaic of loss (pink) and gain (blue) in the above map shows how forests throughout this region are used as crops – grown and harvested in five-year cycles to produce timber or wood pulp for paper production.

This practice of “intensive forestry” is used all over the world to provide valuable commodities and bolster regional and national economies. WRI analysis suggests that if managers of production forests embrace a “multiple ecosystem services strategy”, they will be able to generate additional benefits such as biodiversity, carbon storage, and water filtration.


Forests are protected in Brazil’s indigenous territories

Pink = tree cover loss Dark green = forest Light green = Degraded land or pastures

The traditional territory of Brazils Surui tribe is an island of green surrounded by lands that have been significantly degraded and deforested over the past 10+ years. Indigenous communities often rely on forests for their livelihoods and cultural heritage and therefore have a strong incentive to manage forests sustainably. However, many indigenous communities struggle to protect their lands against encroachment by illegal loggers, which may be seen in Global Forest Watch using annual data from the University of Maryland and Google, or monthly alerts from Imazon, a Brazilian NGO and GFW partner.


Make Your Own Forest Map

Previously, the data required to make these maps was difficult to obtain and interpret, and most people lacked the resources necessary to access, view, and analyze the the information. With Global Forest Watch, this data is now open to anyone with Internet access. We encourage you to visit Global Forest Watch and make your own forest map. There are many stories to tell about what is happening to forests around the world—and your stories can lead to action to protect these special and threatened places. What story will you tell?

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Google joins the Global Alliance for Genomics and Health

Posted by Jonathan Bingham, Product Manager

Generating research data is easier than ever before, but interpreting and analyzing it is still hard, and getting harder as the volume increases. This is especially true of genomics. Sequencing the whole genome of a single person produces more than 100 gigabytes of raw data, and a million genomes will add up to more than 100 petabytes. In 2003, the Human Genome Project completed after 15 years and $3 billion. Today, it takes closer to one day and $1,000 to sequence a human genome.

This abundance of new information carries great potential for research and human health -- and requires new standards, policies and technology. That’s why Google has joined the Global Alliance for Genomics and Health. The Alliance is an international effort to develop harmonized approaches to enable responsible, secure, and effective sharing of genomic and clinical information in the cloud with the research and healthcare communities, meeting the highest standards of ethics and privacy. Members of the Global Alliance include leading technology, healthcare, research, and disease advocacy organizations from around the world.

To contribute to the genomics community and help meet the data-intensive needs of the life sciences, we are introducing:

• a proposal for a simple web-based API to import, process, store, and search genomic data at scale
• a preview implementation of the API built on Google’s cloud infrastructure, including sample data from public datasets like the 1,000 Genomes Project
• a collection of in-progress open-source sample projects built around the common API

Interoperability: One API, Many Apps

Any of the apps at the top (one graphical, one command-line, and one for batch processing) can work with information in any of the repositories at the bottom (one using cloud-based storage and one using local files). As the ecosystem grows, all developers and researchers benefit from each individual developer’s work.

With these first steps, it is our goal to support the global research community in bringing the vision of the Global Alliance for Genomics and Health to fruition. Imagine the impact if researchers everywhere had larger sample sizes to distinguish between people who become sick and those who remain healthy, between patients who respond to treatment and those whose condition worsens, between pathogens that cause outbreaks and those that are harmless. Imagine if they could test biological hypotheses in seconds instead of days, without owning a supercomputer.

We are honored to be part of the community, working together to refine the technology and evolve the ecosystem, and aligning with appropriate standards as they arise.

How you can be involved

To request access to the API for your research, please fill out this simple form to tell us about yourself and your research interests, and we will let you know when we’re ready to work with more partners.

Together with the members of the Global Alliance for Genomics and Health, we believe we are at the beginning of a transformation in medicine and basic research, driven by advances in genome sequencing and huge-scale computing. We invite you to contact us and share your ideas about how to bring data science and life science together.

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The first detailed maps of global forest change

Posted by Matt Hansen and Peter Potapov, University of Maryland; Rebecca Moore and Matt Hancher, Google

Most people are familiar with exploring images of the Earth’s surface in Google Maps and Earth, but of course there’s more to satellite data than just pretty pictures. By applying algorithms to time-series data it is possible to quantify global land dynamics, such as forest extent and change. Mapping global forests over time not only enables many science applications, such as climate change and biodiversity modeling efforts, but also informs policy initiatives by providing objective data on forests that are ready for use by governments, civil society and private industry in improving forest management.

In a collaboration led by researchers at the University of Maryland, we built a new map product that quantifies global forest extent and change from 2000 to 2012. This product is the first of its kind, a global 30 meter resolution thematic map of the Earth’s land surface that offers a consistent characterization of forest change at a resolution that is high enough to be locally relevant as well. It captures myriad forest dynamics, including fires, tornadoes, disease and logging.

Global 30 meter resolution thematic maps of the Earth’s land surface: Landsat composite reference image (2000), summary map of forest loss, extent and gain (2000-2012), individual maps of forest extent, gain, loss, and loss color-coded by year. Click to enlarge

The satellite data came from the Enhanced Thematic Mapper Plus (ETM+) sensor onboard the NASA/USGS Landsat 7 satellite. The expertise of NASA and USGS, from satellite design to operations to data management and delivery, is critical to any earth system study using Landsat data. For this analysis, we processed over 650,000 ETM+ images in order to characterize global forest change.

Key to the study’s success was the collaboration between remote sensing scientists at the University of Maryland, who developed and tested models for processing and characterizing the Landsat data, and computer scientists at Google, who oversaw the implementation of the final models using Google’s Earth Engine computation platform. Google Earth Engine is a massively parallel technology for high-performance processing of geospatial data, and houses a copy of the entire Landsat image catalog. For this study, a total of 20 terapixels of Landsat data were processed using one million CPU-core hours on 10,000 computers in parallel, in order to characterize year 2000 percent tree cover and subsequent tree cover loss and gain through 2012. What would have taken a single computer 15 years to perform was completed in a matter of days using Google Earth Engine computing.

Global forest loss totaled 2.3 million square kilometers and gain 0.8 million square kilometers from 2000 to 2012. Among the many results is the finding that tropical forest loss is increasing with an average of 2,101 additional square kilometers of forest loss per year over the study period. Despite the reduction in Brazilian deforestation over the study period, increasing rates of forest loss in countries such as Indonesia, Malaysia, Tanzania, Angola, Peru and Paraguay resulted in a statistically significant trend in increasing tropical forest loss. The maps and statistics from this study fill an information void for many parts of the world. The results can be used as an initial reference for countries lacking such information, as a spur to capacity building in such countries, and as a basis of comparison in evolving national forest monitoring methods. Additionally, we hope it will enable further science investigations ranging from the evaluation of the integrity of protected areas to the economic drivers of deforestation to carbon cycle modeling.

The Chaco woodlands of Bolivia, Paraguay and Argentina are under intensive pressure from agroindustrial development. Paraguay’s Chaco woodlands within the western half of the country are experiencing rapid deforestation in the development of cattle ranches. The result is the highest rate of deforestation in the world. Click to enlarge

Global map of forest change: http://earthenginepartners.appspot.com/science-2013-global-forest

If you are curious to learn more, tune in next Monday, November 18 to a live-streamed, online presentation and demonstration by Matt Hansen and colleagues from UMD, Google, USGS, NASA and the Moore Foundation:

Live-stream Presentation: Mapping Global Forest Change

Live online presentation and demonstration, followed by Q&A

Monday, November 18, 2013 at 1pm EST, 10am PST

Link to live-streamed event: http://goo.gl/JbWWTk

Please submit questions here: http://goo.gl/rhxK5X

For further results and details of this study, see High-Resolution Global Maps of 21st-Century Forest Cover Change in the November 15th issue of the journal Science.

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