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PROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models. ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dVERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where I DATA CENTER POWER OVERSUBSCRIPTION WITH A MEDIUM VOLTAGE As major web and cloud service providers continue to accelerate the demand for new data center capacity worldwide, the importance of power oversubscription as a lever to reduce provisioning costs has neverbeen greater.
ANEES SHAIKH
Prior to joining Google, he was the Chief SDN Architect in the System Networking product group at IBM, and a research lead at the T.J. Watson Research Center working in all three major divisions (software, services, and systems) of IBM Research. Anees has published widely in the areas of networking, cloud computing, and system management, andCARRIE JUN CAI
About. My research aims to make artificial intelligence systems usable to human beings, so that human-AI interactions are more productive, enjoyable, and fair. I believe AI systems should be designed to augment human agency, and thus approach this process by considering the capabilities and limits of human intelligence. CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED2 To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
PROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models. ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dVERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where I DATA CENTER POWER OVERSUBSCRIPTION WITH A MEDIUM VOLTAGE As major web and cloud service providers continue to accelerate the demand for new data center capacity worldwide, the importance of power oversubscription as a lever to reduce provisioning costs has neverbeen greater.
ANEES SHAIKH
Prior to joining Google, he was the Chief SDN Architect in the System Networking product group at IBM, and a research lead at the T.J. Watson Research Center working in all three major divisions (software, services, and systems) of IBM Research. Anees has published widely in the areas of networking, cloud computing, and system management, andCARRIE JUN CAI
About. My research aims to make artificial intelligence systems usable to human beings, so that human-AI interactions are more productive, enjoyable, and fair. I believe AI systems should be designed to augment human agency, and thus approach this process by considering the capabilities and limits of human intelligence. EXPLORECSR – GOOGLE RESEARCH Increasing student pursuit of computing research is a top priority at Google, especially for students historically marginalized in the field. Since 2018, the exploreCSR awards have supported institutions to design and host research-focused initiatives during the academic year that expose students from marginalized groups to computing research methodologies, career pathways, and exploratory BRAIN TEAM – GOOGLE RESEARCH The goal of the Google Brain team's machine perception efforts is to improve a machine's ability to hear and see so that machines may naturally interact with humans by focusing on building deep learning systems to advance the state of the art and apply ideas to realproducts.
GOODS: ORGANIZING GOOGLE'S DATASETS Goods extracts metadata ranging from salient information about each dataset (owners, timestamps, schema) to relationships among datasets, such as similarity and provenance. It then exposes this metadata through services that allow engineers to find datasets within the company, to monitor datasets, to annotate them in order to enableothers to
SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science HIGH-AVAILABILITY AT MASSIVE SCALE: BUILDING GOOGLE’S DATA Abstract. Google’s Ads Data Infrastructure systems run the multi- billion dollar ads business at Google. High availability and strong consistency are critical for these systems. While most distributed systems handle machine-level failures well, handling datacenter-level failures is less common. In our experience, handling datacenter-levelERIK LUCERO
Erik Lucero is a Staff Research Scientist on the Quantum A.I. team at Google. With over a decade of experience in quantum architectures, Erik has controlled (with high fidelity!) both semiconductor "spin qubits" and superconducting qubits. He has produced a portfolio of iconic photos documenting the quantum processors over the years. TING CHEN – GOOGLE RESEARCH Ting Chen is a research scientist of Google Brain team aiming at advancing general techniques in machine learning and artificial intelligence. His current research interests include self-supervised representation learning, efficient deep neural nets for and with discrete structures, and generative modeling. Before joining Google,he received
AUTOMEMCPY: A FRAMEWORK FOR AUTOMATIC GENERATION OF Memory manipulation primitives (memcpy, memset, memcmp) are used by virtually every application, from high performance computing to userinterfaces.
UNDERSTANDING YOUR USERS: A PRACTICAL GUIDE TO USER This new and completely updated edition is a comprehensive, easy-to-read, "how-to" guide on user research methods. You'll learn about many distinct user research methods and also pre- and post-method considerations such as recruiting, facilitating activities or moderating, negotiating with product developments teams/customers, and getting your results incorporated into the product. CORES THAT DON'T COUNT We are accustomed to thinking of computers as fail-stop, especially the cores that execute instructions, and most system software implicitly relies on that assumption.DATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong. CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dPROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models.SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where IDATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong. CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National ScienceTONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dPROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models. ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where IDATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of VISITING RESEARCHER PROGRAM Through our Visiting Researcher program, both Google and Academia benefit as exciting ideas and research challenges are shared. In doing so, Google's world-class computing infrastructure is utilized to explore new projects at industrial scale, helping universities to be well-equipped to train the next generation of computer scientists to do long-term research. CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.SOFTWARE SYSTEMS
Delivering Google's products to our users requires computer systems that have a scale previously unknown to the industry. Building on our hardware foundation, we develop technology across the entire systems stack, from operating system device drivers all the way up to multi-site software systems that run on hundreds of thousands of computers. We design, build and operate warehouse-scale ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation. GOODS: ORGANIZING GOOGLE'S DATASETS Goods extracts metadata ranging from salient information about each dataset (owners, timestamps, schema) to relationships among datasets, such as similarity and provenance. It then exposes this metadata through services that allow engineers to find datasets within the company, to monitor datasets, to annotate them in order to enableothers to
ERIK LUCERO
Erik Lucero is a Staff Research Scientist on the Quantum A.I. team at Google. With over a decade of experience in quantum architectures, Erik has controlled (with high fidelity!) both semiconductor "spin qubits" and superconducting qubits. He has produced a portfolio of iconic photos documenting the quantum processors over the years. UNDERSTANDING LSTM NETWORKS Understanding LSTM Networks. Christopher Olah. colah.github.io (2015) Download Google Scholar Copy Bibtex.JENNIFER N. WEI
Jennifer is a software engineer with the Brain Research team in Cambridge, MA. She completed her PhD in Chemical Physics at Harvard University. Her primary research interests are applications of machine learning for small molecules. She has published research on applications of machine learning to reaction prediction, inversedesign of
MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.DATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong. CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dPROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models.SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where IDATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong. CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dPROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models.SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where IDATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of VISITING RESEARCHER PROGRAM Through our Visiting Researcher program, both Google and Academia benefit as exciting ideas and research challenges are shared. In doing so, Google's world-class computing infrastructure is utilized to explore new projects at industrial scale, helping universities to be well-equipped to train the next generation of computer scientists to do long-term research. CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.SOFTWARE SYSTEMS
Delivering Google's products to our users requires computer systems that have a scale previously unknown to the industry. Building on our hardware foundation, we develop technology across the entire systems stack, from operating system device drivers all the way up to multi-site software systems that run on hundreds of thousands of computers. We design, build and operate warehouse-scale ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation. GOODS: ORGANIZING GOOGLE'S DATASETS Goods extracts metadata ranging from salient information about each dataset (owners, timestamps, schema) to relationships among datasets, such as similarity and provenance. It then exposes this metadata through services that allow engineers to find datasets within the company, to monitor datasets, to annotate them in order to enableothers to
ERIK LUCERO
Erik Lucero is a Staff Research Scientist on the Quantum A.I. team at Google. With over a decade of experience in quantum architectures, Erik has controlled (with high fidelity!) both semiconductor "spin qubits" and superconducting qubits. He has produced a portfolio of iconic photos documenting the quantum processors over the years.JEFFREY C. MOGUL
Jeff Mogul works on fast, cheap, reliable, and flexible networking infrastructure for Google. Until 2013, he was Fellow at HP Labs, doing research primarily on computer networks and operating systems issues for enterprise and cloud computer systems; previously, he worked MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.JENNIFER N. WEI
Jennifer is a software engineer with the Brain Research team in Cambridge, MA. She completed her PhD in Chemical Physics at Harvard University. Her primary research interests are applications of machine learning for small molecules. She has published research on applications of machine learning to reaction prediction, inversedesign of
DATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong. CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dPROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models.SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where IDATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong. CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n dPROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models.SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.PEYMAN MILANFAR
A bit about my background: Prior to joining Google, I was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014. I was also Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 I was on leave at Google-x, where IDATA MANAGEMENT
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of VISITING RESEARCHER PROGRAM Through our Visiting Researcher program, both Google and Academia benefit as exciting ideas and research challenges are shared. In doing so, Google's world-class computing infrastructure is utilized to explore new projects at industrial scale, helping universities to be well-equipped to train the next generation of computer scientists to do long-term research. CS RESEARCH MENTORSHIP PROGRAM Program details. Students in parallel academic stages and research areas are grouped into virtual mentorship pods with a Google mentor. Pods work toward one of the following goals, shared by each student in the pod and supported by the mentor through group and one-on-one meetings: Defining a research problem.SOFTWARE SYSTEMS
Delivering Google's products to our users requires computer systems that have a scale previously unknown to the industry. Building on our hardware foundation, we develop technology across the entire systems stack, from operating system device drivers all the way up to multi-site software systems that run on hundreds of thousands of computers. We design, build and operate warehouse-scale ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation. GOODS: ORGANIZING GOOGLE'S DATASETS Goods extracts metadata ranging from salient information about each dataset (owners, timestamps, schema) to relationships among datasets, such as similarity and provenance. It then exposes this metadata through services that allow engineers to find datasets within the company, to monitor datasets, to annotate them in order to enableothers to
ERIK LUCERO
Erik Lucero is a Staff Research Scientist on the Quantum A.I. team at Google. With over a decade of experience in quantum architectures, Erik has controlled (with high fidelity!) both semiconductor "spin qubits" and superconducting qubits. He has produced a portfolio of iconic photos documenting the quantum processors over the years. UNDERSTANDING LSTM NETWORKS Understanding LSTM Networks. Christopher Olah. colah.github.io (2015) Download Google Scholar Copy Bibtex. MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.JENNIFER N. WEI
Jennifer is a software engineer with the Brain Research team in Cambridge, MA. She completed her PhD in Chemical Physics at Harvard University. Her primary research interests are applications of machine learning for small molecules. She has published research on applications of machine learning to reaction prediction, inversedesign of
PHD FELLOWSHIP
Nurturing and maintaining strong relations with the academic community is a top priority at Google. The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science andrelated fields.
VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong.TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n d CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation.SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
HIGH-AVAILABILITY AT MASSIVE SCALE: BUILDING GOOGLE’S DATA Abstract. Google’s Ads Data Infrastructure systems run the multi- billion dollar ads business at Google. High availability and strong consistency are critical for these systems. While most distributed systems handle machine-level failures well, handling datacenter-level failures is less common. In our experience, handling datacenter-levelERIK LUCERO
Erik Lucero is a Staff Research Scientist on the Quantum A.I. team at Google. With over a decade of experience in quantum architectures, Erik has controlled (with high fidelity!) both semiconductor "spin qubits" and superconducting qubits. He has produced a portfolio of iconic photos documenting the quantum processors over the years.CARRIE JUN CAI
About. My research aims to make artificial intelligence systems usable to human beings, so that human-AI interactions are more productive, enjoyable, and fair. I believe AI systems should be designed to augment human agency, and thus approach this process by considering the capabilities and limits of human intelligence.PHD FELLOWSHIP
Nurturing and maintaining strong relations with the academic community is a top priority at Google. The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science andrelated fields.
VERSE BY VERSE
Verse by Verse. An experimental AI-powered muse that helps you compose poetry inspired by classic American poets. Something went wrong.TONE TRANSFER
Tone Transfer — Magenta DDSP. Google and the Magenta team collaborated with musicians around the world to turn their instrumental performances into machine learning models. G o o g l e a n d t h e M a g e n t a t e a m c o l l a b o r a t e d w i t h m u s i c i a n s a r o u n d CONTEXT-BASED QUOTATION RECOMMENDATION We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the ENTREPRENEURIAL INNOVATION AT GOOGLE The amount and type of innovation a company achieves are directly related to the way it approaches, fosters, selects, and funds innovation efforts. To maximize innovation and avoid the dilemmas that mature companies face, Google complements the time-proven model of topdown innovation with its own brand of entrepreneurial innovation. ARE NEURAL RANKERS STILL OUTPERFORMED BY GRADIENT BOOSTED To that end, we propose a new neural LTR framework that mitigates these weaknesses, by borrowing ideas from several research fields. Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmarkfor
SERGEY BRIN
Sergey Brin, a native of Moscow, received a bachelor of science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science HIGH-AVAILABILITY AT MASSIVE SCALE: BUILDING GOOGLE’S DATA Abstract. Google’s Ads Data Infrastructure systems run the multi- billion dollar ads business at Google. High availability and strong consistency are critical for these systems. While most distributed systems handle machine-level failures well, handling datacenter-level failures is less common. In our experience, handling datacenter-levelERIK LUCERO
Erik Lucero is a Staff Research Scientist on the Quantum A.I. team at Google. With over a decade of experience in quantum architectures, Erik has controlled (with high fidelity!) both semiconductor "spin qubits" and superconducting qubits. He has produced a portfolio of iconic photos documenting the quantum processors over the years.CARRIE JUN CAI
About. My research aims to make artificial intelligence systems usable to human beings, so that human-AI interactions are more productive, enjoyable, and fair. I believe AI systems should be designed to augment human agency, and thus approach this process by considering the capabilities and limits of human intelligence.AI RESIDENCY
The Google AI Residency Program is a 18-month research training role designed to jumpstart or advance your career in machine learning research. The Google AI Residency Program was created in 2015 with the goal of training and supporting the next generation of deep learning researchers. With machine learning fast becoming a critical area for a BRAIN TEAM – GOOGLE RESEARCH The goal of the Google Brain team's machine perception efforts is to improve a machine's ability to hear and see so that machines may naturally interact with humans by focusing on building deep learning systems to advance the state of the art and apply ideas to realproducts.
SOFTWARE SYSTEMS
Delivering Google's products to our users requires computer systems that have a scale previously unknown to the industry. Building on our hardware foundation, we develop technology across the entire systems stack, from operating system device drivers all the way up to multi-site software systems that run on hundreds of thousands of computers. We design, build and operate warehouse-scalePHD FELLOWSHIP
Nurturing and maintaining strong relations with the academic community is a top priority at Google. The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science andrelated fields.
PROJECT EUPHONIA
Project Euphonia. Project Euphonia is a Google Research initiative focused on helping people with atypical speech be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models. GOODS: ORGANIZING GOOGLE'S DATASETS Goods extracts metadata ranging from salient information about each dataset (owners, timestamps, schema) to relationships among datasets, such as similarity and provenance. It then exposes this metadata through services that allow engineers to find datasets within the company, to monitor datasets, to annotate them in order to enableothers to
SITE RELIABILITY ENGINEERING: HOW GOOGLE RUNS PRODUCTION The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems?LUCY COLWELL
Lucy is a research scientist at Google Research who works closely with colleagues from GAS and Brain to better understand the relationship between the sequence and function of biological macromolecules. Her broader research interests involve understanding how Google's strengths in experimental design and machine learning can be appliedto the
HIGH-AVAILABILITY AT MASSIVE SCALE: BUILDING GOOGLE’S DATA Abstract. Google’s Ads Data Infrastructure systems run the multi- billion dollar ads business at Google. High availability and strong consistency are critical for these systems. While most distributed systems handle machine-level failures well, handling datacenter-level failures is less common. In our experience, handling datacenter-level CORES THAT DON'T COUNT We are accustomed to thinking of computers as fail-stop, especially the cores that execute instructions, and most system software implicitly relies on that assumption.Google Research
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Our researchers publish regularly in academic journals, release projects as open source, and apply research to Google products. See our research philosophy EXPLORE A SAMPLE OF OUR RESEARCH Researchers at Google are working in many domains. See some of our latest research developments from the Google AI blogand elsewhere.
The COVID-19 Research Explorer: An NLU-Powered Tool to Explore COVID-19 Scientific Literature Highlighted Research The COVID-19 Research Explorer: An NLU-Powered Tool to Explore COVID-19 Scientific Literature A Step Towards Protecting Patients from Medication ErrorsHealth & Bioscience
A Step Towards Protecting Patients from Medication Errors A Neural Weather Model for Eight-Hour Precipitation ForecastingAI for Social Good
A Neural Weather Model for Eight-Hour Precipitation Forecasting uDepth: Real-time 3D Depth Sensing on the Pixel 4 Hardware and Architecture uDepth: Real-time 3D Depth Sensing on the Pixel 4 Exploring Nature-Inspired Robot AgilityRobotics
Exploring Nature-Inspired Robot Agility Fast and Easy Infinitely Wide Networks with Neural Tangents Machine Intelligence Fast and Easy Infinitely Wide Networks with Neural Tangents XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization Natural Language Processing XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization We reimagine technology across all areas of Computer Scienceresearch.
Learn how we challenge conventions. See our research areasPUBLICATIONS
We publish hundreds of research papers each year and present our work in a wide range of venues. See some of our most recent research. Accelerator-aware Neural Network Design using AutoMLPreview Abstract
While neural network hardware accelerators provide a substantial amount of raw compute throughput, the models deployed on them must be co-designed for the underlying hardware architecture to obtain the optimal system performance. We present a class of computer vision models designed using hardware-aware neural architecture search and customized to run on the Edge TPU, Google's neural network...View details
Accelerator-aware Neural Network Design using AutoML Berkin Akin , Suyog Gupta On-device Intelligence Workshop at MLSys 2020 (2020) Architecture for a multilingual WikipediaPreview Abstract
Wikipedia’s mission is a world in which everyone can share in the sum of all knowledge. That mission has been very unevenly achieved in the first two decades of Wikipedia, and one of the largest hindrances is the sheer number of languages Wikipedia needs to cover in order to achieve that goal. We argue that we need a new approach to tackle this problem more effectively, a multilingual Wikipedia...View details
Architecture for a multilingual WikipediaDenny Vrandecic
Google (2020)
An Analysis of Object Representations in Deep Visual TrackersPreview Abstract
Fully convolutional deep correlation networks are currently the state of the art approaches to single object visual tracking. It is commonly assumed that these networks perform tracking by detection by matching features of the object instance with features of the scene. Strong architectural priors and conditioning on the object representation is thought to encourage this tracking strategy....View details
An Analysis of Object Representations in Deep Visual Trackers Ross Goroshin , Jonathan Tompson, Debidatta Dwibedi Google Research (2020) Assisted Learning and Imitation PrivacyPreview Abstract
Motivated by the emerging needs of decentralized learners with personalized learning objectives, we present an Assisted Learning framework where a service provider Bob assists a learner Alice with supervised learning tasks without transmitting Bob's private algorithm or data. Bob assists Alice either by building a predictive model using Alice's labels, or by improving Alice's private learning...View details
Assisted Learning and Imitation Privacy Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan ArXiv e-prints , vol. 2004.00566 (2020) Bisect and Conquer: Hierarchical Clustering via Max-Uncut BisectionPreview Abstract
Hierarchical Clustering is an unsupervised data analysis method which has been widely used for decades. Despite its popularity, it had an underdeveloped analytical foundation and to address this, Dasgupta recently introduced an optimization view-point of hierarchical clustering with pair- wise similarity information that spurred a line of work shedding light on old algorithms (e.g., Average-...View details
Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection Alessandro Epasto , Mohammad Mahdian , Sara Ahmadian , VaggosChatziafratis
AISTATS, AISTATS, AISTATS (2020), AISTATS (to appear) A Data-Driven Metric of Incentive CompatibilityPreview Abstract
An incentive-compatible auction incentivizes buyers to truthfully reveal their private valuations. However, many ad auction mechanisms deployed in practice are not incentive-compatible, such as first-price auctions (for display advertising) and the generalized second-price auction (for search advertising). We introduce a new metric to quantify incentive compatibility in both static and dynamic...View details
A Data-Driven Metric of Incentive Compatibility Yuan Deng, Sébastien Lahaie , Vahab Mirrokni, Song Zuo
The Web Conference 2020 (to appear) Adversarial Bandits Policy for Crawling Commercial Web ContentPreview Abstract
The rapid growth of commercial web content has driven the development of shopping search services to help users find product offers. Due to the dynamic nature of commercial content, an effective recrawl policy is a key component in a shopping search service; it ensures that users have access to the up-to-date product details. Most of the existing strategies either relied on simple heuristics,...View details
Adversarial Bandits Policy for Crawling Commercial Web Content Shuguang Han , Michael Bendersky , Przemek Gajda, Sergey Novikov, Marc Najork , Bernhard Friedrich Brodowsky , Alexandrin Popescul Proceedings of the Web Conference 2020 (WWW 2020), pp. 407-417 Dynamic Composition for Conversational Domain ExplorationPreview Abstract
We study conversational domain exploration (CODEX), where the user’s goal is to enrich her knowledge of a given domain by conversing with an informative bot. Such conversations should be well grounded in high-quality domain knowledge as well as engaging and open-ended. A CODEX bot should be proactive and introduce relevant information even if not directly asked for by the user. The bot should...View details
Dynamic Composition for Conversational Domain Exploration Idan Szpektor , Deborah Cohen , Gal Elidan , Michael Fink , Avinatan Hassidim , Orgad Keller , Sayali Kulkarni , Eran Ofek, Sagie Israel Pudinsky, Asaf Revach, Shimi Salant, Yossi Matias The Web Conference, ACM (2020), pp. 12 (to appear) Design in the HCI Classroom: Setting a Research AgendaPreview Abstract
Interaction design is playing an increasingly prominent role in computing research, while professional user experience roles expand. These forces drive the demand for more design instruction in HCI classrooms. In this paper, we distill the popular approaches to teaching design to undergraduate and graduate students of HCI. Through a review of existing research on design pedagogy, an...View details
Design in the HCI Classroom: Setting a Research Agenda Lauren Wilcox , Betsy DiSalvo, Richard Henneman, Qiaosi (Chelsea) Wang Proceedings of the 2019 on Designing Interactive Systems Conference, ACM (2019), pp. 871-883 See our publicationsTEAMS & PEOPLE
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