Microsoft Research https://www.microsoft.com/en-us/research Sat, 25 Feb 2017 22:08:05 +0000 en-US hourly 1 https://wordpress.org/?v=4.7.2 From improving a golf swing to reducing energy in datacenters https://www.microsoft.com/en-us/research/blog/2017-swiss-joint-research-center/ Tue, 21 Feb 2017 17:00:14 +0000 https://www.microsoft.com/en-us/research/?p=364241 2017 Swiss Joint Research Center kick off By Scarlet Schwiderski-Grosche, Senior Research Program Manager Recently, we celebrated an important milestone for our Swiss Joint Research Center (Swiss JRC). We welcomed top researchers from all partners to a workshop at the Microsoft Research Cambridge Lab, to kick off a new phase in our collaboration. This workshop […]

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2017 Swiss Joint Research Center kick off

By Scarlet Schwiderski-Grosche, Senior Research Program Manager

Attendees of the 2017 Swiss Joint Research Center Workshop in Cambridge, UK.

MSR Swiss Joint Research Center

(left to right) Aurelien Lucchi and Sebastian Stich, Postdoctoral Researchers at ETH Zurich and EPFL, and Martin Jaggi, Assistant Professor at EPFL, at the workshop.

Recently, we celebrated an important milestone for our Swiss Joint Research Center (Swiss JRC). We welcomed top researchers from all partners to a workshop at the Microsoft Research Cambridge Lab, to kick off a new phase in our collaboration. This workshop represented the end of a busy 10-month period for the Swiss JRC during which we ramped down projects from the first phase, and conducted a Call for Proposals for the selection of projects for the second phase. At the workshop, researchers from the Swiss JRC presented their selected proposals to kick off the collaborations in the new funding cycle.

First, a little background. The Swiss JRC is a collaborative research engagement between Microsoft Research and the two universities that make up the Swiss Federal Institutes of Technology: ETH Zurich (Eidgenössische Technische Hochschule Zürich, which serves German-speaking students) and EPFL (École Polytechnique Fédérale de Lausanne, which serves French-speaking students). The Swiss JRC is a continuation of a collaborative engagement that began back in 2009, when the same three partners embarked on ICES (Innovation Cluster for Embedded Software) and was renewed for another five years in 2014. Basically, university researchers collaborate with Microsoft researchers to solve problems in computer science.

Microsoft Research Swiss Joint Research Center

Pamela Delgado, PhD student on the EPFL project with Florin Dinu, “Towards Resources-Efficient Data Centers”

With this workshop, the Swiss JRC kicked off 10 projects, four between ETH Zurich and Microsoft and six between EPFL and Microsoft. These projects were chosen from 20 proposals assessed by the Swiss JRC steering committee for their intellectual merit, potential scientific and societal impact and evidence of strong collaborative interest between the project partners.

One compelling project uses drones that follow you around while you ski or play golf, then gives you feedback for improvement of your form—think of it as a personal trainer/GoPro/drone combo that can both figure out how to video you while you do an activity, as well as analyze your performance and make recommendations for improvements. Another drone-based project (or as we like to call them, micro-aerial vehicles, or MAVs), makes the MAV easier to control via a solution-based approach, versus movement-based controls. This project asks, “What is the goal of the MAV flight?” and solves for that, versus making the operator think about both “Where should this MAV go?” and “What should it do while it’s flying?”

Microsoft Swiss Joint Research Center

Babak Falsafi, Professor of Computer and Communication Sciences, EPFL

Other projects address new requirements in the data center, aiming at making data processing more efficient and essentially helping to reduce energy usage. One set of projects assesses data-intensive applications as are common in, for example, machine learning, graph processing, and bioinformatics. These projects explore near-memory processing, better server utilization, improved data clustering, and new approaches to transactional processing. Another set of projects leverages new hardware architectures based on, for example, FPGAs (Field Programmable Gate Arrays) and DRAM (Dynamic Random-Access Memory). Some projects address mechanisms to off-load expensive computations to achieve massive parallelism or to co-locate different stages of deep learning on the same platform. All of these projects propel the leading edge of artificial intelligence.

“Emerging silicon technologies provide an opportunity to offload data management services to near-memory accelerators for better performance. Through several Microsoft Research collaborations, including this funding round’s NeMeSys project, we are rapidly propelling the state-of-the-art in near-memory processing.” – Babak Falsafi, Professor of Computer and Communication Sciences, EPFL

Here’s the list of projects and their principal investigators:

Data Science with FPGAs in the Data Center
Gustavo Alonso, ETH Zurich
Ken Eguro, Microsoft Research, Redmond lab

Human-Centric Flight II: End-user Design of High-level Robotic Behavior
Otmar Hilliges, ETH Zurich
Marc Pollefeys, Microsoft Analog Research & Development

Tractable by Design
Thomas Hofmann and Aurelien Lucchi, ETH Zurich
Sebastian Nowozin, Microsoft Research, Cambridge lab

Enabling Practical, Efficient and Large-Scale Computation Near Data to Improve the Performance and Efficiency of Data Center and Consumer Systems
Onur Mutlu and Luca Benini, ETH Zurich
Derek Chiou, Microsoft Relevance and Intent, Research &Development

Towards Resource-Efficient Data Centers
Florin Dinu, EPFL
Christos Gkantsidis and Sergey Legtchenko, Microsoft Research, Cambridge lab

Near-Memory System Services
Babak Falsafi, EPFL
Stavros Volos, Microsoft Research, Redmond lab

Coltrain: Co-located Deep Learning Training and Inference
Babak Falsafi and Martin Jaggi, EPFL
Eric Chung, Microsoft Research, Redmond lab

From Companion Drones to Personal Trainers
Pascal Fua and Mathieu Salzmann, EPFL
Debadeepta Dey, Ashish Kapoor, and Sudipta Sinha, Microsoft Research, Redmond lab

Revisiting Transactional Computing on Modern Hardware
Rachid Guerraoui and Georgios Chatzopoulos, EPFL
Aleksandar Dragojevic, Microsoft Research, Cambridge lab

Fast and Accurate Algorithms for Clustering
Michael Kapralov and Ola Svensson, EPFL
Yuval Peres, Nikhil Devanur and Sebastien Bubeck, Microsoft Research, Redmond lab

Related:

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Partnership yields key breakthroughs in VR’s “grand challenge” https://www.microsoft.com/en-us/research/blog/partnership-yields-key-breakthroughs-vr-grand-challenge/ Mon, 06 Feb 2017 17:00:56 +0000 https://www.microsoft.com/en-us/research/?p=361382 By Noboru Sean Kuno, Research Program Manager, Microsoft Research Asia The potential for virtual reality (VR) to upend industrial design, medicine, and other specialized fields has now vaulted the emerging field into the ranks of what the National Academy of Engineering calls its 14 grand challenges of the 21st century, an eclectic list of endeavors […]

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By Noboru Sean Kuno, Research Program Manager, Microsoft Research Asia

The potential for virtual reality (VR) to upend industrial design, medicine, and other specialized fields has now vaulted the emerging field into the ranks of what the National Academy of Engineering calls its 14 grand challenges of the 21st century, an eclectic list of endeavors from preventing nuclear terror to securing cyberspace.

The importance of improving VR and 3D immersive communication has been a cornerstone of Microsoft’s long term investment in this technology space, resulting in multiple innovations from

Dr. Gene Cheung, associate professor, National Institute of Informatics, Japan

Dr. Gene Cheung, associate professor, National Institute of Informatics, Japan

Microsoft’s Kinect for Xbox 360 Sensor, Surface Hub and HoloLens to Windows Creator update.

Collaborating with partners

Realizing more immersive communication via 3D applications requires a quantum leap in the capture and exchange of 3D geometry that can only be achieved with an ongoing commitment to signal processing research. At the heart of this effort is our collaborative research (CORE) project with academic partners including Dr. Gene Cheung, associate professor at Japan’s National Institute of Informatics, who has been tackling this problem for years.

Breakthrough

Using depth-sensing devices such as the Kinect Sensor, researchers developed an algorithm to enable better noise reduction and restore missing details across images. Crucially, they discovered a method to utilize graph-signal smoothness prior to enhancing both natural images (see Fig. 1) and depth images.

<bFigure 1: example of original 4-bit image (left) and bit-depth enhanced image to 8 bits using our approach (right)

Figure 1: example of original 4-bit image (left) and bit-depth enhanced image to 8 bits using our approach (right)

Collaboration with Microsoft Research

Dinei Florencio, senior researcher at Microsoft Research, has been working alongside professor Cheung on research into “rate-constrained 3D surface estimation” and “precision enhancement of multiple 3D depth maps.”

“These two research lines are the most active in our recent collaboration,” Florenicio said. “As we make the needed progress toward immersive communication, I believe Gene’s research is bringing some fundamental contributions.”

Other key members of the project include Cha Zhang of Microsoft Research as well as Pengfei Wan, a former graduate student at Hong Kong University of Science and Technology.

Moving forward

Florencio and Chueng are now leading research into whether active light sensing can accurately detect informative bio-signals — such as pulse/respiratory rate and temperature changes on a face — to reveal stress and mood or indicate if subjects are lying. A key question of the research is whether active light sensing can be extended to reveal the same details for shaded or remote human subjects.

“The project is very interesting in that it tries to estimate bio-signals for more efficient face-to-face communications,” said Tao Mei, senior researcher at Microsoft Research. “The Principal Investigator (PI) proposed to use active imaging, which is entirely non-contact and noninvasive, to solve this problem with a novel idea by analyzing the constructed thermal and depth images in an indoor active image sensing system.”

Upon completion, Professor Cheung will make the research tool publicly available. I am looking forward to seeing continuous progress and achievements from this collaboration. We hope more researchers explore this area to expand the frontier of Virtual Reality technologies and realize Princess Leia’s holographic messaging in future.

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AI is getting smarter; Microsoft researchers want to ensure it’s also getting more accurate https://www.microsoft.com/en-us/research/blog/ai-getting-smarter-microsoft-researchers-ensure-ai-accuracy/ Fri, 03 Feb 2017 17:00:58 +0000 https://www.microsoft.com/en-us/research/?p=360965 By Allison Linn, Senior Writer, Microsoft Just a decade ago, the idea of using technology to do things like automatically translate conversations, identify objects in pictures — or even write a sentence describing those pictures — seemed like interesting research projects, but not practical for real-world use. The recent improvements in artificial intelligence have changed that. These […]

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By Allison Linn, Senior Writer, Microsoft

Marine Carpuat working to make AI technologies accurate

Marine Carpuat, an assistant professor of computer science, works with colleague Philip Resnik, professor of linguistics, in the Computational Linguistics and Information Processing Laboratory at the University of Maryland.

Just a decade ago, the idea of using technology to do things like automatically translate conversations, identify objects in pictures — or even write a sentence describing those pictures — seemed like interesting research projects, but not practical for real-world use.

The recent improvements in artificial intelligence have changed that. These days more and more people are starting to rely on systems built with technologies such as machine learning. That’s raising new questions among artificial intelligence researchers about how to ensure that the basis for many of these systems — the algorithms, the training data and even the systems for testing the tools — are accurate and as unbiased as possible.

Ece Kamar

Ece Kamar, researcher

Ece Kamar, a researcher in Microsoft’s adaptive systems and interaction group, said the push comes as researchers and developers realize that, despite the fact that the systems are imperfect, many people are already trusting them for important tasks.

“This is why it is so important for us to know where our systems are making mistakes,” Kamar said.

At the AAAI Conference on Artificial Intelligence, which begins this weekend in San Francisco, Kamar and other Microsoft researchers will present two research papers that aim to use a combination of algorithms and human expertise to weed out data and system imperfections. Separately, another team of Microsoft researchers is releasing a corpus that can help speech translation researchers test the accuracy and effectiveness of their bilingual conversational systems.

The data underpinning artificial intelligence

When a developer creates a tool using machine learning, she generally relies on what’s called training data to teach the system to do a particular task. For example, to teach a system to recognize various types of animals, developers would likely show the system many pictures of animals so it could be trained to tell the difference between, say, a cat and a dog.

Theoretically, the system could then be shown pictures of dogs and cats it’s never seen before and still categorize them accurately.

But, Kamar said, training data systems can sometimes have some so-called blind spots that will lead to false results. For example, let’s say the system is only trained with pictures of cats that are white and dogs that are black. Show it a picture of a white dog, and it may make a false correlation and mislabel the dog as a cat.

These problems arise in part because many researchers and developers are using training sets that weren’t specifically designed for learning the task at hand. That makes sense – a set of data that already exists, such as an archive of animal pictures, is cheaper and faster than building the sets on your own – but it makes it all the more important to add these kinds of safety checks.

“Without these, we are not going to understand what kind of biases there are,” Kamar said.

In one of the research papers, Kamar and her colleagues show an algorithm that they think could be used to identify those blind spots in predictive models, allowing developers and researchers to fix the problem. It’s a research project for now, but they hope that it would eventually grow into something that developers and researchers could use to identify blind spots.

“Any kind of company or academic that’s doing machine learning needs these tools,” Kamar said.

Another research paper Kamar and her colleagues are presenting at the AAAI conference aims to help researchers figure out how different types of mistakes in a complex artificial intelligence system lead to incorrect results. That can be surprisingly difficult to parse out as artificial intelligence systems are doing more and more complex tasks, relying on multiple components that can become entangled.

For example, let’s say an automated photo captioning tool is describing a picture of a teddy bear as a blender. You might think the problem is with the component trained to recognize the pictures, only to find that it really lies in the element designed to write descriptions.

Kamar and her colleagues designed a methodology that provides guidance to researchers about how they can best troubleshoot these problems by simulating various fixes to root out where the trouble lies.

A ‘human in the loop’

For this and other research she has been conducting, Kamar said she was strongly influenced by the work she did on AI 100, a Stanford University-based study on how artificial intelligence will affect people over the next century.

Kamar said one takeaway from that work was the importance of making sure that people are deeply involved in developing, verifying and troubleshooting systems – what researchers call a “human in the loop.” That will ensure that the artificial intelligences we are creating augment human capabilities and reflect how we want them to perform.

Testing the accuracy of conversational translation

When developers and academic researchers create systems for recognizing the words in a conversation, they have well-regarded ways of testing the accuracy of their work: Sets of conversational data such as Switchboard and CALLHOME.

Christian Federmann

Christian Federmann, senior program manager

Christian Federmann, a senior program manager working with the Microsoft Translator team, said there aren’t as many standardized data sets for testing bilingual conversational speech translation systems such as the Microsoft Translator live feature and Skype Translator.

So he and his colleagues decided to make one.

The Microsoft Speech Language Translation corpus, which is being released publicly Friday for anyone to use, allows researchers to measure the quality and effectiveness of their conversational translation systems against a data set that includes multiple conversations between bilingual speakers who are speaking French, German and English.

The corpus, which was produced by Microsoft using bilingual speakers, aims to create a standard by which people can measure how well their conversational speech translation systems work.

“You need high-quality data in order to have high-quality testing,” Federmann said.

A data set that hits on the combination of both conversational speech and bilingual translation has been lacking until now.

Marine Carpuat, an assistant professor of computer science at the University of Maryland, who does research in natural language processing, said that when she wants to test how well her algorithms for conversational translation are working, she often has to rely on data that is freely available, such as official translations of European Union documents.

Those kinds of translations weren’t created to test conversational translation systems and they don’t necessarily reflect the more casual, spontaneous way in which people actually talk to each other, she said. That makes it difficult to know if the techniques she has will work when people want to translate a regular conversation, with all the attendant pauses, “ums” and other quirks of spoken language.

Carpuat, who was given early access to the corpus, said it was immediately helpful to her.

“It was a way of taking a system that I know does great on formal data and seeing what happens if we try to handle conversations,” she said.

Will Lewis

Will Lewis, principal technical program manager

The Microsoft team hopes the corpus, which will be freely available, will benefit the entire field of conversational translation and help to create more standardized benchmarks that researchers can use to measure their work against others.

“This helps propel the field forward,” said Will Lewis, a principal technical program manager with the Microsoft Translator team who also worked on the project.

Related:

Allison Linn is a senior writer at Microsoft. Follow her on Twitter

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Microsoft Research PhD fellowships provide financial support to promising researchers https://www.microsoft.com/en-us/research/blog/microsoft-research-phd-fellowships-provide-financial-support/ Thu, 02 Feb 2017 17:00:32 +0000 https://www.microsoft.com/en-us/research/?p=360389 By Jim Pinkelman, Senior Director, Microsoft Research Since 2008, Microsoft Research has been awarding two-year PhD fellowships to computer science and related researchers at leading universities in the United States and Canada. These awards are designed to help promising young researchers focus on their studies, not their finances! This year’s program provides fellowships to 10, […]

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By Jim Pinkelman, Senior Director, Microsoft Research

Since 2008, Microsoft Research has been awarding two-year PhD fellowships to computer science and related researchers at leading universities in the United States and Canada. These awards are designed to help promising young researchers focus on their studies, not their finances!

This year’s program provides fellowships to 10, second- or third-year PhD students who are studying computer science, electrical engineering or mathematics. Recipients receive full tuition for their program, along with a generous living expense and a conference attendance stipend. In addition, the fellows are offered the opportunity to intern with a Microsoft researcher.

To identify these promising researchers, Microsoft Research goes through a rigorous process. First, department chairs nominate their best candidates, up to 9 from each university. Each application is vetted, and Microsoft researchers interview finalists to identify the awardees.

Microsoft Research 2017 PhD Fellowship Awardees

This year’s awardees for the 2017-19 academic years are:

Michael B. Cohen 
Massachusetts Institute of Technology
Mathematics, complexity and cryptography

Bita Darvish Rouhani
University of California, San Diego
Computer architecture and hardware

Michaelanne Dye
Georgia Institute of Technology
Human-computer interaction, social computing and collaboration

Kira Goldner
University of Washington
Algorithms and economic systems

Aditya Grover
Stanford University
Machine learning and intelligence

Silu Huang
University of Illinois at Urbana-Champaign
Data management and mining

Ethan J. Jackson
University of California, Berkeley
Mobility and networking

Saswat Padhi
University of California, Los Angeles
Software engineering and programming languages

Andrew Quinn
University of Michigan
Operating systems and distributed computing

Mengting Wan
University of California, San Diego
Data management and mining

Congratulations, 2017’s awardees! Interested in applying for next year? Applications from department chairs (sorry, no self-referrals) are due in October; ask your department chair if they are planning to participate.

Learn more

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2017 Microsoft Research PhD scholarships support break-through projects in six countries https://www.microsoft.com/en-us/research/blog/2017-microsoft-research-phd-scholarships/ Tue, 31 Jan 2017 17:00:51 +0000 https://www.microsoft.com/en-us/research/?p=359603 By Jim Pinkelman, Senior Director, Microsoft Research Since 2004, the Microsoft Research PhD Scholarship Programme in Europe, the Middle East, and Africa (EMEA) has supported groundbreaking PhD projects. This year we have 17 projects that span six countries, and include research areas such as computational biology, machine learning, and health science. The winning PhD projects for […]

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By Jim Pinkelman, Senior Director, Microsoft Research

Since 2004, the Microsoft Research PhD Scholarship Programme in Europe, the Middle East, and Africa (EMEA) has supported groundbreaking PhD projects. This year we have 17 projects that span six countries, and include research areas such as computational biology, machine learning, and health science.

The winning PhD projects for the 2017-2018 school academic year were selected from 33 PhD supervisor-led proposals. These PhD supervisors will collaborate with an assigned Microsoft Research co-supervisor to support a PhD student for up to three years as he or she carries out the proposed research. Supervisors are actively recruiting graduate students for these PhD projects with final candidates identified by March 2018.

PhD scholarship recipients conduct collaborative research with Microsoft researchers, and many receive internships at our labs. Since the program’s founding, the Microsoft Research PhD Scholarship Programme has supported 200 students from 51 institutions in 18 countries.

The selected PhD projects and their PhD supervisors for 2017 were:

Decoding the Network Logic Governing Resetting of Pluripotency
Austin Smith, University of Cambridge, UK

Deep Reinforcement Learning for Collaborative Game AI to Enhance Player Experience
Sam Devlin, University of York, UK

Designing Specialised Processors for DB Workloads
Anastasia Ailamaki, EPFL, Switzerland

Efficient DNA Storage Using Composite Letters
Zohar Yakhini, Technion, Israel Institute of Technology

Human-Centred Machine Learning for Adaptive Agents with Vision
Rebecca Fiebrink, Goldsmiths University of London, UK

Learning Computing with Torino: a Physical Programming Language Inclusive of Children with Visual Disabilities
Sue Sentence, King’s College London, UK

Logical Approach to Code Generation and Optimization
Greta Yorsh, Queen Mary University of London, UK

Modelling Infective Exacerbations in Cystic Fibrosis
Andres Floto, University of Cambridge, UK

OutSider: Assessing and Mitigating Side-Channel Leaks on Commodity Platforms
Herbert Bos Vrije Universiteit Amsterdam, Holland

Power Efficient Rack-Scale Fabrics
Noa Zilberman, University of Cambridge, UK

Programmable Single-Cell Biocomputers with Scalable Signal Processing Capacity
Baojun Wang, University of Edinburgh, UK

Providing and Verifying Security on Compromised Platforms
François Dupressoir, University of Surrey, UK

Reinforcement Learning for Adaptive User Interaction
Shimon Whiteson, University of Oxford, UK

Shareable Dynamic Media in Hybrid Meetings
Clemens Klokmose, Aarhus University, Denmark

SMVRF: Secure Messaging Verifiably Realized in F*
Chris Brzuska, Technische Universität Hamburg-Harburg (TUHH), Germany

STARCH: SmarT ARchitectures for Data Center Switching
Wayne Luk, Imperial College London, UK

Towards Ethical Development of Symbiotic Human-Machine Systems; Creating Ethical Frameworks and Solutions
Ewa Luger, University of Edinburgh, UK

Training and Tuning Deep Neural Networks: Faster, Stronger, Better
Volkan Cevher, EPFL, Switzerland

Joint Initiative with Informatics with University of Edinburgh:

Improving the Usability of TLS APIs
Kami Vaniea, University of Edinburgh, UK

Project selection process

These projects were assessed via a two-stage review process. During stage one, a panel of Microsoft researchers determined whether the proposed project met the basic selection criteria, including relevance to topics that are being researched at Microsoft Research Cambridge. Those proposals that advanced to stage two were then evaluated by a board of 80 researchers from Microsoft Research Laboratories.

For those interested in applying for scholarships for next year, online applications open September 1, 2017.

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Microsoft Research and the industrial research cycle https://www.microsoft.com/en-us/research/blog/microsoft-research-and-the-industrial-research-cycle/ Mon, 30 Jan 2017 18:24:21 +0000 https://www.microsoft.com/en-us/research/?p=359282 A personal view By Thomas Ball, Research Manager, Research in Software Engineering (RiSE) group, Microsoft Research The industrial research cycle Here is what I have told new hires of Microsoft Research (MSR) since I became a manager some 14 years ago: MSR gives you the freedom to explore and expand the bounds of scientific knowledge, […]

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A personal view

By Thomas Ball, Research Manager, Research in Software Engineering (RiSE) group, Microsoft Research

The industrial research cycle

Thomas Ball, Research Manager, Research in Software Engineering (RiSE) groupHere is what I have told new hires of Microsoft Research (MSR) since I became a manager some 14 years ago:

MSR gives you the freedom to explore and expand the bounds of scientific knowledge, as in academia, but with the added challenge to align your scientific pursuits with company problems and to drive for impact on Microsoft, especially as you grow in seniority at the company.

This statement is still as true today as it was when I joined MSR 17 years ago and reflects MSR’s associated goals of advancing scientific frontiers and positively impacting the company.

I use the model of “The Industrial Research Cycle” to explain how MSR works. Researchers have the freedom to select problems and to explore in their discipline (the left side of the cycle) to advance science. They also have the responsibility and opportunity, once sufficient exploration has taken place, to focus their attention on an area that they believe can produce impact for the company (the right side). Ideally, the problems/solutions that one explores on the left side of the cycle eventually drive impact on the right side. And the experience one gains from the right side not only validates the science at scale, it also pushes exploration in new directions in the next phase. A researcher will go around the cycle many times during their career.

Research Impact at Microsoft and on ScienceImpact over time

It is difficult to simultaneously explore and focus, and to do both well! Instead, one needs to engage in phases of exploration and focus over years.

I use the “Impact” diagram to explain the different forms/shapes of impact. The x-axis measures the level of scientific impact. The y-axis measures the level of Microsoft impact (see box).  One’s impact is measured by the area under the curve. The shape of one’s impact curve changes over time, both as one goes around the industrial research cycle and as one grows in seniority at the company.

During an exploration phase, the shape of one’s impact curve generally is horizontal because the primary audience is the scientific community. During a focus phase, the shape of one’s curve is generally vertical, building on the foundation.

Some measures of Microsoft impactAs one grows in seniority in the company, the expectations for focusing on Microsoft impact increase. On the other hand, junior researchers enjoy more freedom to explore. Fresh Ph.D. hires at MSR still have much work to do to establish themselves as recognized experts in their fields. While some may indeed engage with product teams early in their career, we do not expect junior researchers to jump right in to address problems of the company.

While we encourage our researchers to actively publish, MSR does not emphasize quantity of publications. Quality is our top priority.

The long term play between Microsoft and SciencePipelines and partners

MSR invests in scientific efforts that may not have immediate impact on Microsoft but that will build a new muscle/capability for the company in the long run. I use the “The long term play” diagram to show that a coordinated and long-term effort often is needed to turn scientific results into company impact.

Below are three examples showing the path to impact, which requires working closely with partners over the long term, building relationships and trust, and changing company culture through new ways of approaching a problem.

Automated defect detection and driver quality

In late 1999, Sriram Rajamani and I started the SLAM project at MSR to investigate new approaches for automatically finding code defects in device drivers. When the Windows Driver Quality was formed in 2002, Byron Cook, Jakob Lichtenberg and Vladimir Levin came into the team to deliver a tool called Static Driver Verifier (SDV), based on the SLAM engine. The first version of SDV was delivered with Windows in 2004. During the last decade, SDV’s underlying analysis engine has been improved/replaced by MSR three times (see papers on SLAM2, YOGI and Corral) by different sets of researchers working closely with the Driver Quality team, including Ella Bounimova, Aditya Nori, Rahul Kumar, Shaz Qadeer, Akash Lal and Shuvendu Lahiri.

From empirical software engineering to tools for software engineers

In 2004, I hired Nachi Nagappan into MSR to spearhead Empirical Software Engineering research at Redmond. For five years, Nachi and colleagues Brendan Murphy, Jacek Czerwonka, Christian Bird and Thomas Zimmermann studied key issues affecting software quality and developer productivity, through analysis of product version histories, bug databases and other data sources.

To scale such analyses across the company, Wolfram Schulte joined with Nachi, Brendan and Jacek to create CODEMINE, a data analytics platform for collecting and analyzing Microsoft software engineering process data. This project started around 2009 (codenamed SWEPT) and culminated around 2013, giving insight into software engineering problems across Microsoft product groups. CODEMINE was essential to making a case for the formation of a new team called Tools for Software Engineers, which is moving the company to a cloud-based software engineering infrastructure.

Computer science education

More recently, the Touch Develop project (www.touchdevelop.com) started in MSR in 2011 to make it possible to program scripts for smartphones on smartphones. An unexpected use of Touch Develop was in K-12 computer science education— teachers found that children were engaged by scripting their smartphones to react to environmental stimuli.

This turned into a project with the BBC to create a small physical computing device with an easy-to-use coding platform (built on Touch Develop). One million of these devices, called micro:bits, were delivered in 2016, enough for every fifth grade student in the UK to receive one. Because of the BBC micro:bit, Microsoft is now investing in a new programming platform for CS education.

Organizing for big impact on big problems

Today, we find a handful of companies developing planetary-scale distributed systems. Amazon, Facebook, Google and Microsoft all have built such systems, and are engaged in optimizing them for performance, reliability, availability, security and privacy. Microsoft Azure is one such system, which provides compute, storage and networking services, and interacts with an ever-growing number of mobile devices and IoT endpoints.

Optimizing every level of the stack, from the hardware assets, to the low-level operating system code, to the user-facing services, is key to its success, and affords opportunities for researchers across a wide range of disciplines, including those in systems, formal methods, software engineering and programming languages.

Here are four new, larger-scale projects related to the cloud that the RiSE group is deeply involved in:

  • The P programming language is transforming the way Microsoft programmers undertake the task of building large asynchronous systems. P has been used to develop USB 3.0 drivers in Windows, as well as services in Microsoft Azure.
  • Project Everest is constructing a high-performance, standards-compliant, verified implementation of the full HTTPS ecosystem, from the HTTPS API down to and including cryptographic algorithms such as RSA and AES.
  • Project Parade is parallelizing a large class of seemingly sequential applications by treating runtime dependencies as symbolic values. The results of this project are leading to substantial performance gains in popular algorithms for machine learning and big data.
  • Project Premonition aims to detect pathogens before they cause outbreaks, by creating new technologies to autonomously locate, collect and computationally analyze the blood-borne pathogens carried by mosquitoes.

Want to be part of the industrial research cycle?

No matter if you’re exploring or focusing, the ride at Microsoft Research is an exciting one.  If you are interested in joining us on this journey, please visit our careers page.

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Project PrivTree: Blurring your “where” for location privacy https://www.microsoft.com/en-us/research/blog/project-privtree-blurring-location-privacy/ Fri, 20 Jan 2017 19:15:07 +0000 https://www.microsoft.com/en-us/research/?p=356072 By Winnie Cui, Senior Research Manager, Microsoft Research Asia Data scientist, Anthony Tockar, used publicly available location data to show how celebrities can be tracked throughout New York City, while working on his Master’s Degree at Northwestern University. By cross-referencing public news and photos about celebrities hailing cabs in NYC, Tockar found out exactly where […]

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By Winnie Cui, Senior Research Manager, Microsoft Research Asia

Data scientist, Anthony Tockar, used publicly available location data to show how celebrities can be tracked throughout New York City, while working on his Master’s Degree at Northwestern University. By cross-referencing public news and photos about celebrities hailing cabs in NYC, Tockar found out exactly where celebrities climbed into cabs, where they traveled and even how much they paid!

As this example shows, location-based services, pulling an individual’s location data from GPS, IP addresses and Wi-Fi network mapping, can be a privacy nightmare. But they can also be incredibly valuable, offering real-time navigation, local weather, geographically targeted search engine results, and other useful functions.

A 2011 Microsoft survey, Location Usage & Perceptions, found that 94 percent of customers considered location-based services valuable. However, the same survey found that 52 percent were concerned about the privacy issues related to the use of geolocation data.

The privacy issue is now a focus of attention in the research community. “Today’s computing power and scale of publicly available data makes it easier to identify individuals from the data,” said Professor Xiaokui Xiao at Nanyang Technological University (NTU).

Recently, the collaboration between Professor Xiaokui Xiao’s team and Dr. Xing Xie’s group at Microsoft Research Asia in Beijing has found a way that might alleviate the privacy concerns. The team proposes a data manipulation technique, called PrivTree, which pre-processes geolocation data to protect individual privacy. Subsequently, the privatized data can be safely used in any prospective analysis, or even made publicly available, without further risk to an individual’s privacy.

PrivTree works by mathematically “blurring” the geolocation information of a specific individual, while maintaining overall accuracy for the dataset as a whole. In the example below, individuals in the dataset are projected onto a map by their geolocation coordinates.

PrivTree geolocation example

Each marker represents an individual in the geolocation database.

Next, PrivTree goes through two phases to “blur out” the geolocation information of each individual.

Phase 1: Map Partitioning

The map is partitioned into a few sub-regions, based on the density of the data points.

Phase 2: Location Perturbation

Using statistical analysis, individuals are subjected to a perturbation scheme where they are randomly removed, added or shuffled to guarantee privacy while maintaining statistical accuracy. A new geolocation database is ready to use, after applying location perturbation to each sub-region.

This ends up with a new set of data points that follows a similar distribution to the original data, but the real location of each participant has been masked. The privatized data is then released as the output of PrivTree. PrivTree can be extended to support all kinds of location data – for example, your daily jogging route uploaded to a health app. The research paper, PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions was accepted by ACM SIGMOD 2016, the world’s top data management conference.

Professor Xiao said this about collaborating with Microsoft researchers, “Microsoft Research Asia’s expertise in managing large sets of geolocation data, such as Beijing taxi data, played a crucial role to the success of this project. It helped us develop and test our model.”

Professor Xiao plans to further integrate PrivTree techniques into Microsoft’s location-based services to provide privacy protection. Dr. Xing Xie, Senior Researcher at Microsoft Research Asia, and a collaborator on this project, observed “Data privacy is a critical challenge in the cloud computing era, especially for user-generated location data that contains a lot of private knowledge about individuals. We hope this joint work can contribute to–and eventually lead to–a safer world for everyone.”

Learn more:

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Microsoft continues to support data science research with $3M cloud credits to NSF BIGDATA program https://www.microsoft.com/en-us/research/blog/microsoft-continues-to-support-data-science-research/ Wed, 18 Jan 2017 17:31:14 +0000 https://www.microsoft.com/en-us/research/?p=352403 By Vani Mandava, Director, Data Science, Microsoft Research The National Science Foundation has launched a new solicitation in 2017 for the advancement of data science research and applications. The solicitation, titled Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA), is inviting proposals under two categories: Foundations […]

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By Vani Mandava, Director, Data Science, Microsoft Research

The National Science Foundation has launched a new solicitation in 2017 for the advancement of data science research and applications. The solicitation, titled Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA), is inviting proposals under two categories: Foundations (F),focusing on fundamental computational sciences with broad applicability to big data problems; and innovative applications (IA) targeting translational activities in the domain sciences combined with methodological disciplines including statistics or modeling.

Microsoft is among the companies providing cloud credits for the qualifying projects.

Data Science

This new collaboration builds on previous efforts between Microsoft and NSF, and the Azure platform has added significant functionality and resources in recent years, including massive investments in open source and advanced data and intelligence services offered under its Cortana Intelligence Suite. More recently, Microsoft committed an additional $3 million in cloud credits to the NSF-supported Big Data Regional Innovation Hubs & Spokes (BD Hubs & Spokes) program. With the new NSF BIGDATA partnership, Microsoft has increased its commitment to NSF affiliated programs to $6 million over a period of three to four years.

The solicitation requires participants to include a big data challenge of intellectual merit to at least one application domain from one or more relevant participating NSF directorates, namely Biological Sciences (BIO), Computer and Information Science and Engineering (CISE); Education and Human Resources (EHR); Engineering (ENG); Mathematical and Physical Sciences (MPS), through its Division of Mathematical Sciences (DMS); and Social, Behavioral and Economic sciences (SBE), or the U.S. Department of Treasury’s Office of Financial Research (OFR).

In addition, proposals requesting access to cloud credits/resources are required to include a detailed usage/resource consumption plan, with the amount and type of storage, compute, network resources or cloud services that incur costs. Microsoft is one of the participating cloud vendors that will provide cloud compute and data services to the NSF BIGDATA projects that are funded.

The minimum request for cloud resources for any BIGDATA proposal, regardless of the NSF budget request, may be $100,000 to serve the program objective of large-scale experimentation and impact. Participants who request Microsoft cloud resources can compute their anticipated cloud spend on through the Azure pricing calculator.

“We have enabled hundreds of data scientists and researchers with research project grants awarded through our Azure for Research Data Science program. We look forward to our participation in the NSF BIGDATA solicitation to enable larger collaborative projects,” said Jim Pinkelman, senior director, Microsoft Research.

NSF anticipates awarding a total of $26.5 million to between 27 and 35 projects, subject to availability of funding. Proposals are due to NSF between March 15 and March 22, 2017. For detailed submission instructions, refer to the NSF Program solicitation 17-534. Researchers may submit proposals via Grants.gov or the NSF FastLane System.

Learn more:

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Stack of Microsoft researchers earn distinctions from premier computing society https://www.microsoft.com/en-us/research/blog/microsoft-researchers-earn-acm-fellows-awards/ Thu, 08 Dec 2016 14:05:19 +0000 https://www.microsoft.com/en-us/research/?p=333053 By John Roach, Writer, Microsoft Research Eight computer scientists at Microsoft research labs around the world have been honored as Fellows of the Association of Computing Machinery, the world’s largest computing society. The organization also named five Microsoft researchers to their list of Distinguished Members. The honors recognize the individuals’ significant contributions and impact to […]

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By John Roach, Writer, Microsoft Research

Abigail Sellen, Deputy Directory and Principal Researcher, Microsoft Research Cambridge in the U.K., ACM Fellow

Abigail Sellen, Deputy Director and Principal Researcher, Microsoft Research Cambridge in the U.K., ACM Fellow. Photography, Jonathan Banks

Eight computer scientists at Microsoft research labs around the world have been honored as Fellows of the Association of Computing Machinery, the world’s largest computing society. The organization also named five Microsoft researchers to their list of Distinguished Members.

The honors recognize the individuals’ significant contributions and impact to computer science across a range of disciplines and highlight the “tremendous respect, reputation and visibility of Microsoft researchers in the external scientific and engineering community,” said Jeannette Wing, corporate vice president, Microsoft Research.

Read the full story on the Next at Microsoft blog.

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Substance, not hype, powers AI excitement at premier machine learning conference https://www.microsoft.com/en-us/research/blog/substance-not-hype-powers-ai-excitement-premier-machine-learning-conference/ Fri, 02 Dec 2016 06:00:57 +0000 https://www.microsoft.com/en-us/research/?p=328967 By Christopher Bishop, Distinguished Scientist and Director of Microsoft Research Cambridge Lab This month, I will attend the Conference and Workshop on Neural Information Processing Systems (NIPS), the premier gathering in the machine learning field. I’ve participated in this conference most years since it began in 1987 and I’m looking forward once again to catching […]

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By Christopher Bishop, Distinguished Scientist and Director of Microsoft Research Cambridge Lab

Christopher Bishop, Distinguished Scientist, Managing Director, Microsoft Research Cambridge Lab, Artificial Intelligence

This month, I will attend the Conference and Workshop on Neural Information Processing Systems (NIPS), the premier gathering in the machine learning field. I’ve participated in this conference most years since it began in 1987 and I’m looking forward once again to catching up with colleagues and friends as well as exploring new developments in the field. Until recently, the conference attracted a few hundred attendees. The number of participants has grown rapidly in recent years and this year there are more than 4,500 people registered!

This explosion of activity in machine learning is remarkable and reflects the positive trend of research making its way to the marketplace. The first manifestation was the growing interest in “big data.” More recently, the focus shifted to “artificial intelligence.” I am regularly asked to speak on AI, including our research as well as more generally about the social, economic, business, and government policy implications. Everyone wants to know what AI means for them. And while there may be more to AI than machine learning, the resurgence of interest in building intelligent machines undoubtedly stems from advances in machine learning, including deep neural networks.

Is this excitement about AI just hype, or is there substance too? In my view, computing is undergoing the most substantial transformation since the foundations of the field were laid by Alan Turing some eight decades ago. This revolution has two complementary aspects. One is the shift from software solutions that are hand-crafted to solutions learned from data. The second transformation underway is from a view of computation as logic to one involving uncertainty expressed through probabilities. Learning from data and computing with uncertainty are intimately linked. From a probabilistic perspective, machine learning can be viewed as a reduction in uncertainty as a result of observing new data. This process is intrinsically sequential and open-ended, with the posterior distribution resulting from observations so far acting as the prior distribution for the next round of data.

This reduction in uncertainty is illustrated in the Clutter productivity feature, first designed in our labs and recently introduced into the Microsoft Exchange email system, used regularly by tens of millions of users. The feature employs a hierarchical probabilistic model to classify a user’s email into high and low priority. Since definition of high and low priority varies from one user to another, a hierarchical model is used to enable personalization. At the top level of the hierarchy is a probabilistic model learned across a large population of users. New users experience this prior model. The system continually adapts based on the user’s own email usage, providing each user a personal set of parameters derived from the shared prior. This allows a new user to have a positive initial experience, while also providing a system that continues to learn thereby creating a customized experience for each user.

Writing the software to implement these kinds of probabilistic models can be complex and challenging. In this case, however, the process was streamlined by creating the code automatically using Infer.NET, which provides a programming language and associated compiler. Infer.NET supports probabilistic variables as first class citizens and provides an elegant example of probabilistic programming. While there are numerous probabilistic programming languages under development, the focus of Infer.NET is efficient inference for large-scale applications.

Microsoft is placing machine learning and AI at the core of its strategy and we are looking for exceptionally talented scientists and engineers interested in this field to join us. We recently created a new AI and Research group of more than 5,000 researchers and engineers dedicated to developing advances in AI. There is tremendous breadth and depth of talent across the group and ample opportunities for teams to collaborate on some of the world’s toughest research and engineering challenges, resulting in the ability to positively impact the lives of millions. Microsoft has long been at the forefront of machine learning and AI. Today, Microsoft holds the record error rate for object recognition in images and recently announced the first achievement of human parity in word-error rate for speech recognition, both built on deep-learning technology developed in our research labs. We also recently announced the creation of the world’s first exa-scale AI supercomputer, based on a global deployment of FPGAs (field programmable gate arrays) in our data centers to complement our CPU and GPU capabilities.

At Microsoft, we are democratizing AI to empower every person and every organization on the planet to achieve more. I’m looking forward to NIPS as a superb opportunity to meet and talk to people about how they can join us in achieving this goal.

Related:

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