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Agenda with Abstracts

Microsoft Research Faculty Summit 2013

Monday, July 15, 2013

Time

Event/Topic

Location

8:00–9:00

Breakfast

9:00–10:30 Opening Plenary Session Kodiak

9:00–9:15

Welcome and Introduction

Tony Hey, Vice President, Microsoft Research Connections

Eric Rudder, Executive Vice President, Advanced Strategy and Research, Microsoft

9:15–10:30

Innovation and Opportunity: The Contribution of Computing to Improving Our World

Keynote speaker: Bill Gates

Bill Gates will field audience questions and share his thoughts and perspective on computing’s impact on society. Rick Rashid, chief research officer of Microsoft, will moderate the discussion.

10:30–10:45 Break

10:45–Noon

Breakout Sessions

 

Prediction Engines

Chair: David Pennock, Microsoft Research

Speakers: Sanmay Das, Virginia Polytechnic Institute & State University; Robin Hanson, George Mason University; and Nicolas Lambert, Stanford University

From crowdsourcing to data-driven models, technology offers new ways to collect and aggregate information on an unprecedented scale, allowing researchers to make reliable predictions about elections, policy, corporate decisions, economics, finance, sports, and entertainment. This session will report on progress in the science and engineering of polls, prediction markets, and forecast models. Examples include a fundamental model that correctly predicted 49 of 50 states nine months before the US Presidential election, and combinatorial prediction markets capable of estimating billions of complex predictions like correlations between states. The session will include talks from academic experts in the field as well as Microsoft researchers investigating prediction markets, scoring rules, polls, and forecast models.

Cascade

Efficiently Verifying Outsourced Cloud Computation: from Theory to Practice

Chair: Bryan Parno, Microsoft Research

Speakers: Michael Mitzenmacher, Harvard University; Michael Walfish, University of Texas at Austin

The growth of cloud computing contributes to a desire to outsource computing from a client device to an online service. However, the client should be able to efficiently verify the correctness of the results returned, to guard against malicious or malfunctioning services. The session speakers have each built a different system for verifying arbitrary outsourced computations. Each draws on a different strand of theory and applies different cryptographic and system-engineering techniques. Collectively, their efforts have brought the cost of verification down by over 20 orders of magnitude in the last three years, making verifiable computation close to practical for a variety of applications.

Rainier

Database Systems Exploiting New Hardware Platforms

Chair: David Lomet, Microsoft Research

Speakers: Paul Larson, Microsoft Research; Justin Levandoski, Microsoft Research; Thomas Neumann, Technische Universität München; and Jignesh Patel, University of Wisconsin, Madison

Main memory databases have become increasingly important as a way to provide high-performance OLTP (on-line transaction processing) capability. By restricting data to by main memory resident, rather than storing disk resident data entirely in the main memory cache, it is possible to use new technology that results in much better performance. This new setting calls for a revolution in the access methods, concurrency control, and recovery. It reduces the need for high scalability because peak performance is so much higher. This session will explore main memory technologies, the performance that they produce, the problems encountered, and the prospects going forward.

St. Helens

Data in Disasters—the Potential and the Limits

Chair: Kate Crawford, Microsoft Research

Speakers: Eytan Adar, University of Michigan-Ann Arbor; Andrés Monroy-Hernández, Microsoft Research; and Leysia Palen, University of Colorado-Boulder

There has been a rapid expansion in the ability to gather and analyze communication data during acute events. As we saw most recently during Hurricane Sandy, people use social media services like Twitter and Facebook to share a range of personal information: be it their location, their health, the status of power and water access, or images of the disaster in their neighborhood. Fields such as crisis informatics and machine learning have brought powerful new insights to how communities deal with crises by drawing on this data. However, there are also causes for concern, such as the privacy and longevity of that data, and the invisibility of the many people and communities that are either not using social media or are living in areas with damaged or overwhelmed ICT infrastructure. This panel draws together a diverse set of individuals who study information use and sharing during acute events. Our panelists will explore research avenues for disaster data, including the difficulties with the reliability of citizen accounts of disaster, issues with privacy and information sharing during disasters, and the hidden biases of disaster data and what it cannot capture.

Hood
Noon–1:00 Lunch

1:00–2:30

Breakout Sessions

Interaction for Machine Learning

Chair: Ashish Kapoor, Microsoft Research

Co-organizers: Dan Bohus, Ece Kamar, Lihong Li, and Jason Williams, Microsoft Research

Speakers: James Fogarty, University of Washington; Lise Getoor, University of Maryland; Patrice Simard, Microsoft Research; and Jerry Zhu, University of Wisconsin, Madison

The traditional role of the human operator in machine learning problems is that of a batch labeler, whose work is done before the learning even begins. However, humans can provide guidance to a learning system via a richer set of inputs and interact directly with the learning algorithm as it learns. Active research problems in this space include designing interactions for acquiring human guidance, active and life-long learning, interactive clustering, query by selection, learning to rank, and effective crowdsourcing. Addressing these problems requires contributions from multiple disciplines such as human-computer interaction, artificial intelligence, and machine learning.

In this session, we bring together researchers from different disciplines and identify how richer forms of interaction with humans could help machine learning systems, in terms of both surveying the major paradigms and sharing information about new work in this area. Through a combination of presentations and discussions, we hope to gain a better understanding of the available algorithms and best practices, of their inherent limitations, and of challenges and opportunities for future research in this space.

Cascade

The Economics of Computing

Chair: Nicole Immorlica, Microsoft Research

Speakers: Dan Huttenlocher, Cornell University; Muthu Muthukrishnan, Microsoft Research; and Eva Tardos, Cornell University

Many computational tasks require the participation of a diverse set of users. These users are motivated by their own interests and act to maximize the utility they gain from interacting with the system. Examples abound, including online advertising auctions, financial transactions, voting platforms, user-generated content sites, and cloud-computing systems. This session focuses on how to design such systems with appropriate economic incentives for users.

Rainier

Hosting Blazing Fast Services: From 1 Core to 1M Cores 

Chair: Victor Bahl, Microsoft Research

Speaker: David Andersen, Carnegie Mellon University; Vijay Gill, Microsoft; Ratul Mahajan, Microsoft Research

This session will focus on the systems and networking infrastructure that enable Microsoft to host thousands of services that serve hundreds of millions of users globally with an unprecedented level of performance. Microsoft builds and operates best-in-class data centers that contain 2,500 to more than 100,000 servers (per data center), which are connected by using cutting-edge networks within and across data centers. To meet the performance, cost, and availability demands of Microsoft and third-party services that are hosted in our data centers, our systems and networks have evolved to be highly programmable and software-defined, thereby using both hardware and software to solve challenges.

 

Presenters from Microsoft products groups and Microsoft Research will describe the networking technologies that fuel our data centers. They will share and discuss results of their investigations and deployments, all of which are a part of Microsoft’s worldwide, multi-year infrastructure investment and development quest.

St. Helens

Computer-Aided Education

Chair: Donald Brinkman, Microsoft Research

Speakers: Sumit Gulwani, Microsoft Research; Armando Solar Lezama, Massachusetts Institute of Technology; and Zoran Popovic, University of Washington

New forms of education are emerging that offer the potential to amplify the reach of a single educator to embrace thousands of simultaneous learners. Organizations like Khan Academy and Coursera are exploring the opportunities and challenges offered by massively open online courseware. In a classroom of thousands, tasks like assignment grading, problem generation, and student analytics can become intractable. In this session, we will explore approaches that seek to mitigate these challenges by applying machine learning, cloud computing, and other innovative technologies.

Hood
2:30–2:45 Break
2:45–4:15 Breakout Sessions

Machine Learning for Interactive Systems: Practical Challenges and Opportunities

Chair: Lihong Li, Microsoft Research

Co-organizers: Dan Bohus, Ece Kamar, Ashish Kapoor, and Jason Williams, Microsoft Research

Speakers: Dan Bohus, Microsoft Research; Léon Bottou, Microsoft Research, Michael Littman, Brown University; and David Traum, University of Southern California

In many settings in which agents interact with people, such as conversational systems and software assistants, machine learning problems go beyond mapping inputs to outputs with supervised learning. Substantial progress has been made in areas like artificial intelligence, operations research, and statistics. Yet due to the complex nature of these problems, applications are often hindered by challenges that have not received sufficient attention in the research community. As a result, there have been relatively few high-profile deployments of these promising technologies.

The goal of this session is to identify critical bottlenecks for applying machine learning in interactive systems, and to discuss opportunities for future research, informed by case studies of deployed applications. Example topics include challenges that are associated with offline evaluation and optimization, the design of states and reward, and life-long and open-world learning.

Cascade

Towards a Research Platform for Internet of Things (IoT)

Chair: Arjmand Samuel, Microsoft Research

Speakers: Arjmand Samuel, Microsoft Research, and Kamin Whitehouse, University of Virginia

We are living in a world of connected devices. These devices can take a number of shapes and forms: from tablet computers and smartphones, to sensors in the home, to embedded and wearable computers. Increasingly, there is interest in deploying devices and sensors in homes, buildings, vehicles, and any other spaces where human activity occurs. Most of these devices require software architectures and cloud services that are generally developed for either a specific device or class of devices. The prototyping and development of new devices requires new architectures and services, which consume precious research resources. To alleviate this barrier in research, a flexible platform with expandable device interface as well as programmatic access to device data and functionality, are required. In this session, researchers from academia and Microsoft Research will talk about research challenges in connected devices in the home and beyond, along with an overview of the upcoming HomeOS platform.

Rainier

Big Data Platforms

Chair: Surajit Chaudhuri, Microsoft Research

"Big Data” holds the promise of having a transformative effect for enterprises and consumers alike. In this session, leaders of our community discuss how data platforms must evolve to realize the potential of Big Data. The first talk in the session outlines how Microsoft is reimagining its data platform strategy taking into account not only the need to support characteristics of Big Data but also the importance of cloud as the delivery mechanism. The second talk describes an architecture that pulls together the core system components necessary to support data analytics at scale. The final talk tackles the challenge of performance isolation that arises when multiple data analytics tenants share the resources in a cloud platform.

 

  • Big Data at Microsoft
    Speaker: Raghu Ramakrishnan, Microsoft
    We are seeing arguably the most sweeping changes in data management since the relational database revolution of the ‘70s and ‘80s. "Big Data," pushing the limits of data volumes, variety of data types and analyses, and real-time response, has become mainstream. Cloud as a delivery channel has been growing simultaneously. These twin changes require significant changes to how we approach the next generation of data management platforms. In this talk, I will discuss some of the directions we are exploring at Microsoft in STB's Data Platforms Group.
  • Taming Big Data with Spark and Berkeley Data Analytics Stack (BDAS)
    Speaker: Ion Stoica, University of California, Berkeley
    Today's data analytics tools are slow in answering even simple queries, as they typically require sifting through huge amounts of data stored on disk, and are even less suitable for complex computations, such as machine learning algorithms. To address these challenges, for the past four years we have been developing BDAS, an open source data analytics stack. At the core of BDAS is Spark, an in-memory parallel execution engine, which enables us to provide unified support for batch, streaming, and interactive computations, as well as support sophisticated graph based and machine learning algorithms. Today, Spark and other BDAS components are used in production by tens of companies and institutions. In this talk, I'll present the architecture and the main design decisions we made in Spark, as well our future plans.
  • Performance Isolation in Multi-Tenant Cloud Data Services
    Speaker: Vivek Narasayya, Microsoft Research
    Multi-tenancy is essential to increase utilization and reduce operational cost in database-as-a-service platforms such as Microsoft SQL Azure, and “Big Data” platforms such as Hadoop and Cosmos. However, contention for shared resources in a multi-tenant system can result in one tenant’s performance being adversely affected by the workload of other tenants contending for shared resources. Assurances on performance isolation can significantly increase the service quality and the tenants’ experience. Our approach to this problem of providing performance isolation is to enable an abstraction of reservation of key system resources (CPU, I/O, memory, etc.) critical to a tenant’s workload. The major challenges lie in supporting this abstraction without statically allocating resources, and techniques for objectively establishing the service provider’s accountability. In this talk, we outline the key ideas of our approach and show demonstrations of performance isolation in relational database-as-a-service and in Hadoop.
St. Helens

Publishing and Perishing in the Twenty-First Century

Chair: Alex Wade, Microsoft Research

Speakers: Jennifer Lin, Public Library of Science; Filippo Menczer, Indiana University; Enrico Motta, Open University; and Jevin West, University of Washington

The creation of quantifiable measures of the impact and relative importance of a publication, a journal, an individual researcher’s output, or a university is close kin to ranking algorithms in information retrieval. Eugene Garfield developed the journal impact factor (IF) a half-century ago based on a two-year window of citations. And more recently, Jorge Hirsch invented the h-index to quantify an individual’s productivity based on the distribution of citations over one’s publications. There are also several competing “world university ranking” systems in wide circulation. Most traditional bibliometrics seek to build upon the citation structure of scholarship in the same manner that PageRank uses the link structure of the web as a signal of importance, but new approaches (or Alt Metrics) are now seeking to harness usage patterns and social media to assess impact.

Hood
4:15–4:30 Break  
4:30–5:30 Plenary Session Kodiak
 

The Beast from Below: How Changes in the Hardware Ecosystem Will Disrupt Computer Science

Keynote speaker: Doug Burger, Director, Client & Cloud Applications, Microsoft

For decades, computer scientists have relied on steadily advancing hardware capabilities. Moore’s Law coupled with Dennard Scaling have improved von Neumann computing by many orders of magnitude. Notably, the underlying changes have been largely invisible to software, with standard instruction sets and compilers hiding the complexity that has produced those enormous gains. However, the easy times are ending; many orders of magnitude gains in performance and efficiency are still achievable, but getting there will break our current notions of instruction sets, compilers, languages, data types, circuits, devices, and applications. Different classes of applications will be enabled, and whole new areas in computer science will emerge. Fortunately, many of the exciting emerging workloads (machine learning, computer vision, speech recognition, and so forth) will be amenable to this new era. In my talk, I will cover the reasons for this shift, and make some predictions about how it will affect our field.

5:30–6:00 Travel to Kirkland Dock

6:00–9:30

Argosy Cruise on Lake Washington

 

Tuesday, July 16, 2013

Time

Event/Topic

Location 

8:00–9:00

Breakfast

 
9:00–10:30 Plenary Session Kodiak

Looking Over the Horizon: How Basic Research Helps Everyone

Keynote speakers:

Peter Lee, Corporate Vice President, Microsoft Research

Jeannette Wing, Corporate Vice President, Microsoft Research

With 13 labs worldwide, Microsoft Research is committed to advancing the state of the art in computing. In this session, Microsoft Research’s leadership team showcases current research that’s having an impact on technology, science, and society.

10:30–1:00

DemoFest McKinley

10:30–11:30

Engaging with Microsoft Research Hood
Noon–1:00  Lunch Hood 
Noon–1:00

Lunchtime Session

 

Innovations and Research Highlights in Attracting Women in Computing

Moderator: Roy Zimmermann, Microsoft Research

Speakers: Tiffany Barnes, North Carolina State University; Kathryn S. McKinley, Microsoft Research; Ruthe Farmer and Lucy Sanders, National Center for Women Information Technology; Juliana Salles, Microsoft Research; and Constance Steinkuehler, University Wisconsin at Madison

With 1.4 million open jobs in computing by 2018 and only 29 percent of those expected to be filled by women, there has been much research and investigation into how to increase the pipeline. Join top researchers and experts in the field who are making strides in this critical area and discuss the challenges and the research collaborations with Microsoft Research that are making a difference in growing more women in computing. Participants will have the opportunity to become active in the movement to facilitate greater diversity among computer scientists.

Cascade
1:00–2:30 

Breakout Sessions

 

The Coming Genomics Software Revolution?

Chair: Bill Bolosky, Microsoft Research

Speakers: David Haussler, University of California, Santa Cruz; David Heckerman, Microsoft Research; David Patterson, University of California, Berkeley; and George Varghese, Microsoft Research

With the cost to sequence a full human genome soon to fall below US$1,000, most people will have their DNA sequenced and stored in a database along with their medical records. The hardware revolution has largely occurred; this session will focus on the software issues that remain. If there were a database of a million or more fully sequenced genomes together with phenotype information such as disease and treatment codes, what software would be necessary to mine it for biological insights that might eventually lead to drug discovery and personalized medicine?

Cancer is a fundamentally genomic disease, and drugs such as Gleevec have shown the effectiveness of combatting the genomic pathways of disease. Could a large genomic database enable progress like this for other cancers, or for diseases other than cancer? What are the medical and systems implications of such a database? Could genomics engender a new software industry?

We will discuss the existing TCGA (The Cancer Genome Atlas) database currently consisting of 5,000 cancer genomes and the open algorithmic problems that arise in making use of the data. We will describe parallel infrastructure and computing paradigms for genome computation, and new systems issues that must be solved. We will discuss issues in scaling genomic inference in the face of confounding factors. We will also describe challenges that must be solved in order to query a vast genome database interactively in a few seconds.

Cascade 

Modern Programming for and via the Web Browser

Chair: Judith Bishop, Microsoft Research

Speakers: Shriram Krishnamurthi, Brown University; Steve Lucco, Microsoft; Tom Ball, Microsoft Research

Programming for web browser is a complex affair, in the same way that programming of early computers via assembly language was before the advent of higher-level languages such as FORTRAN and C. Today, languages such JavaScript, HTML, and CSS form the basic low-level programming abstractions. This session describes research and technology that makes programming for the web easier and safer. We focus on new developments in JavaScript, investigate the latest changes in TouchDevelop for mobile touch devices, and announce TypeScript, a new language for application-scale JavaScript development.

Rainier

From Data Science to Data Intelligence

Chair: Evelyne Viegas, Microsoft Research

Today, we live in a data-driven world and new directions in data-driven research have already revolutionized big data applications such as gaming, Internet vision, machine translation, and spell checking by bringing machine learning to the core of the information revolution. But to continue to drive innovation, three fundamental aspects of data-driven research need to change: data must be made available to the research community to allow for benchmarking, reproducibility and transparency of experimentation need to happen to enable science, and researchers need to work on solving big and real world problems to advance the state of the art. This session addresses all three aspects of data-driven research by exploring the status of data sharing in data science, and looking at what data intelligence may be.

 

    • Predictive Analytics: The Unrealized Power of Data
      Speaker: Roger Barga, Microsoft
      Business metrics do a fine job summarizing the past. But if you want to predict how customers will respond in the future, there is one place to turn: predictive analytics. By learning from historical data, predictive analytics can deliver value beyond standard business reporting: actionable insights and predictions. These predictions encompass both online and offline processing, predicting which customers will buy, click, respond, convert, or cancel. If you can predict it, you can control it.

      In this talk, I will make the case that predictive models are one of the most valuable data products one can extract from Big Data, given the right tools and techniques. We will examine business, marketing, and web problems solved with predictive analytics and highlight the many ways predictions can be used to drive various business decisions. We will discuss how a predictive model works and how one can create a predictive model by using machine learning algorithms, examining the unique role and workflow of the data scientist. We will include a demonstration of a service on Windows Azure that is designed to support the data scientist in the construction of predictive models by using machine learning over data. I will close the talk with open technical and research challenges required to realize the full potential of predictive analytics.
    • Large-Scale Learning Revisited
      Speaker: Léon Bottou, Microsoft Research

      This presentation shows how large-scale data sets challenge traditional machine learning in fundamental ways.

      • Traditional machine learning describes tradeoffs associated with the scarcity of data. These tradeoffs change nature when we consider instead that computing time is the bottleneck.
      • Traditional machine learning optimizes average losses. Increasing the training set size cannot improve such metrics indefinitely. However, these diminishing returns vanish if we measure instead the diversity of conditions in which the trained system performs well. In other words, big data is not an opportunity to increase the average accuracy, but an opportunity to increase coverage.
      • Since the benefits of big data are related to the diversity of big data, we need conceptual tools to build learning systems that can address all the (changing) aspects of real big data problems. Multitask learning, transfer learning, and deep learning are first steps in this direction. 
    • CodaLab: A Platform for Efficient Collaborative Research
      Speaker: Percy Liang, Stanford University
      We are interested in solving two infrastructural problems in data-centric fields such as machine learning: First, an inordinate amount of time is spent on preprocessing datasets, getting other people's code to run, and writing evaluation/visualization scripts, with much of this effort duplicated across different research groups. Second, only a static set of final results is ever published, leaving it up to the reader to guess how the various methods would fare in unreported scenarios. I will discuss a new platform currently under development that aims to tackle these two problems by creating an online community around sharing and executing modules, thereby streamlining the research process.
St. Helens

Visual Recognition

Chair: Andrew Blake, Microsoft Research

Speakers: Kristen Grauman, University of Texas at Austin; Fei-Fei Li, Stanford University; and Larry Zitnick, Microsoft Research

The fields of Computer Vision and Machine Learning are becoming increasingly intertwined, with many of the recent breakthroughs in object and scene recognition coming from the availability of large labeled datasets and sophisticated machine learning techniques. In this session, leading researchers in these fields share their perspectives on recent advances and current challenges. How do sophisticated machine learning approaches aid in solving difficult recognition problems? What role do large labeled datasets and recognition challenges play in advancing the state of the art and enabling data-driven approaches to recognition? And how can the layout of objects in a scene as well as relationship to natural language models give us an edge in describing complex scenes with multiple actors and objects? These are just some of the questions at the forefront of this rapidly evolving research field.

Hood
1:00–3:30  

Design Expo

Chair: Lili Cheng, Microsoft Research 

Kodiak 
 

Making Data Useful: Improving Your Life, Community, and World

We live in a world that is increasingly alive with sensors and data. The big data, sensor networks and transparency movements have left us with a glut of potentially useful free data that is lying fallow. How can we use this to improve life, local community, and the world at large?

 

People and devices collect and share data both passively—monitoring environmental change—and actively—explicitly capturing and sharing information, combining data with other information, and using in unexpected ways. Today, we are only starting to understand how best to put this data to work to improve our lives and the world around us.

 

How might data—particularly information that makes civic society run, like bus schedules, election cycles, political information, first-hand reporting, volunteer logistics, and sporting and media events—make for a better and more community-oriented place to live?

 

How might this data help us understand the past and present and predict the future with more precision, and how might an individual’s personal data be used to help filter and make information more relevant in different contexts or situations?

 

What are key problems this data can be used to help solve, what new troubles can we anticipate it creates?

 

  • Carnegie Mellon University, School of Design, Pittsburg, PA, United States
    Professors: Peter Scupelli, Bruce Hanington
  • Interdisciplinary Center (IDC), Herzliya School of Communication, Israel
    Professors: Noa Morag, Oren Zuckerman
  • National Institute of Design (NID), Ahmedabad, India
    Professors: Bibhudutta Baral and Rupeshkumar Vyas
  • New York University, Interactive Telecommunications Program, New York, NY, United States
    Professor: Clay Shirky
  • Northumbria, Newcastle upon Tyne, United Kingdom
    Professor: Trevor Duncan
  • Technische Universiteit (TU) Eindhoven, Eindhoven, The Netherlands
    Professor: Berry Eggen
  • Universidad Iberoamericana, Design Department, Mexico City, Mexico
    Professor: Jorge Meza Aguilar
  • University of California at Los Angeles, Design Media Arts, Los Angeles, CA, United States
    Professor: Christian Moeller
  • University of Washington, Interactive Design Division, Seattle, WA, United States
    Professor: Axel Roesler 
 
2:30–2:45  Break   
2:45–4:15 Breakout Sessions  
 

Deep Machine Learning: a Panel

Chairs: Li Deng, Microsoft Research, and John Platt, Microsoft Research

Speakers: Yoshua Bengio, Université de Montréal; Honglak Lee, University of Michigan – Ann Arbor; Andrew Ng, Stanford University; and Ruslan Salakhutdinov, University of Toronto

Deep learning is a sub-field of machine learning that focuses on hierarchical representations of features or concepts, where high-level semantic-like features can emerge via automatic layer-by-layer learning from low-level features. In recent years, deep learning has achieved important successes in a variety of applied artificial intelligence tasks including speech recognition, computer vision, and natural language processing. The implications of such recent work have been prominently covered in recent media with both enthusiasm and skepticism. Since 2009, in partnership with academics, Microsoft Research has been pursuing deep learning research and technology transfer, and has pioneered the development of industry-scale deep learning technology for speech recognition. It is useful to share our experience with wider academic communities and learn from each other. To make the material and directions of interest to a broader computer science audience, we plan to offer a tutorial to demystify the “black-art” label often attached to deep learning.

Cascade 

Spam Wars: the Systems Strike Back

Chair: Judith Bishop, Microsoft Research

Speakers: Saikat Guha, Microsoft Research; Ben Livshits, Microsoft Research; and Stefan Savage, University of California, San Diego

How do we safeguard user privacy in a way that does not disrupt the emerging business models behind cloud computing, online social networks, and mobile ecosystems? Numerous efforts focus on identifying and blocking individual abusive advertising mechanisms, but there is a parallel research direction that undermines the associated means of monetization: payment networks. This session brings together the latest attempts to pit software against software in the malware and spam wars. How do we prevent vulnerabilities in modern cars with electronic keys, or electronic tire- or brake- sensors? We’ll discuss how to combine the goals of privacy and content personalization, especially on mobile devices.

Rainier 

Quantum Computing: the Next Frontier

Chair: Krysta Svore, Microsoft Research

Speakers: Scott Aaronson, Massachusetts Institute of Technology; Charles Marcus, Niels Bor Institute; and Matthias Troyer, Eidgenössische Technische Hochschule Zürich

In 1981, Richard Feynman proposed a device called a “quantum computer” that would take advantage of methods founded on the laws of quantum physics and promise computational speed-ups over classical methods. In the last three decades, quantum algorithms have been developed that offer fast solutions to problems in a variety of fields including number theory, optimization, database search, chemistry, and physics. For quantum devices, this past year marks a significant breakthrough. Recent experiments point to the observation of an elusive particle at the heart of several scalable device proposals, called the Majorana fermion. Advancements in understanding the role of quantum speed-ups in the commercial D-Wave One quantum processor, which has been the subject of intense debate, have also been made. This session will showcase quantum algorithms with real-world applications and highlight breakthroughs in quantum devices, including Majorana-based devices and the D-Wave quantum processor.

St. Helens

Visual Motion and Structure

Chair: Rick Szeliski, Microsoft Research

Speakers: Bill Freeman, Massachusetts Institute of Technology; Shahram Izadi, Microsoft Research; and Noah Snavely, Cornell University

For several decades, the analysis of visual motion and 3-D scene structure have been central to the study of computer vision. Recent breakthroughs in analyzing and amplifying subtle motions can now give us insights into previously unstudied phenomena such as natural (visual) biometric signals and basic structure physics. Similarly, the ability to create detailed large-scale models either from photo collections found on the web or from cheap depth cameras (Kinect) opens up new applications in visualization and the exploration of 3-D locations. The session speakers will focus on recent technical breakthroughs, promising applications, and remaining challenges and opportunities in these areas.

Hood
4:15–4:30  Break  

4:30–5:30

Closing Plenary Session Kodiak 
 

Making Data Useful: User-centric Approaches to Data

Keynote speaker: Clay Shirky, Distinguished Writer in Residence and Assistant Arts Professor, New York University

We are swimming in data. Every minute, YouTube sees two days' worth of video uploaded, Tumblr sees about 25,000 blog posts, and there are 2,000 check-ins to 4square. Yet most uses of data—big or small, point or range, stream or batch—are undertaken by organizations for organizations. Though we mortals have benefited from available data in services like search and mapping, most of the innovation in combinations and uses of data takes place far from where we are. As the role of data transforms our lives, how can we make that data useful to individuals, not just organizations?

5:30–7:00  Networking Reception