Microsoft Productivity Research

Microsoft Productivity Research


Request for Proposals

Microsoft is committed to pushing the boundaries of technology to improve and positively influence all aspects of society. The cornerstone of how Microsoft does this is by building tools for personal and organizational productivity. New research, technologies, and services are creating opportunities for transforming productivity experiences in ways that were unimaginable just a few years ago.

The future of productivity is collaborative, intelligent, and deeply embedded in the world around us. The nature of productivity is fundamentally changing with the emergence of the intelligent cloud and edge, increasing use of digital media, and new devices that keep getting smarter year after year. No longer is it enough for the tools Microsoft builds to merely help people be faster, more efficient, and better organized. Our tools must now help people approach problems in new ways.

The goal of this RFP is to spark new research that will expand our understanding of productivity and fundamentally change the ways that people work and live. To help accomplish this goal, Microsoft intends to fund $1 million USD in new collaborative research efforts with university partners so that we can invent the future of productivity together.


Research is an integral part of the innovation loop. Some of the most exciting research is happening in universities around the world. The goal of the Microsoft Productivity Research (MPR) RFP is to establish new university partnerships to collaborate with Microsoft on novel research potentially leading to new capabilities for productivity technologies.

Proposals are invited on all areas of computing related to productivity in the following areas of interest:

Interaction and Sensing, e.g., 

  • New interaction paradigms for productivity beyond the desktop, including using computer vision and human-language technologies. Interactive task completion via natural language.
  • Conversational intelligence that augments existing interactions between people or enables new interaction between people and one or more virtual agents. Multi-agent interaction.
  • Support for the full task lifecycle, including the identification of task structure via human input and automation. The use of task structure to support microproductivity, mobile productivity, and cross-device task completion.

Machine Learning and Machine Teaching, e.g., 

  • Trustworthy and secure systems that learn from large datasets in privacy-preserving manners. Transfer learning for productivity that applies across people, teams, organizations, or tasks while limiting data leakage.
  • Machine teaching methods that enable people without machine learning expertise to teach a system to get better over time. Novel approaches to reward people for their contributions to the system.
  • Intelligible AI systems that are transparent in their purpose and how they arrived at specific decisions. Explainable AI.

Attention and Engagement, e.g., 

  • Emotional intelligence in productivity settings, including the ability to adapt to people’s emotional state. Support for focus and flow, including detecting and maintaining flow.
  • In-depth context understanding and applications thereof (e.g., situational understanding, reasoning, and action). Proactive contextual notifications and recommendations.
  • Methods for capturing people’s underlying intent, and metrics for measuring productivity that tie short term observable behavior to long term goals. The use of choice architectures to enable people to accomplish their goals.

Collaboration and Human Learning, e.g., 

  • Computer-supported lifelong learning, including contextual just-in-time training and social learning. Personal self-reflection and growth for people and teams, including the use of data to diagnose and address productivity challenges.
  • Approaches that explore the future of work via freelancing, distributed work, and dynamic teaming. Techniques that enable and enhance human-in-the-loop systems.
  • Systems and processes that support ethical and inclusive approaches to future productivity scenarios.

Monetary and other awards

Microsoft will provide up to $250,000 USD of funding and other resources for each proposal. Microsoft will provide a payment that has no restrictions on how it is used. A second round of funding pending initial progress and outcomes (see Timeline below) may be considered at some point during this collaboration. All funding decisions will be at the sole discretion of Microsoft. Proposals for this RFP should provide an initial budget and workplan for the research based on the Timeline section below.

Microsoft encourages potential university partners to consider using resources in the following manner:

  • PhD scholarship stipends.
  • Post-doctoral researcher funding at the university.
  • Software/hardware engineer funding at the university.
  • Travel and accommodation expenses of researchers and students visiting Microsoft (see below for details).
  • Limited but essential hardware and software needed to conduct the research.

Proposal plans should include any of these, or other items, that directly support the proposed research.

For those proposals requiring specialty hardware, Microsoft is making a limited number of Surface Hub 2S*, HoloLens 2S** and Azure Kinect*** devices available to support this research. Proposals seeking to integrate these devices should be explicit in how and why inclusion of these devices is necessary to accomplish the research goals and the value they will add to the anticipated research outcomes. Device requests should be limited to the lowest possible number of units required for accomplishing the research goals. The value of devices does not need to be accounted for in the budget section of the proposal.

Microsoft also encourages the strategic use of Microsoft technologies (APIs, specialty hardware, dataset, or SDKs) and open data sets when appropriate to the research questions being investigated. Researchers are invited to consider using productivity resources found in the Microsoft Researcher Tools Index and/or Microsoft Research Open Data Repository if applicable.

At the conclusion of the research collaboration, Microsoft will host each winning team at our offices for a week-long workshop. This will be a venue where research will be shared with relevant researchers and other teams inside of Microsoft.

  • Proposal budgets should include costs for 3-5 members of the university’s MPR team to travel to Redmond, Washington for five business days of meetings with Microsoft.

Microsoft research collaborators, at no cost to the winning teams, will visit the university partner one or more times to foster collaborative planning and research. These visits will be agreed upon and scheduled after an award decision is made. Likewise, a cadence of meetings will be mutually agreed upon at the start of the collaboration. Proposals are welcome to include other suggestions about how to foster an effective collaborative research engagement.


This RFP is not restricted to any one discipline or tailored to any particular methodology. Universities are welcome to submit cross-disciplinary proposals if that contributes to answering the proposed research question(s).

To be eligible for this RFP, your institution and proposal must meet the following requirements:

  • Institutions must have access to the knowledge, resources, and skills necessary to carry out the proposed research.
  • Institutions must be either an accredited or otherwise degree-granting university with non-profit status or a research organization with non-profit status.
  • Proposals that are incomplete or request funds more than the maximum award will be excluded from the selection process.
  • The proposal budget should reflect your university’s policies toward receiving unrestricted gifts and should emphasize allocation of funds toward completing the research proposed.
  • While we will accept multiple proposals from a single university, only one MPR unrestricted gift will be awarded to a single university. To optimize the chances of receiving an award, we encourage researchers from the same university to consider submitting a single, joint proposal (rather than multiple individual proposals) that benefits from their various skills and interests to create the strongest possible proposal.
  • Multiple universities can submit a joint/single proposal together. Please clearly indicate in the budget section how the budget, not to exceed $250,000 USD, will be shared.

Selection Process and Criteria

All proposals received by the submission deadline and in compliance with the eligibility criteria will be evaluated by a panel of subject-matter experts chosen from Microsoft. Drawing from evaluations by the review panel, Microsoft will select which proposals will receive the awards. Microsoft reserves the right to fund the winning proposal at an amount greater or lower than the amount requested, up to the stated maximum amount. Note: Microsoft will not provide individual feedback on proposals that are not funded.

All proposals will be evaluated based on the following criteria:

  • Addresses an important research area identified above that, if answered, has the potential to have a significant impact on that domain.
  • Expected value and potential impact of the research on productivity of people and organizations.
  • Potential for wide dissemination and use of knowledge, including specific plans for scholarly publications, public presentations, and white papers.
  • Ability to complete the project based upon adequate available resources, reasonable timelines, and the identified contributors’ qualifications.
  • Qualifications of the research team, including previous history of work in the area, successful completion of previous projects, research or teaching awards, and scholarly publications.
  • Diversity is highly valued and research teams should strive to reflect a diversity of backgrounds, experiences and talent.
  • Evidence of university support contributed in-kind to directly support and supplement the research efforts.
  • Budget is strategic to maximize impact of research.
  • Possible additional information as requested by the review panel, which might be requested via a conference call.


Proposals should submit a timeline (approximately 12-18 months) or workplan that begins in early 2020 and ends in summer of 2021.

  • October 16, 2019: Proposals due.
  • December 15, 2019: Winners announced.
  • Early 2020: Awards made and planning begins with regularly scheduled meetings, calls and visit(s) by Microsoft to MPR winning university.
  • Early 2021: Review of progress for second round of funding (pending progress and availability of funds).
  • Summer 2021: Report back, five-day meeting at Microsoft in Redmond, Washington.


  • As a condition of accepting an award, principal investigators agree that Microsoft may use their name and likeness to publicize their proposals (including all proposal content except detailed budget information) in connections with the promotion of the research awards in all media now known or later developed.
  • Researchers will be willing to engage with Microsoft about their project and experience, and provide updates via monthly or quarterly calls, as well as attend a workshop at Microsoft in Redmond, Washington. The workshop will likely be held in the summer of 2021.
  • The review process is an internal one and no review feedback will be given to submitters.
  • Microsoft encourages researchers to publish their work in scholarly venues such as journals and conferences. Researchers must provide Microsoft a copy of any work prior to publication. So long as accurate, such publications are not subject to Microsoft’s approval except that, at Microsoft’s request, researcher will delete any Microsoft Confidential Information identified or delay publication to enable Microsoft to file for appropriate intellectual property (IP) protection for any project IP disclosed in such work.
  • All data sets and any new IP resulting from this effort will be made public and publicly available for any researcher, developer or interested party to access to help further the goals of this initiative in providing higher quality and better access to technology services that empowers people and organizations to be more productive.
  • Funded researchers must seek approval of their institution’s review board for any work that involves human subjects.
  • At the completion of the project, the funded researchers will be required to submit to Microsoft a white paper that describes what was learned from this project.

Geographic Availability

*Surface Hub 2S will be available in the following countries. If you are applying from a country not included in this list, please do not include a request for Surface Hub 2S.

  • Australia
  • Austria
  • Belgium
  • Bulgaria
  • Canada
  • Croatia
  • Czech Republic
  • Denmark
  • Estonia
  • Finland
  • France
  • Germany
  • Greece

  • Hong Kong
  • Hungary
  • Ireland
  • Italy
  • Japan
  • Latvia
  • Lithuania
  • Luxembourg
  • Netherlands
  • New Zealand
  • Norway
  • Poland

  • Portugal
  • Qatar
  • Romania
  • Singapore
  • Slovakia
  • Slovenia
  • Spain
  • Sweden
  • Switzerland
  • United Arab Emirates
  • United Kingdom
  • United States

**HoloLens 2 will be available in the following countries. If you are applying from a country not included in this list, please do not include a request for HoloLens 2.

  • Australia
  • France
  • Germany
  • Ireland
  • New Zealand
  • United Kingdom
  • United States

***Kinect for Azure will be available in the following countries. If you are applying from a country not included in this list, please do not include a request for Kinect for Azure.

  • China
  • United States

Proposal Requirements

Collaborative Research Proposal Requirements

Proposals must be written in English and submitted through the Application Form. Proposals must be uploaded no later than 11:59 PM (Pacific Daylight Savings Time) on October 16, 2019. Questions should be sent to and must be received by October 2 in order to allow adequate time for response.

Microsoft shall have no obligation to maintain the confidentiality of any submitted proposals. Therefore, proposals should not contain information that is confidential, restricted, or sensitive. Proposals will be evaluated by a panel of subject-matter experts chosen from Microsoft. Microsoft reserves the right to make the winning proposals publicly available, except those portions containing budgetary information.

Length: The proposal should not be more than seven pages in length of Times New Roman 11-point font. Any documentation beyond that length will not be included as part of the proposal review.

The seven-page limit includes the cover page but the proposal can start on the cover page if additional space is needed. Scholarly references/bibliography can be submitted in addition to the seven pages and will not count toward the seven-page limit.

Cover page: The proposal should have a cover page that provides the following information:

  • Biographical information and contact information: This should include a brief description of any relevant prior research, publications, or other professional experience.
    • Faculty with deep technical experience related to the research areas described above are encouraged to apply. Indicate estimated level of effort/amount of time each faculty member will spend on the project.
    • Post-doctoral researchers and mid- to late-stage PhD students with deep technical experience related to the research should be included in proposals. Indicate the estimated level of effort/amount of time each post-doctoral researcher and PhD student will spend on the project.
  • Project proposal abstract: The abstract should contain the following:
    • A nontechnical description of the project that states the problem to be studied and explains the project’s broader significance and importance.
    • A technical description of the project that states the goals and scope of the research, and the methods and approaches to be used.

Proposal body:  The proposal body should include the following information.

  • Project description: Include what set of questions based on the identified research scenarios above, will be addressed and how they will be addressed. Describe how answering these questions will help advance the state-of-the-art in productivity research.
  • Approach: Describe the methodological and theoretical approach that the researchers will use. Explain exactly how the researchers will go about answering the question. Describe how the researchers will handle the legal and ethical challenges of doing work in this area. This section should also describe how the university MPR team proposes to work with Microsoft counterparts (researchers and engineers) to ensure an effective and positive collaboration.
  • Resources: Proposals should specify if and how Microsoft technologies will be used, namely (1) APIs, (2) Specialty devices including Surface Hub 2*, HoloLens 2**, Kinect for Azure***, (3) Data sets, etc. if applicable.
  • Expected results: Briefly describe what new knowledge is likely to be generated as a result of this research, why these results would be significant, and how this could benefit information workers of tomorrow.
  • Related research: Briefly summarize related research, including references where appropriate.
  • Researcher roles: Describe the role of each researcher involved in the project and explain how their skills and knowledge enable them to address the proposed research.
  • ~12-18-month Timeline/Workplan and Schedule: Describe what milestones will be used to measure progress of the project during the year and when they will be completed. If the project is part of a larger ongoing research program, estimate the time for completion of this project only. It is expected that the award will be made on or after January 1, 2020. Project timelines should reflect starting times on or shortly after this date.
  • Use of funds: Provide a budget (in U.S. dollars) describing how the award will be used. The budget should be presented as a table with the total budget request clearly indicated. Microsoft will consider requests for Azure credits necessary to conduct research. Value of Azure credits will not be considered a part of the budget request. Azure requests should be included in the budget table.
  • Other support: Include other contributions to this project (cash, goods, and services) by your university or other sources, if any, but do not include the use of university/organization facilities that are otherwise provided on an ongoing basis. Describe other grants or funded research that may be leveraged to add value to this research effort. Note: authors of the selected proposal will be required to submit an original letter on their institution’s letterhead, certifying the commitment of any additional or matching support described in the proposal.


Can multiple universities submit a joint/single proposal?

Yes, multiple universities can submit a joint/single proposal together. Please clearly indicate in the budget section how the budget, not to exceed $250,000 USD, will be shared.

If a proposal is submitted by more than one university, jointly, is it possible for Microsoft to pay each university directly or do we need to subcontract to each other?

Yes, Microsoft will pay each university directly provided the budget clearly illustrates the amount to be paid to each university with a total not to exceed $250,000 USD.

Are there constraints on how many applications can come from one university/department?

While we will accept multiple proposals from a single university, only one MPR unrestricted gift will be awarded to a single university. To optimize the chances of receiving an award, we encourage researchers from the same university to consider submitting a single, joint proposal (rather than multiple individual proposals) that benefits from their various skills and interests to create the strongest possible proposal.

How long can my proposed collaboration with Microsoft last?

Project timelines should be approximately 12-18 months. They should reflect the total time estimated to complete the research proposed.

Are proposals required to choose one of the research areas described in the RFP (Interaction and Sensing; Machine Learning and Machine Teaching; Attention and Engagement; Collaboration and Human Learning)?

Yes, proposals must indicate which of the listed research areas will be investigated as part of the proposed research to be eligible for consideration.

Is it a requirement or advisable to have a Microsoft champion who supports our proposal?

It would be considered a positive for the proposal to have a researcher in Microsoft who is supportive but we don’t require it or expect it. If a researcher in Microsoft is interested in expressing support for your proposal, they should send an email of support to with the university PI(s) on cc when the proposal is submitted.

Can proposal budget requests be less than $250,000 USD?

Yes, proposal budget requests can be of any amount up to $250,000 USD.

Does the budget table specified in the Proposal Requirements section count toward the seven-page limit?

The budget is part of the seven-page limit. Scholarly references/bibliography can be submitted in addition to the seven pages and will not count toward the seven-page limit but all of the other required components will count toward the seven-page limit.

If we are to include a letter of support from our university, would this count towards the seven-page limit?

No, letters of support will not count toward the seven-page limit.

Is it an issue if our cover page is more than one page if our proposal is still within the seven-page limit?

As long as the full proposal doesn’t exceed seven pages the rest of the section lengths are flexible.

Does the proposal need to include Microsoft technologies (APIs, specialty hardware, dataset, or SDKs), open data sets, or the productivity resources found in the Microsoft Researcher Tools Index and Microsoft Research Open Data Repository?

No, proposals are not required to or expected to include these resources. They are merely referenced as optional resources.

What is the maximum number of specialty devices that can be requested?

Applicants should request a limited number of specialty devices (low single digits) that are critical to answering the research questions in the proposal.

The Selection Process and Criteria identifies “Evidence of university support contributed in-kind to directly support and supplement the research efforts”. Is Microsoft looking for cost-share commitments, and if so, is the cost-share considered mandatory or voluntary per the terms of the award?

We would be looking for cost-share. This is not a mandatory requirement.

Will Microsoft consider indirect costs (since they are not allowed) evidence of university support?

We would be looking for contributions that directly support the research efforts here so indirect-costs that cover items such as facilities and infrastructure would not count toward university support/cost-share/in-kind contribution.

Is there a percentage or dollar amount that is expected or required as evidence of university support?

Since this is not a requirement, there is no expected amount.

Is the money considered a 'gift'? Are there conditions put on the funds?

The funds will be considered a gift that has no restrictions on how it is used.

Can the grant money can be used by the receiving institutions freely, e.g. to pay an expert postdoc affiliated to one of the participating universities, however, residing in a different country during the project?

There are no restrictions on how the funds are used. We do request that how the funds will be used is clearly illustrated in the required budget portion of the proposal.

Can funds be used to cover costs for Master’s students?

There are no restrictions on how the funds are used. We do request that how the funds will be used is clearly illustrated in the required budget portion of the proposal.

Are overhead and indirect costs allowable in the budget?

The proposal budget should reflect your university’s policies toward receiving unrestricted gifts and should emphasize allocation of funds toward completing the research proposed.

Is it possible to budget for some of the PI's time as part of the Microsoft Productivity Research Award?

As unrestricted gifts, it will be entirely up to the winners to decide how to spend the award to achieve the research goals in the proposal.

To further improve and facilitate our research it would be beneficial to use as many videos as possible. Would it be possible to access Microsoft's internal corpus of Skype conversations?

I’m afraid we will not be able to provide access to any data that is not already publicly available.

Can the data and the results of the project be used for future research by the authors, as it is common in the context of commercial research grants?

Yes, the results of this research are meant to be open and public for unrestricted use by future researchers and technologists.

We plan to have one mid-stage PhD student and three professors in the proposal. Is it advisable to have an additional professor, PhD student, or postdoc in our university MPR team?

You are encouraged to assemble a team that is most likely to achieve the greatest results within the time and budget parameters required.

How implementation-centered should the planned research be? Is it also valuable for Microsoft to i) receive insights on how people work together using their current technology leading to implications for the design of their future tools or should be ii) more focus on creating tool prototypes, per se?

Both of these scenarios are valuable. The results of this research will be open and public and so they are meant to drive future research and technology development. More insight on how people work together leading to implications for designs of future tools – though not designed just by Microsoft but others as well that are working in these topic areas would be of interest. However, if you feel you can develop breakthrough prototypes that also inform future research then that would also be interesting.


2019 Microsoft Productivity Research Collaboration Winners

2020 Microsoft Productivity Research Collaboration winners: Mirjam Augstein and Thomas NeumayrMirjam Augstein and Thomas Neumayr

University of Applied Sciences Upper Austria

Microsoft lead collaborator: Sean Rintel

Supporting Hybrid Collaboration for the Teams of Tomorrow

Abstract: Tomorrow’s information workers are increasingly employed in flexible work settings and oftentimes come upon situations where they engage in hybrid meetings and hybrid collaboration. Although such situations, with their dynamic interplay between co-located and remote collaborators, are increasingly supported by software and hardware tools, there are still significant research gaps related to the description and analysis of such settings (which would also allow for more targeted tool support). Thus, the full potential of existing tools such as the Microsoft Surface Hub with its software solutions for co-located (e.g., Shared Whiteboard) or remote (audio and video conferencing) collaboration in the collaborative settings of the future is not yet fully exploited and requires in-depth conceptual as well as technological research. The envisioned research endeavor includes 1) thorough grounding work on a descriptive framework for hybrid collaboration, a small part of which already exists and was published at ACM CSCW (receiving a best paper award) and 2) technical work on a software prototype for the support of hybrid meetings and in-depth (on-the-fly as well as post-hoc) analysis functionalities based on Microsoft hardware and software tools and APIs. To draw conclusions, we will conduct an extensive qualitative user study.

2020 Microsoft Productivity Research Winner: Margaret BeierMargaret Beier

Rice University

Microsoft lead collaborator: Mary Czerwinski

TeamDNA: Productivity-enhancing Tools for Diverse and Distributed Teams

Abstract: In this project, we are inspired by the question: what are the fundamental elements that impact team productivity?, for example, what constitutes the “DNA of team productivity?” Due to the necessity and prevalence of teamwork in the workplace, cracking this code and building better teams has the potential to significantly improve workplace productivity and also the teamwork experience. As one would expect, the topic has been studied extensively, especially in Organizational Psychology. However, to date, most prior research has combined human observations with self-reported data, thereby resulting in high-level insights but not deployable systems. Thanks to advances in engineering and Microsoft platforms that enable the real-time tracking of team interactions, we have a unique and unprecedented opportunity to study and improve team processes.

2020 Microsoft Productivity Research Collaboration winner: Jim Hollan Jim Hollan

University of California – San Diego

Microsoft lead collaborator: Nathalie Riche

A Human-Centered Information Space

Abstract: For far too long we have conceived of thinking as something that happens exclusively in the head. Thinking happens in the world as well as the head. Thinking is a distributed, socially-situated activity that exploits the extraordinary facilities of language, media, and embodied interaction with the world. With computers becoming ubiquitous and intertwined with every sphere of life, today we increasingly think with computers. This is accelerated by a radically changing cost structure in which the cost to use a thousand computers for a second or day is not appreciably more than to use one computer for a thousand days or seconds. Yet with all the advances in capacity, speed, and connectivity, using computers too often remains difficult, awkward, and frustrating. Even after six decades of design evolution, there is little of the naturalness, spontaneity, and contextual sensitivity required for convivial interaction with information. We argue that this is a result of a legacy document and application-centered design paradigm that presupposes information is static and disconnected from the context of processes, tasks, and personal histories. We propose a new human-centered view of information: as dynamic entities whose representation and behavior are designed in accordance with the cognitive requirements of human activity.

2020 Microsoft Productivity Research Collaboration winners: Chris North and Doug BowmanChris North and Doug Bowman

Virginia Tech

Microsoft lead collaborators: Rich Stoakley, March Rogers

Evaluating Physical and Virtual Large Displays for Windows Productivity Beyond the Desktop

Abstract: The fundamental space limitations of small display monitors pose significant problems for information workers’ productivity. The increased availability of low-cost, large physical displays and the coming feasibility of virtual displays (viewed through AR and VR headsets) will open fundamentally new user interface opportunities. However, little is known about the value of these modalities for desktop use, the trade-offs between physical and virtual displays, and how to best exploit them for productivity tasks. Our goal is to collect empirical data that will inform the design of future productivity hardware and software, such as Microsoft Windows and Office. These results could help to free users from the confines of current desktop environments, and lead to the next major revolution in increased productivity.

Research engagement results: The partnership with Virginia Tech explored how virtual monitors can be used to improve productivity through augmented reality. By looking at range scenarios – from mobile knowledge work to working from home to low vision contexts – the team found ways to expand beyond small screen and monitors that occupy physical space. These videos give an example of the immense flexibility virtual monitors can bring regardless of how much display space is needed:

2020 Microsoft Productivity Research Collaboration winner: Dragomir RadevDragomir Radev

Yale University

Microsoft lead collaborator: Ahmed Hassan Awadallah

Improving Employee Productivity Using Text Summarization

Abstract: Company employees spend a large fraction of their time reading text documents such as company policies, technical manuals, patents, research papers, industry news articles, and email, among others. Reading text takes time that can be used for other work-related activities or for enjoying more leisure time. We are proposing to improve employee productivity, both during onboarding and throughout their entire careers, through automatic text summarization techniques. We will develop a generic, state of the art library, named SummerTime, that will be used on summarization tasks, such as single-document and multi-document summarization, query-based summarization, text simplification, and text re-targeting. The code base will be flexible enough to allow the introduction of new techniques, data sets, and evaluation metrics. We will also implement a number of classic and recent neural algorithms and also improve the state of the art using transfer learning and several novel neural architectures.

Research engagement results: The project with Yale focused on improving employee productivity via dialogue summarization. Dialogue summarization has become increasingly important since the COVID-19 pandemic given the growth in video conferencing, and the team created a benchmark to measure how well current models perform in the dialogue domain. Their results can enable meeting summarization models to help new employees’ onboarding process or new student’s learning process by providing a concise summary of meeting or class interactions.

  • QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (NAACL 2021). Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu and Dragomir Radev. Accepted by NAACL 2021. [GitHub, video, poster]
  • An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (submitted to EMNLP 2021). Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah and Dragomir Radev.

The team also developed a new, wide-coverage summarization library named SummerTime. SummerTime targets non-expert users to expand access for state-of-the-art summarization models to a wider range of people. Users of the library do not need an NLP background and functionality is provided to help identify the best model to use for a particular case, including visualization, automatic model selection, and automatic model assembly.

  • SummerTime: Text Summarization Toolkit for Non-experts (submitted to EMNLP 2021, Demo Track). Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma, Troy Feng,Yusen Zhang, Tao Yu,Ahmed Hassan Awadallah, Dragomir Radev. [GitHub repository]

For more about this project, contact: Ahmed H. Awadallah (hassanam)

2020 Microsoft Productivity Research Collaboration winner: Akane SanoAkane Sano

Rice University

Microsoft lead collaborator: Mary Czerwinski

Unobtrusive Personalized Work Engagement Assistant

Abstract: Work engagement and workload/task management are an important aspect of achieving successful and productive everyday information work missions. However, work tasks/schedule and strategies to promote work engagement and well-being could vary from person to person. It is hard to adapt one strategy to all workers. In this proposal, we examine the hypothesis that multi-modal ubiquitous sensors and AI technologies help design a personalized work engagement assistant to suggest personalized productivity management strategies and provide unobtrusive personalized feedback to enhance work engagement and well-being. The aim of the proposed work is to develop and validate an unobtrusive personalized closed-loop system to measure work engagement and workload, and provide personalized real time feedback including work engagement management assistant and subtle sensory feedback based on the user’s physiological and behavioral data. We are focused on the development of unobtrusive and practical technologies, and selected the optimal sets of tools and mechanisms based on our team’s interdisciplinary work: ubiquitous and effective sensing and computing, computational imagining, machine learning, organizational psychology and human computer interaction.

2020 Microsoft Productivity Research Collaboration winner: Cyrus ShahabiCyrus Shahabi

University of Southern California

Microsoft lead collaborator: John Krumm

Privacy-Preserving Machine Learning Techniques for Improving Individual and Organizational Productivity

Abstract: Studying patterns of human activity (e.g., moving behaviors, daily routines, organizational workflows) can significantly improve productivity. Neural networks are a powerful tool to capture such patterns, but they need large amounts of individual data (e.g., location data) to train on, which raises significant privacy concerns. This project will design and implement differentially-private techniques to train neural networks. We will focus on skip-grams, which are suitable for sparse data, especially when used in conjunction with negative sampling. We will design algorithms that can build accurate models for human activity patterns, even under strict privacy constraints. We will also study privacy budget allocation strategies across different stages of the model, and we will perform tuning of model hyper-parameters to improve accuracy and performance.

Research engagement results: The collaboration with USC consisted of three different workstreams.

The first effort created:

  • A density-aware technique for publication of OD matrices in a differential private way,
  • An extension of the OD concept to multiple dimensions that allows one to quantify privately the frequency of certain trajectory segments of interest, and
  • An ML-based technique for accurately answering differentially private range count queries on geospatial data. All techniques were extensively tested using large-scale real datasets, and significantly outperformed all existing state of the art approaches.

The second parallel effort identified a new problem of quantifying the intrinsic value of information of trajectories, including a technique for quantifying the intrinsic VOI of trajectories:

The third effort worked on evaluating a methodology for different diseases and transmission models and quantifying the impact of sampling bias. The team considered the spread of SARS and the flu, in addition to work on COVID-19.  Results showed the robustness of the method to bias in observed trajectories.

For more about this project, contact: John Krumm (jckrumm)