Dissertation Grant Program

Dissertation Grant Program

About

Microsoft recognizes the value of diversity in computing. The Microsoft Research Dissertation Grant aims to increase the pipeline of diverse talent receiving advanced degrees in computing-related fields by providing a research funding opportunity for doctoral students from groups under-represented in computing (women, African-Americans/Blacks, Hispanics/Latinos, American Indians/Alaskan Natives, Native Hawaiians/Pacific Islanders, and/or people with disabilities).

Provisions of the award

  • The 2017 Microsoft Research Dissertation Grant recipients will receive funding up to 20,000 USD for academic year 2017–18 to help them complete research as part of their doctoral thesis work.
  • Microsoft will arrange and pay for travel and accommodations to grant recipients to attend a two-day Microsoft Research workshop in Redmond, Washington, on November 13–14, 2017.
    • The workshop will provide grant recipients an opportunity to present their research, meet individually with Microsoft researchers in their research area and receive career coaching from Microsoft researchers.

Eligibility criteria

  • PhD students must be enrolled at a university in the United States or Canada and doing dissertation work that relates to computing topics in which Microsoft Research has expertise (click on Research Areas at the top of the page for a full list).
  • PhD students must be in their fourth year or beyond in a PhD program when they apply for this grant. The student must continue to be enrolled at the university in the autumn of 2017. Funding is for use only during their time in the PhD program; it cannot be used for support in a role past graduation, such as a postdoc or faculty position. The applicant will need to confirm their PhD program starting month and year, as well as their expected graduation month and year.
  • Payment of the grant, as described above, will be made directly to the grant recipient’s university and dispersed according to the university’s policies.
  • Applicants must attest that they self-identify with at least one group under-represented in computing. This includes: women, African-American/Black, Hispanic/Latino, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, and/or people with disabilities.

Apply

How to apply

The 2017 Microsoft Research Dissertation Grant application period is now closed. Please check back next year for information on our 2018 program.

  • PhD students must apply directly for the grant.
  • Applications must include: a curriculum vitae; thesis topic description (maximum two pages, font no smaller than 10 point); description of how the grant would be used, including a budget (maximum one page); contact information for three references (at least one of which must be from your primary academic advisor/supervisor); and three to six names of Microsoft researchers, chosen for topical relevance, who you would agree to be paired with as a mentor. A list of researchers and research areas can be found on our people webpage.
    • Microsoft will provide instructions and request a reference letter from each of your three reference contacts separately on Monday, April 10. All three contacts must submit your reference letters by Monday, April 24 at 11:59 PM Pacific Time in order for your application to be considered. Due to the number of applications, we will not respond to questions asking if your references were submitted in time.
  • Funding can be requested to support items such as equipment, data, travel, and staff salary needed for research; the request is not limited to these examples.
  • Applications must be submitted via the online application tool in any of the following formats: Word document, text-only file, or PDF. Email or hard-copy applications will not be considered.
  • Applications submitted to Microsoft will not be returned. Microsoft cannot assume responsibility for the confidentiality of information in submitted applications. Therefore, applications should not contain information that is confidential, restricted, or sensitive.
  • Incomplete applications will not be considered.
  • Due to the volume of submissions, Microsoft Research cannot provide individual feedback on applications that do not receive grants.

FAQ

Get answers to frequently asked questions about the Microsoft Research Dissertation Grant.

Eligibility criteria

Are international students (those who are not citizens of the United States or Canada) eligible to apply?

Yes, if you are an international student attending a school in the United States or Canada and meet the eligibility requirements.

What if I'm a student attending a university outside the United States or Canada?

This program includes only schools in the United States and Canada. If you are a student attending a school outside the United States and Canada, you are not eligible for this grant.

What if I will be completing my PhD before the autumn of 2017?

Students must still be enrolled in their PhD program during the autumn of 2017 in order to receive and use the grant. Grants are for completing dissertation research only, and cannot be used for support in a role past graduation, such as a postdoc or faculty position.

What if I am not in my fourth year or beyond during the application period?

Students must be in their fourth year or beyond in a PhD program when they apply for this grant. Students must have started their PhD in September 2013 or earlier to be considered to be in their fourth year of the program for this year’s application process.

Grant review process

How will applications be judged?

Reviewers will rate applications based on the technical/scientific quality and the potential impact of the proposed research.

Who will review the applications?

Applications will be reviewed by researchers from Microsoft Research with appropriate topical expertise. The three to six Microsoft researchers who the applicant referenced as people they would agree to be paired with as a mentor could be among those asked to review that applicant’s submission.

When will I know the outcome of the review process?

Selected grant applicants will receive notification no later than Friday, June 30, 2017. Due to the volume of submissions, Microsoft Research cannot provide individual feedback on applications that do not receive research grants.

Grant award details

If selected, when will I receive the grant funding?

Persons awarded a Microsoft Research Dissertation Grant in June will receive their financial award in July or August of that year. Microsoft sends the payment directly to the university, which then disperses funds according to its guidelines.

Do I need to include university “overhead” charges in my grant budget?

No. This award will be provided as an unrestricted gift with no terms and restrictions applied to it. No portion of these funds should be applied to overhead or other indirect costs.

Are there any tax implications for me if I receive this research grant?

The tax implications for the research grant are based on the policy at the university.

Will intellectual property be an issue if I am awarded a research grant?

The Microsoft Research Dissertation Grant is not subject to any intellectual property (IP) restrictions.

Grant Recipients

Ebuka ArinzeEbuka Arinze

Johns Hopkins University

Dissertation Title: Nanoengineering for Tunable Energy-Efficient Optoelectronics

Colloidal nanomaterials, such as semiconductor quantum dots, are of interest for various optoelectronic applications due to their size-tunable optical properties, distinctive electronic structure, and low-cost fabrication. Color-tuned and semi-transparent photovoltaics, devices with controlled and tunable reflection and transmission spectra, are of significant interest due to their potential applications in building-integrated photovoltaics, vehicular heat and power management, and multijunction photovoltaics. High-performance computing technologies coupled with advanced optimization methods have made it possible to rapidly and efficiently design and predict new device structures without having to rely on costly, time- and resource-intensive “trial-and-error” lab-based experiments in the field of optoelectronics. My project focuses on using nanoengineering techniques, including multi-objective optimization algorithms, plasmonic nanoparticle enhancements, and hybrid-materials-based surface modifications, to design and build colloidal quantum dot-based devices with controlled optical and electrical properties for the next generation of inexpensive and ubiquitous light harvesting, detection, and emission technologies.


Juan Camilo Gamboa HigueraJuan Camilo Gamboa Higuera

McGill University

Dissertation Title: Transfer of Robot Motor Behaviors from Low-Fidelity Domains

I’ve been working on algorithms for synthesizing controllers for a six-legged underwater autonomous vehicle, to perform a variety of navigation and pose control tasks. These algorithms allow us to specify data collection tasks, e.g. coral reef monitoring, from high level objectives encoded as numerical cost functions. To reduce the amount of data needed for each task, and since models of underwater dynamics are computationally expensive, we use model-based reinforcement learning techniques where the models are data-driven. A problem with these approaches is that, even if they are data efficient, collecting new data is expensive. I’m investigating techniques that mitigate this cost by re-using prior knowledge, from simulation or similar environments. Our current  approach, which we call policy adjustments, allows us to transfer previously learned controllers by reasoning about the discrepancies between the source of the knowledge (a simulator) and the deployment environment (a physical robot in the ocean).


Esha GhoshEsha Ghosh

Brown University

Dissertation Title: Efficient, Privacy-Preserving, Secure Cloud Computation and Storage

Adopting cloud services to reduce operational, maintenance and storage costs, is becoming increasingly common. However, outsourcing data and computations, is opening up new challenges in terms of integrity and privacy of the data and the computations on them. Along with such important security and privacy concerns, availability, and scalability are major factors in such settings. My thesis addresses various problems in this space of secure storage and computation outsourcing. In summary, the main contributions of my thesis are the following.

  1. Designing models and protocols for outsourced queries on structured dynamic data with efficiency, integrity and privacy guarantees along with prototype implementations.
  2. Designing efficient (general) verifiable computation primitives for data-intense applications along with prototype implementations.
  3. Developing an expressive framework for efficient graph queries on encrypted networks along with prototype implementations.
  4. Designing efficient protocols to facilitate secure storage of encrypted data in the cloud while enabling deduplication.

Kavita KrishnaswamyKavita Krishnaswamy

University of Maryland, Baltimore County

Dissertation Title: Smart Algorithms via Knowledge Management of Safe Physical Human-Robotic Care

The beginning of a new era in safe assistive robotics will occur when people with disabilities and seniors let intelligent software control a mobile robotic manipulator to safely reposition their body and limbs. Our goal is to explore the intersection between providing physical care and robotics, and how it is possible to translate safe patient handling and mobility guidelines into smart human-robotic interaction (HRI) algorithms. For a mobile manipulator with knowledge-managed algorithms. we propose to create an accessible low fidelity 3D Web interface for manipulating a high degree-of-freedom robot to safely reposition the human body and limbs. Our efforts seek to standardize protocols and regulations for how artificial intelligence agents related to physical HRI can achieve body and limb repositioning tasks. As assistive robotics become more mainstream, these best practices can improve safety in direct physical care in the process of repositioning the human body with a mobile robotic arm.


Himabindu LakkarajuHimabindu Lakkaraju

Stanford University

Dissertation Title: Interpretable Machine Learning for Human Decision Making

My research primarily focuses on exploring how machine learning can help improve real world decision making in domains such as health care and criminal justice. To this end, my thesis addresses various challenges involved in developing and evaluating interpretable machine learning frameworks which can complement and provide insights into human decision making. More specifically, my thesis focuses on the following diverse yet related research directions: developing frameworks which can be used to compare the effectiveness of algorithmic and human decision making, building models for obtaining interpretable and diagnostic insights into the patterns of mistakes made by human decision makers, learning accurate and interpretable models (or approximations to existing machine learning models) which can complement human decision making. The main contribution of my thesis is to address these problems under realistic assumptions which hold in real world decision making such as presence of unmeasured confounders and limited availability of labeled data.


Paula MatePaula Mate

Indiana University, Bloomington

Dissertation Title: Examining the Implementation of the Health Information System in Mozambique: Understanding the Experiences of Health Care Workers with ICTs

My study examines the implementation of the health information system (HIS) in Mozambique and the roletechnologies play in educating health professionals for better delivery of care. Through a comprehensive examination of the HIS, from development to roll-out, I analyze the relationship between colonial and (post)colonial governmental top-down policies and compare them to the on-the-ground reality of using information and communications technology (ICTs) to provide health education given social, economic, and political realities in Mozambique. Part of the problem with studies of technologies in poor parts of the world is that they are often conducted by highly educated researchers and are conducted in English. However, majority of the population in poor nations does not speak English. Such studies become irrelevant to the life experiences of those being studied. I will disseminate findings from this study in Portuguese and English through talks and publications in U.S., Mozambique, and other international venues.


Martez Edward MottMartez Edward Mott

University of Washington

Dissertation Title: Accessible Touch Input for People with Motor Impairments

Touch-enabled devices such as smartphones, tablets, and interactive kiosks are some of the most pervasive technologies in the world today. As a result, touch has emerged as one of the most dominant forms of input for computing devices. Despite the overwhelming popularity of touch input, it presents significant accessibility challenges for millions of people with motor impairing conditions such as cerebral palsy, muscular dystrophy, and Parkinson’s disease. My dissertation research takes an ability-based design approach toward improving the accessibility of touch-enabled devices for people with motor impairments. I intend to create intelligent interaction techniques that allow people with motor impairments to touch in whichever ways are most comfortable and natural for them, and for the system to react as if it was touched precisely.


Shadi A. NoghabiShadi A. Noghabi

University of Illinois at Urbana-Champaign

Dissertation Title: Building Large-scale Production Systems for Latency-sensitive Applications

In this era of increased engagement with technology, many latency-sensitive applications processing large amounts of data have emerged. For example, we expect social networks to show hashtag trends within minutes, data from IoT to be processed within seconds, and online gaming to react within milliseconds. In all these diverse areas, handling large scale data in a real-time fashion is crucial. At scale, providing low latency becomes increasingly challenging with many complexities in distribution, scaling, fault-tolerance, and load-balancing. My research has focused on developing techniques that broadly explore these issues with particular attention to end-to-end latency and building massive-scale solutions. Most of my work is deployed in large-scale production systems with hundreds of millions of users. My research contributions span a wide range of frameworks including: Ambry (LinkedIn’s mainstream geo-distributed media store), Apache Samza (a stream processing engine used by LinkedIn, Uber, TripAdvisor, etc.), and Freeflow (a high-performance container networking solution).


John R. PorterJohn R. Porter

University of Washington

Dissertation Title: Understanding and Improving Real-World Video Game Accessibility

My dissertation work attends to the intersection of accessible human-computer interaction and video game design. Games continually grow more complex, pervasive, and significant in 21st century life. However, due to  inaccessibility, games are often actively disabling experiences for many gamers with impairments, systematically excluding them from full participation in an increasingly important activity. Therefore, my work proposes to understand the play experiences of gamers with impairments and offer novel design solutions for mitigating the accessibility barriers they face. My proposed investigations seek to understand how accessibility barriers manifest in mainstream games, to empower gamers with impairments to better navigate the landscape of game accessibility through novel information design, and to address underlying institutional concerns that perpetuate systemic accessibility issues in the game development industry through education interventions.


Andrew S. StampsAndrew S. Stamps

Mississippi State University

Dissertation Title: Applications of Heterodox Rendering Methods to Visualization

Information visualization is an illustrative method to depict data, and the structure of this data  is not necessarily known beforehand. The classic rendering via rasterization of visualization primitives tends to minimize extraneous details; every drawn pixel or glyph has a tight correspondence to the data on which it is based. A simple line chart for example.  It is thought that a more expressive or artistic rendering of data might harness additional insight through abstraction, or even an emotional connection. These expressive methods which I have classified as Heterodox Visualization (HV) methods, include non-photorealistic rendering (NPR), stylized rendering processes like pixelization, and other rendering approaches, like those that mimic natural media e.g. painting or sketching. To date there has been little systematic guidance covering how these HV methods could be applied to information visualization. My research will help determine, through experiment, which techniques pose a benefit to different types of visualizations.


Vasuki Narasimha SwamyVasuki Narasimha Swamy

University of California, Berkeley

Dissertation Title: Real-time Ultra-reliable Wireless Communication

My research focuses on designing wireless communication protocols for Internet-of-Things (IoT) applications that require low-latency and high-reliability. This can enable exciting new interactive and immersive applications such as exoskeletons, inter-vehicle communication for self-driving cars, robotics & factory automation, virtual & augmented reality, high-performance gaming, and the smart grid. I am developing wireless communication protocols that employ simultaneous relaying by all radios in the network. This allows us to overcome bad channels and guarantee the latency requirements. My early work dealt with understanding the fundamental limits of using cooperative communication for high-performance applications. Currently, I am exploring the key physical layer requirements that are needed to implement these protocols. I am modeling how synchronization and channel estimation impacts the performance of these protocols. Ultimately, understanding the fundamental limits of high-reliability and low-latency wireless will enable us to engineer exciting applications.


César TorresCésar Torres

University of California, Berkeley

Dissertation Title: Hybrid Aesthetics – A New Media Framework for the Computational Design of Creative Materials, Tools, and Practices within Digital Fabrication

Technology plays an important role in both constraining and guiding how users explore, express, and innovate in a variety of creative tasks. Practices are emerging which blend both physical and computational techniques and materials providing new opportunities to expand the aesthetic repertoire available to creative practitioners. This thesis contributes a framework for understanding how to create these hybrid elements and develop materials, tools, and practices that stimulate the imagination to explore a wider gamut of creative expressions. Through a series of design tools, the thesis introduces data structures that break constrictive digital modes of practice, conceptual framings for guiding aesthetic exploration, and design principles for the adoption, sharing, and teaching of hybrid techniques. This work serves as a bridge between art and technology and challenges the narrative of who can participate and use digital fabrication technologies to include traditional artists, designers, and the broader community of creative practitioners.

News

Dissertation Grant Winners Announced

The Microsoft Research Dissertation Grant program offers financial support to selected doctoral students from groups that are under-represented in the field of computing in the form of grants to complete their dissertations. The grants were announced today, so I sat down with Dr. Meredith (Merrie) Ringel Morris, chair of the Microsoft Research Dissertation Grant program and a Principal Researcher at Microsoft Research, to find out more about the recipients.

June 2017

New Dissertation Grant provides support to under-represented groups in computing

Microsoft Research is funding a new academic program, the Microsoft Research Dissertation Grant, offering selected doctoral students doing computing research at U.S. and Canadian universities up to US $20,000 to fund their dissertation work. This program is open to students currently under-represented in the technology sector, including women, people with disabilities, and people who are African-American/Black, Hispanic/Latino, American Indian/Alaskan Native, or Native Hawaiian/Pacific Islander, reflecting Microsoft’s commitment to growing the number of diverse students obtaining computing degrees.

March 2017