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 underrepresented in computing.
Check back early next year regarding the 2019 Microsoft Research Dissertation Grant program.
Provisions of the award
- The 2018 Microsoft Research Dissertation Grant recipients will receive funding up to 25,000 USD for academic year 2018–19 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, in the autumn of 2018.
- 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.
- 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 2018. 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.
Check back early next year regarding the 2019 Microsoft Research Dissertation Grant program.
How to apply
- PhD students must apply directly for the grant.
- Applications must include:
- Curriculum vitae
- Thesis topic description (maximum two pages including references, font no smaller than 10-point)
- Description of how the grant would be used, including a budget (maximum one page)
- Month and year you entered the PhD Program and your expected graduation date
- Primary area of research (click on Research Areas at the top of the page for a full list)
- Contact information for three references who are established researchers familiar with your research (at least one of which must be from your primary academic advisor/supervisor and only one letter can be from a current Microsoft employee). Microsoft will automatically provide instructions and request a reference letter from each of your three reference contacts separately as you submit your application. Those auto-generated emails will be sent from Microsoft CMT “email@example.com”, which may end up in your spam folder. References will be asked to attach a letter to your application in our tool. Note that all three contacts must submit your reference letters by Monday, April 16, 2018, 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. You will receive an auto-generated confirmation each time one of your references submits a letter.
- Three to six names and email addresses 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. Do not contact the researchers for the purpose of listing them as a potential mentor. Do not list researchers with a Corporate Vice President or Managing Director title.
- Funding can be requested to support items such as equipment, data, travel, tuition, 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.
Get answers to frequently asked questions about the Microsoft Research Dissertation Grant.
Check back early next year regarding the 2019 Microsoft Research Dissertation Grant program.
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 2018?
Students must still be enrolled in their PhD program during the autumn of 2018 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 2014 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, 2018. 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.
Can I simultaneously receive fellowships from other companies?
If you accept a Microsoft Research Dissertation Grant, you may receive another fellowship from another company or institution during the same academic period. However, if your tuition and/or stipend are being covered by a fellowship award, then you should not request tuition/stipend funds from the grant as part of your budget.
2018 Grant Recipients
University of Washington
Dissertation Title: Toward Disability-Informed Human-Centered Design
Experience shows us that people with disabilities can positively impact interaction design for everyone. However, publishers of interaction design rubrics–such as Human-Centered Design–have tended to focus on supporting the design process for people with disabilities, rather than by them. My research focuses on developing an inclusive toolkit that augments current Human-Centered Design activities to be accessible to people with disabilities. Drawing from this toolkit, I will offer new ways to connect disability with design, all based on the life experiences of people with disabilities.
Georgia Institute of Technology
Dissertation Title: Trust, Technology and Community Engagement
The work of community engagement performed by public officials in local government provides valuable opportunities for city residents to participate in governance. Technology stands to play an increasingly important role in mediating community engagement; however, the practices and relationships that constitute community engagement are currently understudied in human-computer interaction (HCI). Of particular importance is the role that trust plays in the success of community engagements—either establishing trust, or more frequently, overcoming distrust between public officials and city residents. To address this challenge, my research seeks to understand how trust could inform the design of technology to support the work of community engagement performed by public officials in local government. My research will culminate in a design framework that will inform development of technology for trust-based community engagement.
Ryan M. Corey
University of Illinois at Urbana-Champaign
Dissertation Title: Array Signal Processing for Augmented Listening
Augmented listening technologies, such as hearing aids, smart headphones, and audio augmented- reality platforms, promise to enhance human hearing by processing the sound we hear to reduce unwanted noise and improve understanding. State-of-the-art listening devices perform poorly, however, in noisy environments that have many competing sound sources. Large microphone arrays with dozens or hundreds of sensors could allow listening devices to separate, process, and enhance multiple sound sources in real time while sounding natural to the user. My dissertation addresses several unique challenges of array processing for real-time listening applications, such as tracking human movement, preserving the user’s spatial awareness, estimating the dynamics of multiple simultaneous sound sources, and maintaining an imperceptible input-to-output delay. I am also developing first-of-their-kind wearable microphone array prototypes and data sets to help other researchers develop ambitious new augmented listening algorithms and applications.
Carnegie Mellon University
Dissertation Title: Quantifying and Mitigating Risks of Algorithmic Decision Support
Machine learning is increasingly being used for decision support in critical settings, where predictions have potentially grave implications over human lives. Examples of such applications include child welfare, criminal justice, and healthcare. In these settings, the characteristics of available data and of deployment contexts give rise to challenges that have not been sufficiently addressed in the machine learning literature, including the presence of selective labels, unobservables, and the effects of omitted payoff bias. When left unaddressed, these challenges may lead to systemic biases, self-fulfilling prophecies, and loss of human trust in the systems. My research is focused on quantifying the performance and fairness risks of algorithmic learning in these settings, and on reducing these risks by developing novel algorithms.
Dissertation Title: Artistic Vision: Providing Context for Capture-Time Decisions
As cameras become smarter and more pervasive, more people want to learn to be better content creators. People are willing to invest in expensive cameras as a medium for their artistic expression, but few have easy ways to improve their skills. Inspired by critique sessions common in in-person art practice classes, my dissertation research focuses on designing new interfaces and interactions that help people become better photo takers. Using contextual in-camera feedback, users can capture photos and videos in a way that is more informed and intentional, while still allowing for their aesthetic and creative decisions.
University of Rochester
Dissertation Title: Computationally Efficient Modeling and Audio Enhancement Algorithms for Reverberant Acoustic Systems using Orthonormal Basis Functions
Highly interactive modeling methods and audio enhancement algorithms underlie the operation of modern acoustic systems. The capability of a system to produce lifelike acoustic experiences significantly depends on the accuracy and computational efficiency of the modeling and audio processing algorithms employed. Accordingly, my research has focused on the development of methods and algorithms that accurately model highly reverberant acoustic systems and process acoustic signals using as few parameters as possible. Such accurate yet computationally efficient modeling and processing algorithms are of essential interest in a wide variety of applications ranging from virtual acoustics to healthcare. My main contribution is the development of algorithms, which rely on orthonormal basis functions and time-frequency representation of an acoustic system, that provide high accuracy over a wide range of frequencies in real-time. As an early demonstration, I propose an efficient solution to adaptive feedback cancellation problems.
University of Florida
Dissertation Title: Privacy Preserving Computational Cameras
Major advances in computer vision and mobile technologies have set the stage for widespread deployment of connected cameras, spurring increased concerns about privacy and security. To address these concerns, I’m building novel computational cameras that perform privacy processing, at the camera level, via optical filtering of the incident light-field and/or sensor-level electronics, and developing a data-driven framework to learn privacy-preserving encoding functions through adversarial optimization. Moving forward, I aim to leverage this framework to build low-power privacy-preserving computational cameras with camera-level implementations of learned encoding functions.
Massachusetts Institute of Technology
Dissertation Title: Human-Guided Reinforcement Learning in Real-World Environments
Deploying AI systems safely in the real world is challenging. The rich and complex nature of the open world makes it difficult for machines trained on limited data to adapt and generalize well. The errors that can result from an imperfect model can be extremely costly (e.g., car accidents, incorrect diagnoses). My research focuses on using human feedback to help reinforcement learning agents better adapt to the real world, leading to safer deployment of these systems. This involves developing robust models that can accurately predict uncertainty in the world, use different forms of human input to learn, and adapt quickly in real-time to new changes in the environment. Developing such systems that learn from humans intelligently will move us closer towards more generalizable robots that perform a variety of tasks in such applications as assistive robotics, healthcare, and disaster response.
University of Pennsylvania
Dissertation Title: Hierarchical Approaches to Improve the Flow, Style, and Coherence of Conversational Agents
There has been a renewed focus on dialog systems, including non-task driven conversational agents (i.e. “chit-chat bots”). Dialog is a challenging problem since it spans multiple conversational turns. To further complicate the problem, there are many contextual cues and valid possible utterances. We propose that dialog is fundamentally a multiscale process, given that context is carried from previous utterances in the conversation. Neural dialog models, which are based on recurrent neural network (RNN) encoder-decoder sequence-to-sequence models, lack the ability to create temporal and stylistic coherence in conversations. My thesis focuses on novel hierarchical approaches to improve the responses of neural chatbots.
Mina Tahmasbi Arashloo
Dissertation Title: Programmable Network Monitoring and Control
Real-time and fine-grained network monitoring and control is crucial for operating networks that match the security and performance requirements of today’s online services. To that end, modern network devices offer programming interfaces for fine-grained specification of what information to maintain across packets, and how to process packets based on it. These interfaces, however, are quite low-level and suitable only for programming a single device, making them cumbersome to use in today’s large-scale networks. My thesis focuses on designing programming platforms that facilitate the use of programmable network devices for large-scale and real-time network monitoring and control. More specifically, these platforms consist of (i) domain-specific languages that are expressive enough for high-level specification of policies for end-to-end network transport, network-wide state-aware monitoring and control, and path-based network monitoring, and (ii) compilers that use efficient intermediate data structures to automatically distribute and implement these specifications on programmable network devices.
Dissertation Title: Methods in Interpretability and Causal Inference for Better Understanding of Machine Learning Models
I aim to develop methods to help users of machine learning models increase both the trust in and understanding of their models. My dissertation is in the two fields of interpretability and causal inference. The two fields, seemingly disparate, actually share the common goals of revealing and adjusting for biases that can arise when building machine learning models. In interpretability, I am developing methods to probe tree ensembles and audit black-box risk scoring models such as COMPAS. In causal inference, I have worked on methods that use machine learning to more flexibly estimate treatment effects from observational data. To complete my dissertation, I plan to probe the definition of interpretability — still a subject of debate in machine learning — by conducting a large-scale comparison of different models claimed to be interpretable and augment this quantitative evaluation with human subject experiments using domain experts.
2017 Grant Recipients
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 Higuera
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).
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.
- Designing models and protocols for outsourced queries on structured dynamic data with efficiency, integrity and privacy guarantees along with prototype implementations.
- Designing efficient (general) verifiable computation primitives for data-intense applications along with prototype implementations.
- Developing an expressive framework for efficient graph queries on encrypted networks along with prototype implementations.
- Designing efficient protocols to facilitate secure storage of encrypted data in the cloud while enabling deduplication.
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.
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.
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 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. 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. 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. 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 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.
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.