Funding can be used to support travel, field work, supplies, language training, and even living expenses. Check out and bookmark these 30 unique dissertation research fellowships for domestic and international doctoral students enrolled in U.
University of Washington Dissertation Funding for dissertation 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. 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. 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.
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.
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.
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.
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.
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. Moving forward, I aim to leverage this framework to build low-power privacy-preserving computational cameras with camera-level implementations of learned encoding functions.
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.Goizueta Foundation Graduate Fellowship Program aims to expand the scholarship of Cuban, American, Latin, hemispheric, and international studies by providing funding to doctoral students interested in using the resources available at the University of Miami Cuban Heritage Collection (CHC) for .
Available to post-field research graduate students these programs are typically aimed at providing dissertation writers with financial support for one year in order to . Procedures: Any doctoral candidate pursuing an advanced post-graduate degree (such as Ph.D.
or DBA) in auditing, accounting, or business from an accredited educational institution is eligible to apply for the doctoral dissertation grant. FINANCING YOUR RESEARCH Research grants for work on the senior thesis are available from a number of Harvard institutes and centers.
Application deadlines for these grants are usually well before Spring Break of the junior year, so you have to plan early. In addition to having a good idea of your thesis topic, many grants also require that you already have an advisor.
Dissertation Grants are available for advanced doctoral students and are intended to support the student while analyzing data and writing the doctoral dissertation.
Proposals are encouraged from the full range of education research fields and other fields and disciplines engaged in education-related research, including economics, political. Dissertation Grants: Doctoral students were eligible for dissertation grants of up to $20, for one year to support dissertation research and writing under the guidance of a faculty dissertation advisor.