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2018 Dissertation Research Grant Funding (RE03)

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❶My thesis focuses on novel hierarchical approaches to improve the responses of neural chatbots. Get answers to frequently asked questions about the Microsoft Research Dissertation Grant.

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2015 Dissertation Grants
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However, there are restrictions for those in healthcare related fields, including dentistry, medicine, pharmacy, and veterinary medicine; please contact the DAAD New York office if your academic pursuits are in these fields. Applications accepted in November for month and short-term grants, and in May for short-term grants. The fellowship is for months, provides travel, health insurance and a monthly stipend of 1, Euros.

The fellowship lasts for months and provides travel, health insurance and a monthly stipend of 1, Euros. Candidates do not have to be U. The program offers support for graduate students, faculty, Ph. Scholars in the social sciences and humanities are eligible. Fellows can be doctoral students based at any academic institution in the United States and will be selected from a range of academic disciplines.

Applicants must be a U. Kim Foundation provides fellowships and grants to support graduate students and young scholars who are working in the history of science and technology in East Asia from the beginning of the 20th century, regardless of their nationality, origins, or gender.

Comparative studies of East Asia and the West as well as studies in related fields mathematics, medicine and public health are also welcome. The Beckman Center for the History of Chemistry at the Chemical Heritage Foundation, an independent research library in Philadelphia, accepts applications for short- and long-term fellowships in the history of science, technology, medicine, and industry.

Applications come from a wide range of disciplines across the humanities and social sciences. Awards are made in all fieds. Applicants must have a well-defined research, study or creative arts project that makes a stay in Scandinavia essential.

Preference is given to those candidates who draw on the library and archival resources of more than one partner. It is required that each fellow spend a minimum of 3 days per week in residence in the Lillian Goldman Reading Room using the archival and library resources. It is expected that applicants will have completed all requirements for the doctoral degree except for the dissertation.

DeKarman fellowships are open to students in any discipline, including international students, who are currently enrolled in a university or college located within the United States.

The fellowship is for one academic year and may not be renewed or postponed. Special consideration will be given to applicants in the Humanities. The one-month fellowship is offered annually, and is designed to provide access to Yale resources in LGBT Studies for scholars who live outside the greater New Haven area.

Graduate students conducting dissertation research, independent scholars, and all faculty are invited to apply. The fellowship must take place between September and April. Since every research project is different, Microsoft is not prescribing how the funds should be spent, but expects to see requests for equipment or data set purchases, compensation for experimental participants, travel for collecting or presenting research results, or student stipends.

In addition to receiving their grant, awardees will receive additional travel support to attend a two-day mentoring workshop in the autumn at the Microsoft Research Redmond Lab. Recipients will also present a talk describing their dissertation research and will receive feedback on their work from a panel of Microsoft researchers. Attending the Dissertation Grant Workshop is an incredible learning and networking opportunity for the winners. Winners will be notified by June Detailed information about the award, as well as the application form, can be found on the official website for the Microsoft Research Dissertation Grant.

I am excited about this opportunity to recognize and support technical innovation by students from under-represented groups; increasing the pipeline of diverse student talent is an important step toward growing a strong and diverse computing workforce. Artificial intelligence, Computer vision, Human-computer interaction, Medical, health and genomics, Programming languages and software engineering. Research shows that diverse teams are more productive teams.

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.

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. 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.

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.

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. 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.

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. 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. There has been a renewed focus on dialog systems, including non-task driven conversational agents i. 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. 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. 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. 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 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.

Ebuka Arinze Johns Hopkins University. 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.

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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, to fund their dissertation work.

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Dissertation Grantees may not accept concurrent grant or fellowship awards from another agency, foundation, institution or the like for the same dissertation project that .

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Accepted dissertation proposals may be awarded up to $5, in grant funding, with the possibility of renewal, as well as additional resources to expand doctoral candidates’ educational opportunities and career development. • Investigate federal funding sources. For most students, it might seem like a long shot, but your dissertation research might be a good fit to the research interests of an institute of the National Institutes of Health, which offers two grant programs for students.

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Dissertation Grant. MNRS Dissertation: Founders’ Circle Endowment Fund Grant. The Midwest Nursing Research Society Foundation is pleased to offer a Founders’ Circle Endowment Fund Grant of up to $2, moiprods.tk purpose of the dissertation grant is to encourage dissertation research that advances nursing science and practice. The Microsoft Research Dissertation Grant program provides research grants to PhD students who are members of groups under-represented in computing.