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

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We use only trusted providers of online transactions: Be in the know! Easily connect with your writer to exchange files and share ideas. Your paper will be completed by the specified deadline. 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. Diversity, particularly in the area of computing research, means including unique perspectives that otherwise might not have a voice, fueling innovation.

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

Microsoft sends the payment directly to the university, which then disperses funds according to its guidelines. 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. If you accept a Microsoft Research Dissertation Grant, you may receive another fellowship from another company or institution during the same academic period.

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.

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.

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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. writing a good college admissions essay length Buy A Doctoral Dissertation Grant Nsf gps research papers lord of the flies thesis.