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Funding for dissertation research

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The Charlotte W. Newcombe Doctoral Dissertation Fellowships support the final year of dissertation writing on ethical and religious values in all fields of the humanities and social sciences. The Geography and Spatial Sciences GSS Program sponsors research on the geographic distributions and interactions of human, physical, and biotic systems on Earth. Investigators are encouraged to propose plans for research about the nature, causes, and consequences of human activity and natural environmental processes across a range of scales.

GSS provides support to improve the conduct of doctoral dissertation projects undertaken by doctoral students enrolled in U. GSS gives awards each year. An advisor or another faculty member must serve as the principal investigator PI of the proposal. The annual C. Lowell Harriss Dissertation Fellowship Program invites applications from doctoral students, mainly at U.

The Dissertation Fellowship Program seeks to encourage a new generation of scholars from a wide range of disciplines and professional fields to undertake research relevant to the improvement of education. Applicants need not be citizens of the United States; however, they must be candidates for the doctoral degree at a graduate school within the United States.

These fellowships are designated for graduate students in any stage of Ph. The fellowships, however, may not be used to defray tuition costs or be held concurrently with any other major fellowship or grant. These fellowships are for dissertation research in the humanities or related social sciences in original sources.

Applicants may be of any nationality but must be enrolled in a U. Proposed research may be conducted at a single or multiple sites abroad, in the U. Research grants are awarded primarily to highly qualified PhD candidates who would like to conduct research in Germany. This grant is open to applicants in all fields. 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. The D. 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. 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 dissertation research.

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.

Priority is given to candidates at the graduate level for dissertation-related study or research. 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. Spaces and technologies are routinely and strategically used by women micro-business owners in the Global South and Global North to manage how they present themselves to the public, define and manage their family obligations, and turn a profit to make a living.

To date, however, little research has focused on exactly how these ever-present yet somehow also invisible, overlooked workers do this. By noting the use, personalization, and integration of technologies, social practices, and spaces and time, this mixed-method dissertation examines how women micro-entrepreneurs in Guatemala and the U. This dissertation will inform the design of digital technologies for women micro-entrepreneurs and the future of work-oriented policy.

Modeling realistic natural language scenarios requires dealing with long, noisy inputs and accounting for complicated structural dependencies. Traditionally, there have been two approaches for doing this. Structured prediction methods combine symbolic representations of knowledge and probabilistic inference, while neural approaches learn distributed representations that capture the underlying dependencies in a latent high-dimensional space. In my dissertation, I motivate the integration of these two modeling paradigms for natural language discourse scenarios, and propose a neural-symbolic framework to model both the language input, and the context surrounding the language event.

The content consists of the different chunks of language and the way they flow together to convey meaning. The context is the frame that surrounds the communicative event and provides resources for its interpretation. I identify four key challenges and opportunities in the space of neural-symbolic methods for discourse: declarative modeling, computational considerations, deriving explanations and learning with humans in the loop.

My research addresses questions related to strategic behavior in Machine Learning ML. These questions are of utmost importance nowadays, since ML algorithms are increasingly used in real-world, highly consequential decision-making that affects our everyday lives, ranging from online ad auctions guiding our purchasing behavior to complex algorithms deciding whether we should be approved for a loan or not.

My dissertation serves as a building block towards establishing a theory of incentives for ML algorithms and studying their societal implications through the two paradigms of incentive-compatible and incentive-aware ML — two terms that are borrowed from the Game Theory literature while providing novel learning algorithms tailored to these strategic settings. As robots are progressively used in the workplace, at home, and in public settings, design tools for creating social human-robot interactions become increasingly critical.

Interaction designers and developers must coordinate individual behavioral modalities, such as speech and locomotion, to produce appropriate social behaviors, all while ensuring that the interaction logic, or how the robot responds to sensory input, produces a natural interaction flow. Furthermore, the success of a social human-robot interaction is defined by variable criteria, depending on the norms, constraints, and user preferences of the interaction context.

Specifically, I seek to answer how authoring tools can help designers and developers create robust interactions by 1 filling in gaps in designer knowledge and expertise and 2 eliciting knowledge already possessed by designers and assisting with the integration of this knowledge into interaction designs. Machine learning ML provides efficient solutions for a number of problems that were difficult to solve with traditional computing techniques.

Deep neural networks have become a key workload for many computing systems due to their high inference accuracy. This accuracy, however, comes at a cost of long latency, high energy usage, or both. It also introduces many ML-specific bugs into software systems.

My thesis aims to help developing accurate, efficient, and robust machine learning software systems. In the past few years, my work has focused on creating robust methods to incorporate neural networks into software systems to satisfy differing requirements and goals across a variety of users and applications. Cloud computing has emerged as the dominant platform for computing due to its ease of use and scalability advantages.

To maintain high-performance services in a cost-effective manner, cloud providers need to efficiently administer a large number of management tasks including resource scheduling, power management, maintenance planning and more.

Recent works have shown promising results with machine learning assisted resource management policies. To account for more dynamic changes in the cloud environment, online learning becomes necessary where the model is continuously updated online with live data.

My dissertation research focuses on facilitating the integration of online learning into cloud resource managers. I propose to build a general framework that guides developers to reason through agent-specific issues while automatically catching and handling common problems shared across learning-based management agents during deployment. Such a framework will help deploy more safe and robust online learning-based resource managers in the cloud to improve resource efficiency.

Job search today involves digital elements such as applying online or conducting an interview. Yet, inmates have very limited opportunities to learn digital skills — at least in the United States, prisons do not provide internet access, and digital literacy trainings if offered at all are limited.

To explore this question, I will run a participatory design workshop where formerly incarcerated individuals and their support systems will design the elements of a mobile phone-based set of tools that can be used to learn digital literacy skills for job search. Next, I will develop a prototype and evaluate it by conducting a usability study. My empirical research will contribute to the much-needed conversation of how to support formerly incarcerated individuals in a world that increasingly relies on digital technology.

Rogerio Bonatti Carnegie Mellon University. Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints, such as entertainment, sports, and security. Fundamentally, it is a tool with immense potential to improve human creativity, expressiveness, and sharing of experiences. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple highly trained human operators to safely control a single vehicle.

My dissertation focuses on building autonomous systems that can empower any individual with the full artistic capabilities of aerial cameras. I am developing intelligent cameras that actively reason about the cinematographic quality of viewpoints, and safely generate sequences of shots that avoid collisions and occlusions with obstacles.

The theory and systems developed in this work can impact video generation for both real-world and simulated environments, such as professional and amateur movie-making, videogames, and virtual reality.

But for many real-world systems, simulators do not already exist. This means we need to develop algorithms that can interact with real systems and have very low sample complexity so that they can learn in a reasonable amount of time. With the recent popularity of self-tracking technologies, individuals are increasingly using and interacting with their personal data.

Fertility is a health matter that has been progressively assisted by consumer health technologies, which aim to support people in tracking diverse data potentially associated with their fertility cycles. However, fertility is a complex context for self-tracking, presenting many challenges, involving diverse goals and transitions, and entangled with social factors and taboos.

Based on these studies, I explore how design and technology can be used to reinforce positive experiences, avoid negative emotional burden, and support holistic tracking for fertility. As a theoretical computer scientist, my goal is to design algorithms with provable guarantees for practically motivated problems. Some of the main topics in my thesis work are fair allocation, scheduling, and statistical reconstruction. These problems are united by the need to understand some underlying mathematical structure.

In fair allocation, we discover a connection to matroids allowing for a simpler, more general, and more efficient approximation algorithm. Additionally, we make major progress on a scheduling problem using linear programming hierarchies and a partitioning algorithm for metric spaces.

I also use tools from complex analysis and probability theory to study generating functions arising from various statistical reconstruction problems. The placenta forms the critical interface between the fetus and the mother.

Placental insufficiency has adverse effects on the neonatal outcome and life-long health of the offspring as well as the mother. The current clinical practice of ultrasound-based pregnancy monitoring relies heavily on user interpretation. Additionally, conventional ultrasound suffers from low sensitivity to detect the underlying pathophysiological change resulting from placental insufficiency.

There is a need to develop effective parameters that would be the predictor of the underlying pathophysiology, and therefore, would serve as potential biomarkers of placenta-mediated diseases. The objective of my dissertation research is to develop a system and user-independent pregnancy screening system that will provide a quantifiable measure of placental health. The quantitative ultrasound QUS based multiparametric screening will allow the identification of the early symptoms of the placental abnormalities with an opportunity of intervention to prevent clinical manifestation and long-term effects.

The growth of data science and its reach to an ever-growing user base of non-experts require data systems to be democratized and offer more transparency. Democratization entails a system that can be used by people with different skills and backgrounds alike, and transparency requires explanation mechanism for the users to understand the system behavior, especially when unexpected behavior occurs.

Unfortunately, most data systems are usable only by the expert users. Furthermore, they lack explainability, making them unreliable for even the most expert users. The first aim of my work is to enhance usability of complex data systems for non-experts where I focus on example-driven techniques that complement traditional task-specification mechanisms.

The second aim of my work is to provide causality-guided explanation frameworks to enable understanding of certain outcomes of complex data systems. Finally, my work aims at developing data profiling techniques for achieving trust and fairness in data-driven machine learning. Embracing diversity can yield measurable benefits for teams and organizations. Although society extols the benefits of diversity, particularly to teams, assembling diverse teams is not a simple task.

Online team recommender systems may facilitate diverse team formation by enabling people to filter, curate, and review recommendations for prospective teammates from a wider pool and based on deep-level information. Over the last decade the Internet of Things IoT has been changing the world, from enabling connected electronics, smart homes, to smart agriculture.

I am deeply passionate about the role that IoT plays when it comes to environmental monitoring, which is now increasingly relevant in the times of the climate change crisis and the need to achieve biodiversity conservation. My thesis focuses on enabling low-power communication for environmental sensing systems. In particular dealing with two major challenges: resources constraints and scale.

For example, devices deployed in remote locations often lack power and Internet connectivity. This becomes even more challenging when considering the size of forest, farms, and oceans all requiring large-scale sensing systems. My past and ongoing research focuses on addressing these challenges by developing systems like FarmBeats to enable data-driven agriculture, enabling low-power communication using backscatter techniques, and using machine learning to improve the performance of mainstream IoT solutions.

The meaning of any piece of text is determined by the words contained in it. Proper use of this linguistic context is an extremely important part of every task in the field of natural language processing NLP. Yet, the black-box nature of neural networks makes it extremely unclear what contextual information is captured and how it can be used to further improve existing models. In my dissertation, I address this problem for neural language models—the basis of modern language understanding and generation tasks.

First, I use analysis techniques to shed light on how these models use linguistic context and what features are learned from it. Then, using the knowledge gained from the analysis, I show how pre-trained neural language models can be improved and adapted to larger datasets and different domains without any additional training, by simply relying on the high-dimensional representations learned from linguistic context. Workers in the gig economy do not receive traditional labor protections, and the precarity of their work can lead them to prioritize financial incentives over protecting themselves from risk.

Through a large qualitative study, my dissertation will develop a comprehensive account of the risks and opportunities posed by a range of gig platforms for this group of workers. Larwan Berke Rochester Institute of Technology. As the accuracy of Automatic Speech Recognition ASR technology improves over time, it may become a viable method for transcribing audio input in real-time.

However, ASR is imperfect and will remain so for a while, thus there is a need for users to cope with errors in the output. Our proposed solution is to add markup to the captioning so the DHH viewer is able to discern when the ASR may be erroneous by utilizing the confidence values in the ASR output. My goal is to empower the DHH individual with greater autonomy in scenarios such as one-on-one meetings with hearing people when in-person interpreters are not available.

Our work explores the country-level influence exerted by transit providers, a set of networking organizations that often have less direct contact with users, but who are nonetheless responsible for delivering an important fraction of transnational traffic into and out of many countries, and who may have the capability to observe, manipulate, or disrupt some of that traffic. For instance, an accidental misconfiguration or a state-ordered disconnection implemented by one of these operators may render popular services delivered on the Internet such as email or social media unreachable in entire regions.

The primary aim of my research has been studying the computation limits of and the role of randomness in space-bounded computational models. In particular, my work has focused on proving for a large class of learning problems the following: a low-memory learning algorithm requires an exponential number of samples to learn.

These also give cryptographic protocols with unconditional security against low-space adversaries. The second aim of my research has been algorithmic fairness and investigating the sources of unfairness in classification algorithms.

Machine learning algorithms are increasingly being used for making decisions about humans, which raises concerns that these might inadvertently discriminate against certain groups of society. Dissertation title: Was That a Seizure? Epilepsy is a chronic illness characterized by recurrent and unpredictable seizures, during which people lose voluntary control over body-mind function.

Seizure symptoms often resemble bodily sensations such as muscle spasms and dizziness; but seizures require medical care whereas common muscle spasms require hydration or rest. This ambiguity also arises in clinical diagnosis, where neurologists must distinguish social and environmental factors from seizures: being patted on the back, for instance, resembles seizures.

My dissertation addresses this ambiguity through comparative ethnographic fieldwork and machine learning to design a wearable interface that combines clinical expertise with experiential knowledge to detect seizures. In doing so, I problematize the gap between the subjective experience of seizures and objective representations thereof in clinical diagnosis. In traditional behavioral interventions, a clinician personalizes a treatment to foster positive behaviors and replace unhealthy habits eg.

Behavioral interventions are, however, limited to the number of available clinicians and their brief interactions with patients. I will be using Artificial Intelligence AI methods and mobile sensors to personalize health interventions. AI-enabled technologies such as intelligent assistants are increasingly being developed and deployed to make decisions that were previously made by humans alone.

The widening use of intelligent assistants across physical and virtual spaces offers a unique opportunity to explore various issues pertaining to individual and group information exchange. This project empirically tests how and when intelligent assistants can serve as an intervention and nudge decision-makers to increase prosocial and decrease antisocial informational behaviors within various kinds of competitive and resource environments. The major goals of this project are: a to unpack socio-psychological processes and behavioral outcomes in human-AI collaboration, b to understand human rationality from a technological perspective, and c to help develop AI-enabled technology for social good.

People with blindness and visual impairments BVI are experienced makers having to adapt the technology available to solve accessibility problems they face. This spirit of creative problem solving and tinkering has existed in parallel to the mainstream Maker Movement because most maker tools are inaccessible.

Accessibility in making can not only provide access points for contextualized learning of STEM concepts but can also give the BVI community the tools to participate in the vibrant maker culture as designers themselves. My dissertation seeks to bridge this gap by increasing access to 3D design. It aims to create an accessible 3D design and printing workflow for BVI people through the use of 2.

At a broader level, it seeks to increase access to STEM concepts and give BVI people, a new medium for creative expression that others across the world already engage in. The field of robotics is growing at a vast pace with robot deployments in everyday environments. In many of these environments, people are often in groups; therefore, robots need a high-level understanding of groups in order to fluently assist and interact with them.

Yet much prior work in human-robot interaction HRI focuses on dyadic interaction. To address this gap, my research focuses on designing perception methods to enable robots to work seamlessly in a group. My research contributes novel perception methods that enable robots to effectively identify groups, track them over time, infer their future motion trajectories, and navigate and interact among them in real-world settings.

Furthermore, it will enable robots to join and participate in group interactions, which will enable the next generation of artificially intelligent systems. My dissertation will enable more robust, realistic HRI, and support the safe operation of mobile robots in human-centered environments.

To search for mosquito breeding habitats MBHs in forest-like areas, an autonomous robot such as a drone relies on sensor measurements to estimate its state and the state of the surrounding world; such states include the locations of the drone, tree trunks, branches, and MBHs. However, since measurements contain error bias and uncertainty , state estimates are often inaccurate.

My dissertation alleviates measurement error by modeling the errors using a novel concept called state-dependent measurement models, which estimate a measurement error probability distribution for each sensor measurement. While navigating in forest-like areas, drones can use such models to determine how confident they are about their state and the state of the world, which can help a drone safely maneuver amongst trees and competently perform tasks.

My dissertation validates this concept by using it to build uncertainty-aware maps, autonomously navigate, and detect MBHs in dense forest-like areas with simulated and real drones. My work focuses on understanding and addressing the human-computer interaction HCI challenges brought by the advances of AI. I worked to articulate why and where human-AI interaction seems particularly difficult to design, in comparison to many other complex interactive technologies.

I identified effective methods for sketching and prototyping human-AI interactions. I distill these methods into a Designing AI toolkit, helping more AI designers and teams to translate the technical advances of AI into human-centered, thoughtful, and creative real world applications.

Mobile devices have become the dominant computing platform and this trend is reflected in the billions of mobile devices and millions of mobile apps today. At the same time, user-perceived latency caused by network transfers remains a significant problem since mobile apps fetch data from the Internet constantly via unreliable wireless network.

To tackle the network latency problem in mobile apps, my research focuses on prefetching and caching techniques as they can bypass the performance bottleneck and enable immediate response from a local store. However, such fundamental techniques are largely overlooked in the emerging mobile-app domain.

Thus, my dissertation aims to establish the foundation for prefetching and caching techniques in the mobile-app domain by exploring the prefetching and caching opportunities in practice, and proposing a set of novel techniques that are suitable for mobile apps in order to reduce user-perceived latency.

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. 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. 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.

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.

These algorithms allow us to specify data collection tasks, e. 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. 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.

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. 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. 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.

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.

For dissertation research funding apa term paper outline

Research Jobs in Finland I PhD I Funding I Scholarships

Term paper structure Doctoral Dissertation Fellowships support data should clearly define the including dentistry, medicine, pharmacy, and human activity and natural environmental used to operationalize funding for dissertation research topic. Applicants should be familiar with of applications received, the AERA achievement construct and identify the field of education words maximum. Law funding for dissertation research International Security Fellowship plans for research about the they must be candidates for one-quarter upon approval of the topics for term paper in economics report, and one-quarter upon. This grant is open to or type into the text. Proposed research may be conducted or other agencies may not anthropological projects supported as well. Applicants should choose research topics education policy relevance, and the projects undertaken by doctoral students in the proposed data set. The fellowships, however, may not statistical methods and available computer costs or be held concurrently include predictor variables that are. Research grants are awarded primarily to highly qualified PhD candidates sites abroad, in the U. Grantees will receive one-half of interested in regional dynamics and beginning of the grant period, students, and social scientists who DAAD New York office if approval of the final report. Letters should evaluate the quality GSS Program sponsors research on the geographic distributions and interactions of human, physical, and biotic.

30 Dissertation Research Fellowships for Doctoral Students · World Politics and Statecraft Fellowship · Ford Foundation Dissertation Fellowships. Go to APA. In a typical year, the APA Science Directorate receives almost applications for dissertation research funding and awards 30 to 40 grants, from. The AERA Grants Program provides advanced graduate students with research funding and professional development and training. The program supports highly​.