In response to exponentially increasing demand for digital media, today's Internet landscape has evolved into a multitude of diverse and interdependent distribution systems designed to move content as efficiently as possible. While many of these systems have \emph{individually} been explored in depth by both academic and industrial communities, a cross-sectional...
Assistive robotics focuses on human-robot systems that provide physical support and assistance to the elderly and people with motor-impairments. While assistive machines, such as the powered wheelchair, can significantly enhance the functional independence of individuals, many users are challenged by their direct operation, the manner in which such systems are...
Citizen media literacy is essential in a democratic society, particularly in the online environment where valid media sources have proliferated alongside purveyors of fake news. This dissertation explores technologies that automatically detect aspects of bias in news articles, with the ultimate aim of leveraging them to augment media literacy. It...
Historically, there have been large disparities in the degree to which different communities have access to resources and representation within society. With the increased availability of the internet and the growth of user-generated content platforms like Twitter and Wikipedia, there are opportunities to alleviate some these long-standing barriers to access...
The analytical paradigm in philosophy has as a pillar an analysis of the sentence as a basic entity. New sentences may be built from old sentences recursively through the application of logical constants, recently including intensional operators. Models are built on how these logical constants interact with each other, and...
Deep neural networks have shown impressive performance for many applications. In this dissertation, leveraging the capabilities of neural networks for modeling the non-linearity exists in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The suggested models can transfer knowledge from...
Many volunteer communities rely on technological systems to help their members connect, collaborate and learn the norms of how to participate in the organization. This dissertation presents research that examines technological interventions designed to support participation in three different volunteer-run communities, all of which have porous boundaries, and allow volunteers...
Neural networks have revolutionized the field of computer vision since they provide solutions to a number of previously unsolved problems and achieve promising performance both in terms of accuracy and computational efficiency. It has increasingly become recognized as providing high performance for applications as diverse as image classification, object detection,...
Super-resolution (SR) has become one of the most critical problems in image and video processing. In Chapter 2 of this thesis, a detailed review of existing Deep Learning (DL) techniques for addressing the SR task, with an emphasis on how DL and analytical techniques can be combined, is provided. Chapter...
Biological systems comprise diverse collections of cellular and non-cellular components with intricate relationships and dynamic interactions. To gain system-level understanding, we must be able to accurately model these systems, both experimentally and computationally. Agent-based models (ABMs) in particular are a uniquely intuitive, modular, and flexible framework capable of supporting multi-scale,...
The ever growing desire for accurate estimation and efficient learning necessitates the efforts to quantitatively characterize uncertainties for models. In this thesis, four problems pertaining to uncertainty quantification are discussed: A sequential stopping framework of constructing fixed-precision confidence regions is proposed for a class of multivariate simulation problems where variance...
Sound is one of the most important mediums to understand the environment around us. Identifying a sound event in prerecorded audio (such as a police siren, a dog bark, or a creaking door in soundscapes) leads to a better understanding of the context where the sound events occurred. To do...
In conventional data federations, a set of data providers each possess an autonomous database and collectively make the union of these databases available for querying by a client from a unified SQL interface. This setting however, provides no guarantees on data privacy or security. With my work, I consider a...
This dissertation asks how researchers can create more equitable algorithmic systems. Ultimately, this thesis explores methods and implications of representing subjects of analysis in the design and evaluation of algorithmic systems. I also unpack how algorithmic tools measure and quantify human behavior, giving heed to the potential impacts of these...
Computational imaging (CI) is a class of imaging systems that optimize both the opto-electronic hardware and computing software to achieve task-specific improvements. Machine/deep learning models have proven effective in drawing statistical priors from adequate datasets. Yet when designing computational models for CI problems, physics-based models derived from the image formation...
Volunteer-based physical crowdsourcing systems connect individuals to make unique contributions to solve local and communal problems and enable new services. A key challenge in enabling such systems is attracting enough willing volunteers who can make useful contributions to achieve desired system goals. While most volunteer-based systems provide volunteers flexibility to...
Algorithmically-driven social platforms present a challenge for self-presentation and identity management by obscuring audiences behind algorithmic mechanisms. Users are increasingly aware of this and actively adapting through folk theorization, but we do not know how users are coping with the constant change endemic to these platforms. We also do not...
In this thesis we study two problems, one in unsupervised learning - k-means clustering and the other in a supervised learning setting with the presence of adversarial perturbations. We do a beyond-worst case style analysis and show that in either case instances that are resilient to adversarial perturbations are also...
Commonsense inference is a critical capability of modern artificial intelligence (AI) systems. The machines need commonsense knowledge to perform tasks exactly like human being does. Learning commonsense inference from text has been a long standing challenge in the field of natural language processing due to reporting bias -- people do...
This dissertation explores the design and evaluation of augmentative and alternative communication (AAC) technologies for people with aphasia. Humans use speech and language to communicate their thoughts and opinions as well as express their individuality, autonomy and agency (George Armitage Miller 1951; Ahearn 2001). Speech and language are important tools...