From cyber theft of personal financial information to Advanced Persistent Threat (APT) attacks, nowadays endpoint devices suffer from various intrusions which cause inestimable property and privacy loss. To protect the security on endpoints, endpoint detection and response (EDR) systems have been developed to serve as the powerful solution against those...
Motivated by real-world problems in various fields, mechanism design governs the design of protocols for strategic agents and has applications both in computer science and economics. Due to the revelation principle – a seminal observation in mechanism design, a vast number of studies in mechanism design focus on revelation mechanisms...
Cardiovascular disease is the leading cause of death in US and non-invasive cardiac imaging has vital importance for early detection and diagnosis of heart disease. Cardiac Magnetic Resonance (CMR) is arguably the most versatile imaging modality and capable of a comprehensive evaluation of heart disease without ionization radiation. Despite the...
When first-year students begin college they are thrown into a new environment where they are expected to simultaneously perform academically, form new relationships, and become independent. Many students struggle with this transition; experiences of stress, anxiety, and depression are common. For the majority of residential college students this is their...
Location-aware technologies, such as personal navigation applications, location-based AR games, and artificial intelligence systems that learn from data about places, increasingly mediate our understanding of and interactions with the world. However, a number of risks associated with location-aware technologies have emerged, jeopardizing the welfare of its users. This dissertation seeks...
Data Science and related fields like Artificial Intelligence, Machine Learning, and Statistics provide indispensable research methods for understanding a wide variety of phenomena from large datasets. However, as methodical and empirical as these methods aim to be, there are many subjective and discretionary choices that the data scientist must make...
Our experience of the physical world is mediated by our senses, but while most people have five senses, interactions with computer systems are largely limited to the visual sense. When working with nonvisual artifacts, like sound, on computers, such artifacts are typically transformed, or re-encoded, into something visual. Determining how...
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...
Millions of people freelance in the growing online gig economy, making it important to advance pay equity and support freelancers in earning their livelihoods online. Compared to offline employment, freelancing introduces at least two challenges that threaten freelancers’ ability to secure work and the equitability of the gig economy: 1)...
Supervised learning model is one of the most fundamental machine learning models. It can provide powerful capability of prediction by learning complex patterns hidden in many, sometimes thousands, predictors. It can also be used as a building block of other machine learning tasks, like unsupervised learning and reinforcement learning. Such...
The language Esterel has found success in many safety-critical applications, from aircraft landing gear to digital signal processors. Its unique combination of powerful control operations, deterministic concurrency, and real time execution bounds are indispensable to programmer in these kinds of safety-critical domains. However these features lead to an interesting facet...
The world is awash in data and much of artificial intelligence focuses on learning models of the underlying structure in this data or the mechanisms governing its evolution. Both neural and symbolic models have weaknesses that make these models sub-optimal from a use perspective. Much of this data is in...
Three-dimensional (3D) imaging has been widely used in academic research and industrial applications. Compared to 2D representations, 3D imaging can yield more information about geometric structures of an object such as small surface variations that are difficult to perceive otherwise. 3D image contents provide additional information that is complementary to...
The study and design of machines that are able to analyze the auditory scene and organize sound into parts that are perceptually meaningful to humans is referred to as machine hearing. Such machines are expected to distinguish between different sound categories (e.g., speech, music, background noise), focus on a sound...
Social media and online forums provide spaces where people can gather beyond restrictions of geographic proximity. For some individuals with mental illness, these spaces are vital; providing outlets and communities where a multitude of experiences are accepted and understood, rather than judged against normative, often ableist standards. For nearly three...
Surface appearance represents the sense impression of the surface. In visual art, the artists try to use the appearance of their artworks to express their mental state and philosophy. Researchers in the cultural heritage community has been trying to use different analysis approaches to interpret artworks. In Computer Graphics and...
In the current state of robotics, the systems we create are heavily reliant on our consistent guidance, programming of tasks, and oracle information that allow them to operate in the world that we inhabit. What happens to our robotic systems when we are unable to perform as an oracle, creating...
Peer review is a commonly used tool to manage large classes. It allows students to grade and provide feedback to each other based on rubrics provided by instructors. Peer review has been proved to be effective in improving students' learning outcomes by many research. During providing peer review, students are...
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...
A core problem in many computer vision applications is visual recognition (including object classification, detection and localization). Recent advances in artificial neural networks (aka ”deep learning”) have significantly pushed forward the state-of-the-art visual recognition performances. However, due to the lack of semantic structure modeling, most current deep learning approaches do...
Modeling human language is at the very frontier of machine learning and artificial intelligence. Statistical language models are probabilistic models that assign probabilities to sequences of words. For example, topic models are frequently used text-mining tools to organize a vast set of unstructured documents by exploring their theme structure. More...
We address the problem of efficient maintenance of the answer to a new type of query: Continuous Maximizing Range-Sum (Co-MaxRS) for moving objects trajectories. The traditional static/spatial MaxRS problem finds a location for placing the centroid of a given (axes-parallel) rectangle $R$ so that the sum of the weights of...
Polymer nanocomposites are a class of advanced materials comprised of soft polymer matrix and nano-filler inclusions. While it has been found qualitatively that enhancements of material properties could be achieved by dispersing inorganic nano-particles into organic polymer matrix, the intrinsic governing principles of such composite has not been thoroughly studied...
Connecting structure and function in nanoscale engineered materials and devices relies on the analysis of the fundamental arrangement of matter, frequently under dynamic conditions. The demand to image structures at fundamental length scales has touched inorganic materials, biology, and frequently hybrid hard/soft materials with unique phenomena driven by heterogeneous components....
In this dissertation, we study different machine learning algorithms including probabilistic, sparse and deep learning based models applied to multi-sensory datasets. In many machine learning problems, samples are collected from more than one source or modality. Also, various feature extraction methods can be used to provide more than one set...
Data mining is multidisciplinary process involving computer science, artificial intelli- gence, and machine learning. The aim of data mining is discovering knowledge from a vast amount of data. This process consists of a set of stages forming a pipeline. This pipeline process consists of multiple steps: 1) Finding the right...
Blockchains are an exciting new type of Peer-to-Peer (P2P) distributed systems, which enable parties to transact directly, and maintain the record of said interactions in a distributed manner. A unique feature of blockchains is their ability to maintain a consensus without requiring knowledge on the number of participants, nor their...
The theory of how humans and machines control and communicate with each other is at the core of the scientific field known as Human-Robot Interaction (HRI). Researchers in this sub-discipline of robotics are therefore particularly interested in developing methods to chuppahreduce the inherent friction in this communication and control channel....
Newcomers, or new members to organizations or professions, bring insights that are critical to the advancement of society. Yet newcomers often have low self-efficacy, or low beliefs in their abilities to achieve a task, which can impact performance and retention. Research suggests that self-efficacy can be developed through in-person social...
This dissertation combines perspectives from social networks and teams research to advance understanding of team self-assembly. Across three substantive chapters, I explore team member search behaviors and invitation patterns in contexts where individuals exercise agency to select team members. First, I consider the search for team members in a social...
Responsiveness -- the time it takes for a message recipient to respond to a message -- has long been of interest to scholars in the fields of computer-mediated communication and human-computer interaction. It has been hypothesized that responsiveness is used to signal emotional information, and many empirical studies have demonstrated...
Abstract The work presented in this dissertation addresses three broad areas of video signal processing: video transmission, motion estimation and error concealment. In the first category, focused on the source-side, we present two machine learning models for efficient content-aware resource allocation and packet prioritization for video transmission over shared/constrained, lossy...
Visual matching is an important and fruitful research topic in computer vision area. Starting from the early face recognition, super-resolution, object tracking to the most recent person re-identification, cross-model retrieval, visual matching plays an important role as the core component in these tasks. The quality of visual matching directly and...
Annual age-adjusted breast cancer incidence rates in the United States have been static for decades. More recently, the development of massively parallel, high throughput DNA sequencing has enabled the cataloging of somatic mutations in cancer. Mutations are non-random and occur within sequence motifs. These motifs provide us with evidence to...
In the near future, self-driving or driverless vehicles will operate without human control, enabling passengers to use their time in new ways. This opens up avenues for designing new interactions and experiences for individuals or groups traveling in an automobile. For that scenario, automobile manufacturers propose developing bigger and better...
Recovering three-dimensional (3D) structural information of a specimen from a single two-dimensional (2D) measurement remains an important but challenging task in microscopic imaging. A conventional 2D microscopic image has a shallow depth-of-focus (DoF). Thus, recovering 3D information usually requires sequentially z-scanning the focal planes. This process is time consuming and...
Automated sketch collaborators might help us create more dynamic intelligent tutoring systems, work out designs, reduce bias in solving spatial social problems, and organize our ideas. Here, we examine some properties of sketch recognition methods designed to help serve that goal. Structure Mapping techniques are applied to symbolic structural descriptions...
Natural Language Processing methods have become increasingly important for a variety of high- and low-level tasks including speech recognition, question answering, and automatic language translation. The state of the art performance of these methods is continuously advancing, but reliance on labeled training data sets often creates an artificial upper bound...
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...