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...
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...
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...
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...
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...
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...
Human language processing is incremental. In this dissertation, I explore how an incremental perspective can help us clarify our understanding of transformational syntax, which typically proceeds bottom-up. As part of our exploration, I develop an incremental head-driven parsing algorithm for Minimalist Grammars. The two main innovations of this parsing algorithm...
Security and robustness are two critical problems in modern computing system. In this disserta- tion, we study these two problems in both hardware system and learning system.Firstly, we discuss the robustness problem in hardware system. Modern microprocessors suffer from significant on-chip variation at the advanced technology nodes. The development of...
Recent developments in deep learning have led to breakthroughs in rendering novel views from sparse input views of a scene.While the accuracy of these algorithms has improved dramatically, it has come at a huge computational cost.
While developments in graphics hardware have ameliorated some of the computational burdens, deep learning-based...
This thesis studies Bayesian-robustness of algorithm design. The main perspective requires for a single fixed algorithm that its performance is an approximation of the optimal performance when its inputs are independent and identical draws (i.i.d.) from every unknown distribution which is an element of a known, large class of distributions....
In this thesis, we aim to develop efficient algorithms with theoretical guarantees for noisy nonlinear optimization problems, with and without constraints, under various different assumptions. Apart from Chapter 1 which provides relevant backgrounds, the remaining of thesis is divided into four chapters. In Chapter 2, we establish the theoretical convergence...
Over the past decade as smartphones and wearable tracking devices have grown in popularity, more individuals have begun collecting their own health and behavioral data. Innovations in sensor technology now allow individuals to continuously collect data over long periods of time with minimal effort. As a result, more data has...
This research looks at the robotic shape formation problem, which is one of the fundamental problems in robotic swarm systems. Here, the task is to move a group of robots to form a user-specified shape. In this dissertation, the task of shape formation is divided to four problems: (i) using...
Manufacturing processes are known for their intricacies in changing material shapes and properties. New generations of manufacturing technologies, known as flexible manufacturing, are moving toward design freedom, which allows producing parts with optimized geometries and high customizations at an affordable cost even for low-volume productions. Two prominent flexible manufacturing processes...
Next generation cellular networks are expected to support a massive data traffic volume and satisfy a vast number of users that have latency-critical quality-of-service expectations. Towards serving this demand, it is envisaged that the interference management problem will be the main bottleneck due to the likeliness of a heavily interfering...
At its core, the purpose of microscopy is to make objects and their underlying structures visible under high magnification. With the remarkable progress of electron microscopy, the sub-micron “high” magnification of light microscopy has been completely refashioned to encompass subatomic length scales. Unfortunately, higher-magnification does little to negate existing interpretability...
Existing nonlinear optimization methods have proven reliable over the past few decades for a wide range of applications but have critically relied on accurate function and gradient evaluations. Modern nonlinear optimization problems arising from machine learning and scientific computing applications are increasingly complex and large scale, which make accurate evaluations...
Language models are the foundation of many natural language tasks such as machine translation, speech recognition, and dialogue systems. Modeling the probability distributions of text accurately helps capture the structures of language and extract valuable information contained in various corpora. In recent years, many advanced models have achieved state-of-the-art performance...
We live in an increasingly computational world; one that, in the near term, may require everyone to be computationally literate. Computer science (CS) education has greatly increased its reach in the last two decades with an increasing number of students having access to formal computer science classroom experiences in the...
The dissertation builds on my current research to demonstrate the connection between affect and learning through machine learning and qualitative analysis of interactions where players use a complex systems game. The project is threefold: First, I developed a thinking and learning intervention, the agent-based modeling simulation Ant Adaptation. I showed...
Wearable-based human activity recognition is well-studied in the machine learning and pervasive computing community. A large corpus of studies focused on using wearable sensors to recognize health-related behaviors that involve high periodicity in the sensed signal, such as sitting, walking, and running. Other activities that occur less frequently throughout the...
Asymmetric relationships between creators and consumers in peer-produced knowledge repositories produce inequitable knowledge representation--or knowledge gaps. These gaps result in unequal access to information, and downstream technologies that leverage peer-produced data perpetuate these inequities. Effective knowledge gap identification represents a necessary first step towards equitable knowledge representation. However, while prior...
The past decade has seen the rapid progress of deep learning, which becomes a game-changing technique in different data-intensive domains, with the availability of large scale data, cost-effective computing hardware and more advanced learning theory and algorithms. Despite of the rapid progress of deep learning methods in daily-life applications, such...
Automated driving has become a very popular topic in the recent years and is becoming more and more of a reality. In this new trend, High Definition (HD) maps play an important role in many ways that will provide a safer and more efficient driving experience, especially in terms of...
Modern data sets are increasingly vast, not only in the number of samples, but also in the number of measurements, or features, that they contain. This high-dimensionality poses a unique set of problems for data analysis due to a set of phenomena known as ``the curse of dimensionality.'' This thesis...
Art has been tied to scientific and technological advancements throughout history, providing methods and mediums for communication, expression, and exploration. Art is a dialogic domain that evolves with the technological advances in society–incorporating technology and computational tools to create new genres of art. We live in an increasingly computational and...
Since the invitation of ARPANet in 1969, network protocols and communication systems have continued to emerge. Especially in the past decade, the prosperity of mobile internet and cloud computing has resulted in a large number of network protocols and communication systems, which have become critical infrastructure for our society. Availability...
The rise and racial gap in maternal mortality and morbidity in the US growing public health crisis. The US maternal mortality rate is double that of peer countries such as the UK and Canada. Even more striking, Black women are 243% more likely to die from childbirth-related causes. According to...
We consider general utility models and information structures of the agents and illustrate when economic conclusions for designing simple mechanisms in classical settings extends for general environments. We show that whether economic conclusions can be generalized depends on the details of the generalizations. For example, in single-item auction, competition and...
While there is high demand for university computer science (CS) courses, students often struggle when learning to program. Prior work has identified that student perceptions of their programming ability may contribute to these challenges. For example, studies show that students often perceive that they do not belong, are not capable...
The current view in neuroscience holds that the brain, together with its sensory and motor structures and the environment, form a closed-loop system – a sensorimotor loop – in which the brain receives information from the environment and converts it into a motor response while simultaneously making predictions about future...
Memory management and address translation need significant optimizations in order to not behindrances in the near future. Currently, plenty of work has started to address issues within the
current abstraction of the hardware-software codesign of paging. I argue that a new abstraction
is needed in order to properly address this...
The advent of metamaterials—hierarchical structures that manifest properties beyond those found in nature through geometry rather than material composition—inspired new possibilities and research in many fields. In mechanics, periodic metamaterials exhibit behaviors ranging from unprecedented compressibility to extreme stiffness. Numerous geometric classes of metamaterials with these properties have been discovered,...
Wearable visual systems, such as ego-centric wearable cameras, have failed to integrate into everyday life. We have witnessed the abandonment of wearable visual systems as consumer devices (e.g., Google Glass) and as research tools (e.g., SenseCam). While it is natural for some technologies to die out, visual wearable systems are...
In this dissertation, we aim to develop algorithms that achieve optimality with provable complexity guarantees under various settings in reinforcement learning (RL). Specifically, in Markov decision processes (MDPs), we study single-agent and multi-agent online RL, respectively, and offline RL under the presence of unobserved confounders. Single-agent online RL. We design...
In recent years, machine learning on graphs (or networks) has gone from a niche topic with only a few active researchers worldwide, to a heavily invested field with novel use cases for dealing with relationships and/or interactions within complex systems in the natural and social sciences. Traditionally, choosing the right...
X-ray imaging at nano and micro-scale is of great importance for the material science and defense industry. Large penetration depth and low wavelength of x-rays offer an important potential to image objects at high resolution and in a non-invasive process. While the ever-growing community is pursuing novel applications and looking...
Visual Question Answering (VQA) increasingly attracts industry and academia attention. It requires the model to provide a natural language answer by an image and a related natural language question. Meanwhile, it relates to multidisciplinary research such as natural language understanding, visual information retrieval, and multimodal reasoning. As a multimodality task,...
Imagine sitting in a room listening to some friends play a song. Perhaps one friend is playing guitar, another playing bass, and a third is playing drums. The musical content in this scene is extraordinarily complex, yet it contains many types of structure that is easy for us to comprehend....
Many computing technologies are primarily useful because of the existence of some set of data created by people, intentionally in some cases and unintentionally in others. For instance, technologies like search engines, recommender systems, classifiers, and language models are all dependent on digital records of things people have said, done,...
A massive amount of data is generated every second all around the world. Machine learning becomes the most attractive solution to consume the data fuel and transform it into productivity. It has yielded great results in many fields, such as healthcare, marketing, finance, etc. Machine learning models are usually designed...
Public-facing data-driven technologies such as social media platforms and search engines rely on data producers, such as users and crowd workers, to be feasible and financially sustainable. Recently, it became clear that the goals of these data-driven technologies do not always align with those of the public, causing public backlashes...
Task-oriented conversational systems are becoming increasingly popular, as shown by the rise of conversational recommendation systems across multiple platforms (e.g., Google Home, Alexa, and Siri) and domains (e.g., local establishments, e-commerce, books, music, and movies). However, users are still largely limited in what preferences they can express and how, as...
In the Maximum-a-Posteriori (MAP) Inference problem, for any given probability distribution, the goal is to find the point in the support of that distribution with the highest probability. Potts models and Determinantal Point Processes (DPPs) are probabilistic models that were introduced in the context of statistical physics several decades ago....
As our world is increasingly filled with data visualizations, having the skills to leverage data visualizations is essential for participation in society. Confident engagement with data visualizations is critical for being an educated member of society; however, research has shown that it is difficult for individuals to digest and gain...
Machine learning is seeping into every fabric in various practical domains such as autonomous driving, wearable computing, and smart buildings. However, in the actual development and integration, especially when the learning-based components are frequently included as components of large complex systems where the physical instances can be included as interactable...
Task-oriented conversational systems are becoming increasingly popular, as shown by the rise of conversational recommendation systems across multiple platforms (e.g., Google Home, Alexa, and Siri) and domains (e.g., local establishments, e-commerce, books, music, and movies). However, users are still largely limited in what preferences they can express and how, as...
The production and spread of digital news involves a wide range of actors: journalists and the organizations that employ them, social media platforms, audiences, and myriad commentators, citizen journalists, bloggers, and other actors who contribute to the news ecosystem without inhabiting an official role. These actors interact in flexible, often...
Human communication has become increasingly reliant on systems made and managed by large technology companies like Google, Apple, Twitter, and Meta (formerly Facebook). These systems offer people many benefits, but they also present new challenges for society. In recent years, researchers, lawmakers, and journalists have suggested that large technology companies...