Clustering is a fundamental task in unsupervised learning, which aims to partition the data set into several clusters. It is widely used for data mining, image segmentation, and natural language processing. One of the most popular clustering methods is centroid-based clustering, including k-medians and k-means clustering. k-medians and k-means clustering...
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...
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...
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,...
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...