This thesis advances visualization design research by developing and evaluating new theoretical knowledge and computational techniques, which target the rising complexity of data and growing …
Despite the fact that the human genome was sequenced ten years ago, there exists no database of cis-regulatory architecture that is validated conclusively by rigorous …
This dissertation describes three primary contributions to the field of medical imaging: (1) a mathematical model ("Blockhead") of the macroscopic and microscopic structure of the …
Reinforcement learning defines the problem facing agents that learn to make good decisions through action and observation alone. To be effective problem solvers, such agents …
Recent research has proposed a variety of cross-cutting tools to help monitor and troubleshoot end-to-end behaviors in distributed systems. However, most prior tools focus on …
Humans are able to solve complex problems by distilling their knowledge of the world into simplified task-relevant representations and creating plans to achieve their goals. …
The widespread popularity of visual data exploration tools has empowered domain experts in a broad range of fields to make data-driven decisions. However, a key …
This work unifies insights from the systems and functional programming communities, in order to enable compositional reasoning about software which is nonetheless efficiently realizable in …
Abstract of Advancements in Portfolio Methods for Optimal Multi-Agent Pathfinding, by Eric Ewing, Ph.D., Brown University, October 2024 Multi-Agent Pathfinding (MAPF) is a critical problem …
The human genome exhibits a rich structure resulting from a long history of genomic changes, including single base-pair mutations and larger scale rearrangements such as …
Variation in genomes occurs in many forms, from single nucleotide changes to gains and losses of entire chromosomes. Large-scale rearrangements, called structural variants (SVs), are …
Just as an interconnected-computerized world has produced large amounts of data resulting in exciting challenges for machine learning, connected households with robots and smart devices …
Online stochastic combinatorial optimization problems are problems in which a decision maker is trying to minimize or maximize an objective by making a sequence of …
Recently, some researchers have attempted to exploit state-aggregation techniques to compute stable distributions of high-dimensional Markov matrices. While these researchers have devised an efficient, recursive …
The confluence of ubiquitous, high-performance networking and increased availability of online information has led to the emergence of a new class of large-scale stream processing …
Peer-to-peer systems have been proposed for a wide variety of applications, such as file-sharing, distributed storage, and distributed computation. These systems seek the benefits of …
There are two primary issues facing database systems designed for on-line transaction processing (OLTP): scalability and performance. Traditional disk-based OLTP architectures are the result of …
Current efforts in syntactic parsing are largely data-driven. These methods require labeled examples of syntactic structures to learn statistical patterns governing these structures. Labeled data …
The adoption of cloud computing has pushed many big-data analytics frameworks to run as multi-tenant services where users submit jobs and a cluster resource manager …
