Master of Sciences, Case Western Reserve University, 2022, EECS - Computer and Information Sciences

We expand on the framework of graph matrices first introduced by Ahn et al. [1], which are a class of random matrices whose entries' dependence can be described by a small graph. While Ahn et al. assume that a univariate distribution underlies this dependence, we relax this assumption and introduce graph matrices whose input structure is derived from a multivariate probability distribution. We then show spectral norm bounds on these graph matrices as being consistent with those under the univariate setting using the trace power method. Our result expands Ahn et al's work by allowing for random matrices with more complicated dependencies between elements. We present potential applications that have such dependencies under the multivariate setting in fields such as graph theory.

Committee: Harold Connamacher (Advisor); Vincenzo Liberatore (Committee Member); Mark Meckes (Committee Member) Subjects: Computer Science; Mathematics

Master of Science in Electrical Engineering, University of Toledo, 2013, College of Engineering

Processing large graph datasets represents an increasingly important area in computing research and applications. The size of many graph datasets has increased well beyond the processing capacity of a single computing node, thereby necessitating distributed approaches. As these datasets are processed over a distributed system of nodes, this leads to an inter-node communication cost problem (also known as inter-partition communication), negatively affecting the system performance. This research proposes new graph partitioning algorithms to minimize the inter-node communication by achieving a sufficiently balanced partition. Initially, an intuitive graph partitioning algorithm using Random Selection method coupled with Breadth First Search is developed for reducing inter-node communication by achieving a sufficiently balanced partition. Second, another graph partitioning algorithm is developed using Particle Swarm Optimization with Breadth First Search to reduce inter-node communication further. Simulation results demonstrate that the inter-node communication using PSO with BFS gives better results (reduction of approximately 6% to 10% more) compared to the RS method with BFS. However, both the algorithms minimize the inter-node communication efficiently in order to improve the performance of a distributed system.

Committee: Robert Green (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); William Acosta (Committee Member); Mansoor Alam (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy, Case Western Reserve University, 2023, EECS - Computer and Information Sciences

This dissertation presents a comprehensive approach to advancing proteomics and under-studied proteins in network biology, emphasizing the development of reliable algorithms, fair evaluation practices, and accessible computational tools. A key contribution of this work is the introduction of RoKAI, a novel algorithm that integrates multiple sources of functional information to infer kinase activity. By capturing coordinated changes in signaling pathways, RoKAI significantly improves kinase activity inference, facilitating the identification of dysregulated kinases in diseases. This enables deeper insights into cellular signaling networks, supporting targeted therapy development and expanding our understanding of disease mechanisms. To ensure fairness in algorithm evaluation, this research carefully examines potential biases arising from the under-representation of under-studied proteins and proposes strategies to mitigate these biases, promoting a more comprehensive evaluation and encouraging the discovery of novel findings. Additionally, this dissertation focuses on enhancing accessibility by developing user-friendly computational tools. The RoKAI web application provides a convenient and intuitive interface to perform RoKAI analysis. Moreover, RokaiXplorer web tool simplifies proteomic and phospho-proteomic data analysis for researchers without specialized expertise. It enables tasks such as normalization, statistical testing, pathway enrichment, provides interactive visualizations, while also offering researchers the ability to deploy their own data browsers, promoting the sharing of findings and fostering collaborations. Overall, this interdisciplinary research contributes to proteomics and network biology by providing robust algorithms, fair evaluation practices, and accessible tools. It lays the foundation for further advancements in the field, bringing us closer to uncovering new biomarkers and potential therapeutic targets in diseases like cancer, Alzheimer' (open full item for complete abstract)

Committee: Mehmet Koyutürk (Committee Chair); Mark Chance (Committee Member); Vincenzo Liberatore (Committee Member); Kevin Xu (Committee Member); Michael Lewicki (Committee Member) Subjects: Bioinformatics; Biomedical Research; Computer Science

Master of Computer Science, Miami University, 2021, Computer Science and Software Engineering

Causal maps use directed network structures to represent causality between concepts in a system, and are vital for conceptual modeling- a core activity in the field of Modeling & Simulation (M&S). Simulation models are generated from collections of maps, introducing scalability challenges as modelers are unable to effectively process large collections manually, or when maps contain many concepts. Despite this, there is a paucity of research on reducing human interventions across the various steps in causal mapping. In this thesis, we develop Network Science tools to overcome these challenges and present a framework for processing maps automatically. First, we demonstrate how the accepted practice of manually transforming evidence into maps introduces significant bias and that indirect elicitation must be fully documented. To further reduce the risk of bias from modelers, we present and evaluate a method to combine maps using semantic and causal information. We then develop a systematic, data-driven approach to extract a useful model from combined maps, in part by characterizing whether recently proposed metrics on identifying central concepts are feasible in large maps. Our approach is validated through studies on suicide modeling and can subsequently be used to process causal maps in many other research areas.

Committee: Philippe Giabbanelli PhD (Advisor); Karen Davis PhD (Committee Member); Vaskar Raychoudhury PhD (Committee Member) Subjects: Computer Science

Master of Science, The Ohio State University, 2020, Computer Science and Engineering

As the number of data sources and dynamic datasets grows so does the need for efficient online algorithms to process them. Clustering is a very important data exploration tool used in various fields from data science to machine learning. Clustering finds structure unsupervised among indecipherable collections of data. Online clustering is the process of grouping samples in a data stream into meaningful collections as they appear over time. As more data is collected in these streams online clustering becomes more important than ever. Unfortunately, the number of available efficient online clustering algorithms is limited due to the difficulty of their design that often requires significant trade-offs in clustering performance for efficiency. Those that do exist require expert domain knowledge of the data space to set hyperparameters for the algorithm such as the desired number of clusters. This domain knowledge is often unavailable, so resources must be spent tuning hyperparameters to get acceptable performance. In this thesis we present an online modification to FINCH, the recent state-of-the-art parameter-free clustering algorithm by Sarfraz et al. called Stream FINCH (S-FINCH). We examine the stages of the FINCH algorithm and leverage key insights to produce an algorithm which reduces the online update complexity of FINCH. We then compare the performance of S-FINCH and FINCH over several toy and real-world datasets. We show theoretically and empirically that our S-FINCH algorithm is more efficient than the FINCH algorithm in the online domain and has reasonable real-time update performance. We also present several alternative cluster representatives which can be used to build different agglomerative cluster hierarchies using the S-FINCH algorithm. We compare the cluster quality and clustering time performance of these new representatives with the original FINCH algorithm. The S-FINCH algorithm presented in this thesis allows for fast and efficient online (open full item for complete abstract)

Committee: James Davis PhD. (Advisor); Juan Vasquez PhD. (Committee Member); Kyle Tarplee PhD. (Committee Member); Wei-Lun Chao PhD. (Committee Member) Subjects: Computer Science; Information Science; Information Technology

PhD, University of Cincinnati, 2008, Engineering : Electrical Engineering

In today's world, with ever increasing design complexity and constantly shrinking device sizes, the microelectronics industry faces the need to develop an entire system on a single chip (SoC). This need gives rise to the responsibility of developing mature Computer-Aided-Design (CAD) tools to tackle such complexities. Unlike digital CAD tools, automated synthesis tools for the irreplaceable analog sections are still immature. Circuit-level analog synthesis comprises of two steps – Topology formation and Sizing of the topology. Topology selection and topology generation are two approaches to topology formation. Research in topology selection has almost been discontinued owing to heavy designer dependency. But with the advent of evolutionary techniques like Genetic Algorithm (GA) and Genetic Programming (GP), topology generation gained popularity. Topology generation is the art of generating device level circuit schematics satisfying user specifications. This thesis makes a genuine endeavor to develop topology generation tools individually for both passive analog circuits involving R, L, and C components and active circuits that involve additional MOS devices. For passive circuits, we present a GA-based synthesis framework, where the component values for the first set of circuits are set through a deterministic computational technique. Further, the crossover technique for breeding off-springs from parent solutions obeys certain constraints to ensure the formation of structurally correct circuits. The work has been further extended with the introduction of novel selection and crossover strategies. The above techniques have been successful in synthesizing various low-pass and high-pass filter designs. In the pursuit of developing an active circuit topology generator, we have developed a self-learning optimization algorithm involving multiple design variables. To measure the effectiveness of this technique, we applied it first to a relatively easier domain viz. MPLS c (open full item for complete abstract)

Committee: Ranga Vemuri (Committee Chair); Wen-ben Jone (Committee Member); Harold Carter (Committee Member); Dharma Agrawal (Committee Member); Jintai Ding (Committee Member) Subjects: Computer Science; Electrical Engineering; Engineering; Systems Design

Master of Science (MS), Bowling Green State University, 2009, Computer Science

Data mining algorithms help reveal hidden information in a repository. Distributed mining algorithms meet this need by distributing data and computation. One of the mostimportant issues of these algorithms is how to safely mine the data. Secure Multiparty Computation (SMC), a framework for safe mining of distributed data, provides some security promises of the computation. This thesis addresses certain aspects of SMC including the role of Hamiltonian and edge-disjoint Hamiltonian cycles. We formalize the notion of trust in a network and show thatcertain network configurations are better than others. We propose and analyze an algorithm for id assignment in networks that outperforms an existing algorithm.

Committee: Ray Kresman PhD (Advisor); So-Hsiang Chou PhD (Committee Member); Mohammad Dadfar PhD (Committee Member) Subjects: