Dissertation Defense Announcements

Since the 1954 Brown v. Board of Education ruling against school segregation, Black women teachers (BWTs) have had perpetually high rates of attrition, despite their legacy of providing high quality, emancipatory education. Thus, the purpose of this study was to contextualize the attrition of critical BWTs to better understand the factors that would support their sustainability in urban schools. Specifically, I investigated (a) the relationship between Black women’s intersectional identities and their experiences as critical educators in urban schools, (b) the compounding factors that led to their ultimate departure, and (c) the complexities of their decision to leave the profession.

Using Black feminist thought and cognitive dissonance theory as my framework, I employed sista circle methodology to study fifteen post-service critical Black women teachers. Each participant engaged in an individual interview, one of three sista circles, and a written reflection. Data analysis revealed three major themes that offer rich context and a complex narrative of why critical BWTs love and leave the classroom: instinct vs. opposition, commitments vs. personal needs, and dissonance-reduction strategies. As they are examined, these themes suggest several actions that can be taken by key stakeholders to support their professional sustainability.

In the past few decades, pairwise distance based statistical methods have been developed to identify spatial and/or temporal clusters of disease, study the association between the dissimilarity of ecological communities and distance in geographical locations. With emergence of high-throughput technologies, pairwise distance base methods are widely used in the analysis of genetics and genomics data, especially when the data structure fails the fundamental assumptions of classical multivariate analysis, including independency and normality. However, much of existing knowledge has been around non-parametric or semi-parametric estimations usually employing permutation techniques to assess statistical significance, which are known to be computationally expensive and sensitive to the choice of permutation.

Majority of this thesis focuses on linear regression of pairwise distance matrices. We consider the pairwise correlation structure between the distances and investigate the large sample properties of the ordinary least square estimator of the model coefficients. Extensive simulations are conducted to evaluate the performance of our method with finite sample size.

Another major component of the thesis is the human microbiome data analysis. We analyze the integrative Human Microbiome Project (iHMP) data set of composition of microbial communities in the digestive tracts of humans by using multiple statistical methods, including our proposed method. The results are presented and interpreted. Existing challenges and future works are also discussed.

In recent years the grid modernization and rapid growth in distributed energy resources due to environmental consciousness have resulted in distribution grids becoming more active which has led to significant interaction between transmission and distribution grids. In this dissertation, novel approaches in modeling and management tools are proposed considering integrated power transmission and distribution systems with Distributed Energy Resources (DERs). First, new power methods for power distribution system considering DERs is proposed in a single-phase, three-phase, and three sequence domain. Second, an integrated transmission and distribution (T\&D) grid model where transmission and distribution systems are considered as a single unit is proposed. A coalescing Ybus approach is used to obtain the bus admittance matrix of the combined T\&D system. Further, to successfully capture the effect of unbalances in the system at the same time reducing computational burden owing to the larger size, a three-sequence modeling framework is used for a unified system. A three-sequence-based multi-period power flow method is used to accurately capture the time-varying aspects of the system. Next, a three-sequence fault analysis method capable of conducting short circuit analysis on a DER integrated unbalanced distribution system is developed. All these sequence-based methods are then used for steady-state analysis of the integrated T\&D system. Finally, a sensitivity-based coordinated voltage control scheme using reactive power support from DERs is proposed which can lead to reduced voltage regulator operations and tighter voltage profiles. The proposed methods have been validated using large-scale IEEE T\&D feeders to prove the real-life implementation capabilities of the models and tools

The development of in-memory computing has fueled the emergence of in-memory computing systems. Data explosion is also posing an unprecedented demand for memory capacity to handle the ever-growing data size. Thus, in-memory computing systems are increasingly looking inward at hybrid memory caches of under-processed data as resources to be mined. Our preliminary study finds that some existing data management strategies often trade application performance for low memory utilization, and hence can induce frequent I/O operations between memory system and storage system.

To achieve this goal, we propose to design a hybrid memory system that includes fast and relatively slow memory hardware. In order to realize a runtime system that automatically optimizes data management on hybrid memory, we will (1) propose a new shared in-memory cache layer among parallel executors that are co-hosted on the same computing node, which aims to improve the overall hit rate of data blocks; (2) develop a middleware layer built on top of existing deep learning frameworks that streamlines the support and implementation of online learning applications; (3) design a unified in-memory computing architecture with efficient data management strategy to optimize memory allocation and recycle for ML applications.