Bachelor of Arts (BA), Ohio University, 2024, Sociology

In the past decade, the number of studies of transgender individuals has grown exponentially, but specific research into subcommunities under the broader transgender label is lacking. Most studies focus on either binary transgender individuals (transgender women and transgender men) alone, or combine all persons identifying as any gender other than their gender assigned at birth into one large group of “transgender people.” However, the limited intragroup research that has been done suggests that experiences vary drastically depending on one's gender identity, and we can imagine that the experience of binary transgender individuals would be very different than the experiences of nonbinary transgender individuals. This study reports on the lived experiences of nonbinary people – individuals identifying as neither men nor women, but rather existing outside of the gender binary. Qualitative interview methods were used to gather information from nonbinary individuals about their personal experiences with identity development, external experiences of stress from other people, and the internalized impacts of minority stress. Based in grounded theory, the subsequent report on these interviews includes discussion and analyses of the data collected. This discussion utilized a newly suggested framework – the Respect, Support, and Perception Theory – for analyzing interpersonal relationships with genderqueer individuals and the potential internalized effects that these relationships have on nonbinary individuals.

PhD, University of Cincinnati, 2022, Arts and Sciences: Sociology

Transgender Americans experience high rates of medical discrimination. As a result, they frequently avoid getting medical care, even when needed, for fear of mistreatment. While most trans people want to leverage medicalization to embody their gender identities, finding gender-affirming healthcare is challenging because competent providers are rare, care is geographically inaccessible, and insurers commonly refuse to cover medical interventions. This dissertation describes how trans people access and navigate medicine given the multitude of healthcare constraints and barriers they face. I investigate how trans Americans decide whether and how to medicalize their gender identities. Specifically, people considering or engaged in medicalized body projects described healthcare experiences related to their information-seeking behaviors and practices, receipt of diagnoses, and navigation of provider interactions, medical institutions, and insurance policies. I analyzed 70 hours of indepth interviews with 34 transgender people who live in the greater Cincinnati, Ohio area, using a grounded theory methodology. Interviews were transcribed and thematically coded using NVivo. Emergent analytic categories center on themes related to experiences of medicalization, how trans individuals operationalize community networks to prepare for medicalization, and experiences of gender delegitimation in medicine. This research reveals that: 1) binary-oriented and non-binary trans people experience medicalization differently, particularly when trying to make sense of the diagnosis of gender dysphoria; 2) transgender people cultivate subcultural health capital via community networks, which allows them to gather informational and navigational capital to anticipate and mitigate medical discrimination, mistreatment, and gatekeeping; and 3) trans people experience institutional, interpersonal, and internalized gender delegitimation in medical settings. I present my findings in three jou (open full item for complete abstract)

Master of Science in Electrical Engineering (MSEE), Wright State University, 2019, Electrical Engineering

Signed-digit representation has been used to perform fast binary addition by eliminating the dependant carry chains. In this thesis, a high-speed radix-2 signed-digit architecture is first presented, which is easily expandable to 8-bit, 16-bit, 32-bit, and 64-bit signed-digit adder. The architecture mainly consists of two digital components: 1) 2s complement adder, and 2) redundant binary to 2s complement converter. The 2s complement adder adds two input operands and gives the sum resulting in a signed-digit format. The redundant binary to 2s complement converter converts the redundant binary format to the 2s complement format output. Next, by using a blend of software and custom design optimization, the 2s complement adder, the redundant binary to 2s complement converter, and the N-bit signed-digit adder (for N= 8, 16, 32, 64) are all 100% testable.

Master of Science in Chemical Engineering, Cleveland State University, 2017, Washkewicz College of Engineering

Chemical products are made by combination of processes that includes synthesis, separation and purification. Separation processes comprises a large portion in these industries and are considered to be critical as most of the applications in chemical industries involves mixtures. Traditional separation methods such as distillation, evaporation, drying etc., requires high energy. For example, air separation to produce nitrogen and oxygen was previously practiced by cryogenic distillation that involves high pressure units and large energy requirement. On other hand, adsorption processes utilize less energy resources and is unique among separation methods. The phase separation is achieved by the existence of a solid. Uniqueness of adsorption is the higher selectivity achievable by tailoring the adsorbents. Design and optimization of adsorption processes requires equilibrium information as models proofed by experimental data for understanding the conditions occurring in the process. This study is aimed at measuring, analyzing and reporting equilibrium data for pure component and binary mixture of N2 and O2 in LiLSX material. In addition, a model, Dual-Site Langmuir, is tested for its ability to represent the data in relevant range. Volumetric measurement technique is one of the most commonly used method for measuring pure and binary adsorption which involves measuring pressure change in a known volume of gas. In this study, pure adsorption equilibria for N2 and O2 in LiLSX was measured at three temperatures (277.15K, 296.15K, 318.15K). Isotherms in this study were of Type-1 represented by Dual-Site Langmuir model which constitutes the primary source of information necessary to model on adsorption process. Dual-Site Langmuir model is also used in this work to predict binary adsorption data. It should be noted that no adjustable parameters are available hence, DSL model is completely predictive using only pure component isotherm information. Binary adsorption equilibria f (open full item for complete abstract)

Ed.D., Antioch University, 2025, Education

This research addresses the critical need for supporting transgender and gender non-conforming (TGNC) students within K-12 public schools, recognizing the detrimental effects of overlooking their unique needs. Current policies often fail to address these needs adequately, leading to mental health challenges and negative educational outcomes. The study aims to identify the key factors influencing the successful implementation of transgender-supportive policies in schools. A review of current research underscores the importance of challenging existing gender-binary norms and promoting inclusivity for TGNC students. By creating affirming spaces and addressing systemic barriers, schools can foster a safe and supportive environment for TGNC youth, ultimately leading to more positive educational experiences and outcomes. Trans studies, queer pedagogy, and transgender theory collectively advocate for a nuanced understanding of gender within educational and societal frameworks. They challenge traditional educational structures that frame transgender individuals as problematic and instead emphasize gender self-determination, ambiguity, and the rejection of binary views. Utilizing an online survey and semi-structured interview in a mixed-methods approach, data was collected from school administrators across the Commonwealth of Pennsylvania to better understand their experiences in enacting trans-supportive policies and the impact the implementation of these policies has on TGNC students. This study contributes to the growing body of research on supporting TGNC students in educational settings, highlighting the necessity for continued efforts to dismantle cisnormativity and promote inclusivity across the gender spectrum. This dissertation is available in open access at AURA (https://aura.antioch.edu) and OhioLINK ETD Center (https://etd.ohiolink.edu).

Doctor of Philosophy, Case Western Reserve University, 2025, Macromolecular Science and Engineering

Stimuli-responsive polymers represent a versatile and dynamic class of materials with broad applications in drug delivery, diagnostics, and smart surface design. These polymers can respond to various external stimuli, including temperature, light, pH, and specific chemical triggers, allowing precise control over their properties and functions. The synthesis and assembly of stimuli-responsive particles, such as micelles, polymersomes, and nanoporous structures, have significantly advanced drug delivery systems and diagnostic tools by enabling stimuli-triggered payload release, enhancing therapeutic efficacy, and reducing side effects. Additionally, integrating these polymers with micro- and nano-patterned surfaces has enabled the development of smart interfaces with tunable properties, including wettability, adhesiveness, and optical characteristics.

Doctor of Philosophy, The Ohio State University, 2024, Computer Science and Engineering

Binary code comprehension, particularly within the context of stripped binaries, stands as a very useful task in binary analysis and software security applications ranging from malware analysis to vulnerability discovery and binary reverse engineering. Understanding stripped binary code is challenging due to the absence of symbols such as variable names, data types, and function names. This complexity is further exacerbated by the variety of binary abstract interfaces, instruction sets, computer architectures, compiler optimizations, and obfuscations. This dissertation systematically explores the problem of binary code comprehension using binary analysis, deep learning, and large language models. We first present an exploratory study, BinSum, on how machine learning models, particularly the state-of-the-art generative large language models, can understand binary code with a comprehensive benchmark and dataset encompassing over 557K binary functions. Subsequently, motivated by BinSum's finding of the semantic significance of function names in binary code, we introduce SymLM, a novel binary function name prediction framework, employing a unique neural architecture that captures comprehensive function semantics by modeling both the execution behavior of functions and their calling contexts. The third contribution of this dissertation focuses on the evaluation of code summaries' quality, in which we introduce a novel LLM-based code summary semantic evaluation metric, SimLLM, for assessing semantic similarity. This methodsignificantly surpasses traditional metrics and exhibits a high correlation with human judgment, addressing their shortcomings in understanding domain-specific terminologies prevalent in code summaries. Finally, we explore the generalizability of function name prediction by presenting BinSymn, a novel model architecture, trained on domain-adapted generative LLMs. Together, BinSum, SymLM, SimLLM, and BinSymn provide a comprehensiv (open full item for complete abstract)

Master of Arts (MA), Ohio University, 2024, Art History (Fine Arts)

This thesis examines the cultural and religious contexts behind the production of the Girona Apocalypse [Museu de la Catedral de Girona, Num. Inv. 7(11)], a tenth-century copy of Beatus of Liebana's eighth-century Commentary on the Apocalypse. It delves into the ways in which medieval society, guided by the gendered perceptions of the Latin Church, played a pivotal role in categorizing women within a binary framework: either as pure or immoral. The focus is on the portrayal of femininity in the Apocalypse of St. John, where the contrasting figures of the Great Harlot and the Woman Clothed with the Sun are juxtaposed, and how this imagery and symbolism are transformed into feminine archetypes in the Girona manuscript, resulting in a pictorial conflict and shedding light on the nuanced dynamics of gender in medieval Iberia. The Girona Apocalypse was created at the dual monastery at San Salvador de Tabara, and apparently was illuminated by a woman, Ende. Her contribution provides a subtle layer to the understanding of womanhood in medieval Iberia, highlighting the importance of the role she played in a society where women received limited validation and recognition. The Girona Beatus not only offers a unique perspective on the conception of womanhood in the Middle Ages, but also provides valuable insights into how a woman artisan painter navigated her identity within the constraints of a malecentric Christian narrative.

Doctor of Philosophy (PhD), Wright State University, 2023, Computer Science and Engineering PhD

Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features in gray encoding (GE) and kernel density estimator (KDE) with cumulative distribution function (CDF). Additionally, sentiment analysis and unique coding are assigned to each category of user activities so that the detection model can distinguish all activities, the correlation between activities, and the temporal characteristics of the activities. The first detection system is a Long-Short-Term Memory (LSTM) network. The first detection system reduced FNR, but the performance degraded as the dataset's size increased. The second detection system combines convolutional neural networks (CNN) and LSTM networks. Processing and modeling of the dataset created two problems that hindered the performance of the previous two detection systems (1) dimensionality and (2) vanishing short rows due to zero padding. The last detection system aims to reduce the curse of dimensionality and short rows vanishing. Two neural models are utilized, embedding layer and autoencoder. The embedding layer removes padded zeros and produces dense embedded output. The autoencoder compresses the input data samples to a shorter length and feeds the processed data samples to the detection model. All detection systems presented a high performance in classifying users' activities and detecting insider threats. The first (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Statistics

Exponential family PCA (Collins et al., 2001) is a widely used dimension reduction tool for capturing a low-dimensional latent structure of exponential family data such as binary data or count data. As an extension of principal component analysis (PCA), it imposes a low-rank structure on the natural parameter matrix, which can be factorized into two matrices, namely, the principal component loadings matrix and scores matrix. These loadings and scores share the same interpretation and functionality as those in PCA. Loadings enable exploration of associations among variables, scores can be utilized as low-dimensional data embeddings, and estimated natural parameters can impute missing data entries. Despite the popularity of exponential family PCA, we find several statistical issues associated with this method. We investigate these issues from a statistical perspective and propose remedies in this dissertation. Our primary concern arises from the joint estimation of loadings and scores through the maximum likelihood method. As in the well-known incidental parameter problem, this formulation with scores as separate parameters may result in inconsistency in the estimation of loadings under the classical asymptotic setting where the data dimension is fixed. We examine the population version of this formulation and show that it lacks Fisher consistency in loadings. Additionally, estimating scores can be viewed as performing a generalized linear model with loadings as covariates. Maximum likelihood estimation (MLE) bias is naturally involved in this process but is often ignored. Upon identifying two major sources of bias in the estimation process, we propose a bias correction procedure to reduce their effects. First, we deal with the discrepancy between true loadings and their estimates under a limited sample size. We use the iterative bootstrap method to debias loadings estimates. Then, we account for sampling errors in loadings by treating them as covariates with me (open full item for complete abstract)

Master of Fine Arts, Miami University, 2023, English

This collection has ten stories exploring queerness, religion/apostacy, family/estrangement, and how we react in the face of complete upheaval. Eschewing traditional forms, the collection crosses genres, consisting of fiction, non-fiction, and hybrid stories, making it not all one thing or another, but a non-binary collection, if you will. In several of the stories, we encounter monsters; in others, we plot to kill them. “The Path” follows a Mormon missionary caught in a liminal space between believing and doubting, though “Lessons From the Grave” plants its feet firmly outside of religion, detailing Mormon violence against queer people. The stories progress from grief to rage to acceptance, a record of emotions through two years of estrangement and shunning.

Doctor of Philosophy, The Ohio State University, 2022, Astronomy

While the Universe might at first appear static and unchanging to a casual observer, it is teeming with variable sources and cataclysmic events that mark the births, lives, and deaths of the many and varied objects filling our Universe. In recent years, modern time-domain surveys have revolutionized the study of stellar variability by providing access to time series data for millions of stars in the Milky Way. The All-Sky Automated Survey for SuperNovae (ASAS-SN) monitors the entire visible sky daily using 20 robotic telescopes in Hawaii, Texas, Chile, and South Africa. In addition to the real-time detection of bright supernovae and other transients, archival ASAS-SN data allows for the time series characterization of over 100 million stars. By analyzing the ASAS-SN time series data for ~61 million stars, I made the first homogeneous all-sky catalog of bright variable stars and then uniformly classified them using machine learning techniques. This catalog includes the discovery of ~220,000 new variable stars and ~660,000 variables in total. I present studies that use this catalog combined with information from large scale spectroscopic surveys to study various populations of variable stars. Finally, I present examples of the discovery of rare and unusual variable stars using ASAS-SN, including the most extreme 'heartbeat'' star ever discovered.

Master of Science in Cyber Security (M.S.C.S.), Wright State University, 2022, Computer Science

Vulnerabilities in source code can be compiled for multiple processor architectures and make their way into several different devices. Security researchers frequently have no way to obtain this source code to analyze for vulnerabilities. Therefore, the ability to effectively analyze binary code is essential. Similarity detection is one facet of binary code analysis. Because source code can be compiled for different architectures, the need can arise for detecting code similarity across architectures. This need is especially apparent when analyzing firmware from embedded computing environments such as Internet of Things devices, where the processor architecture is dependent on the product and cannot be controlled by the researcher. In this thesis, we propose a system for cross-architecture binary similarity detection and present an implementation. Our system simplifies the process by lifting the binary code into an intermediate representation provided by Ghidra before analyzing it with a neural network. This eliminates the noise that can result from analyzing two disparate sets of instructions simultaneously. Our tool shows a high degree of accuracy when comparing basic blocks. In future work, we hope to expand its functionality to capture function-level control flow data.

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Environmental Engineering

Topical delivery of dermatological drugs or cosmetic agents can provide significant benefits to the skin. On the other hand, dermal exposure to hazardous chemicals contributes to many occupational diseases and disorders. In this study, mechanistically-based skin penetration models were developed to simulate and predict the transient skin penetration of both hydrophilic and lipophilic compounds in various dermal exposure scenarios. The development was undertaken starting with an Excel-based spreadsheet model for transient diffusion of lipophilic and moderately polar compounds and adding the capability of including highly polar compounds and also an explicit representation of solvent-deposited solids. In Chapter 2, three one-dimensional diffusion models and two three-dimensional diffusion models were constructed on Mathematica in order to simulate desorption profiles of hydrophilic chemicals from human stratum corneum. The physical models for desorption behavior involved a combination of transverse diffusion through the tissue, and lateral diffusion and exchange with skin appendages. By optimizing transverse and lateral diffusion coefficients to match the experimentally measured profiles, we found that the lateral diffusivity values greatly exceeded the transverse values, but the transverse clearance exceeded lateral clearance due to the sparsity of skin appendages. The results confirmed that the transverse transport of hydrophilic compounds across human stratum corneum could be effectively described by one-dimensional models. In combination with earlier work comparing transcellular and intercellular pathways in the stratum corneum matrix, they furthermore confirmed that transcellular transport is an important component of the stratum corneum's polar pathway, in addition to the already-recognized appendageal transport mechanism. The desorption analyses led to the development of an Excel-based steady-state diffusion model that included a polar pathway. Extens (open full item for complete abstract)

Master of Sciences, Case Western Reserve University, 2021, EECS - Computer Engineering

Binary translation (BT) is the process of converting executable binary from one instruction set architecture (ISA) to another. Accelerated binary translation (XBT) refers to BT using FPGA for hardware acceleration and feeding the target processor at-speed. This work proposes a reconfigurable pipelined structure built on FPGA that performs XBT on different ISAs. An XBT system that translates MIPS to RISC-V is implemented and tested on the Xilinx Zynq platform. Results of several benchmarks show obvious speedup of approximately 48 times compared to an equivalent software approach.

Doctor of Philosophy, The Ohio State University, 2021, Astronomy

Rotation plays an important role in the life of stars, and offers a potential diagnostic to infer their ages and that of their planets. The idea of using stellar rotation as a chronometer is known as gyrochronology. While potentially fruitful over a wide range of ages and masses, gyrochronology has not been vetted across all the relevant regimes, and from a theoretical perspective the evolution of rotation is not accurately predicted by stellar models from first principles. For these reasons, empirical studies of rotation play a key role in stellar astrophysics. In recent years, much of the classical knowledge on the rotational evolution of low mass stars has been challenged by new data sets (e.g., Kepler and Gaia). In particular, contemporary results have raised concerns regarding the applicability of gyrochronology as a universal age diagnostic. In this context, this dissertation carries out three efforts to examine gyrochronology in different evolutionary regimes and stellar configurations. Regarding young stars (< 1 Gyr), I illustrate the impact that removing the non-member contamination by using the precise Gaia astrometry has on the rotational sequences of open clusters. The clean and updated sequences show that the overall fraction of rotational outliers decreases considerably but not completely; demonstrate that ground-based rotation periods can be as constraining as space-based periods; illustrate that 1.0 to 0.6 Msun stars populate a global maximum of rotation periods (with potential implications for exoplanet habitability); and suggest evidence that rapid rotators experience a lower torque than intermediate rotators in the saturated domain. Importantly for future calibrations and tests of stellar models, the percentiles of the revised rotational sequences are made publicly available. Regarding old stars (few Gyr), I carry out a search for common proper motion wide binaries in the Kepler field. These systems that can be thought of as the smallest ve (open full item for complete abstract)

MS, University of Cincinnati, 2021, Engineering and Applied Science: Computer Science

Searching for interesting patterns in large datasets has become commonplace due to the rate at which the data is growing. Formal Concept Analysis (FCA) is a great tool to capture the innate concept structure within the data. Hence, it is gaining attention from professionals in various domains such as psychology, sociology, medicine, biology, linguistics, and computer sciences. The rate of data generated in these application domains is in increasing order. Hence, there is a strong need for algorithms that scale well with the growing data and run-on commodity hardware clusters rather than requiring high-performance systems. This thesis discusses the enumeration of formal concepts from a large dataset with minimum intent and extent size. The problem of computing concepts has a close correspondence with the enumeration of the maximal bicliques from a bipartite graph and closed itemsets in association rule mining. We will use this parallel to turn the formal context into an adjacency matrix of a bipartite graph. The bigraph is clustered into smaller subgraphs that hold the 2-hop projection for each node. Each subgraph is processed in parallel to yield formal concepts within its cluster. These results are filtered to yield global formal concepts. Additionally, we have presented a couple of techniques to avoid handling redundant 2-hop projections. Finally, we discuss the special case of handling dense 2-hop projections (hubs) with an example of a real-world dataset. To our knowledge, this is the first work on decomposing these hubs into smaller ones to generate formal concepts.

Ph.D., Antioch University, 2021, Antioch New England: Marriage and Family Therapy

Parents of non-binary children undergo profound changes as they learn to first understand and then support their child. In order to provide family therapists with a foundation from which to work with these families, a thorough review of the literature is provided as well as a narrative research study. Chapter one provides an introduction to the topic of non-binary gender and transgender identities, defines the terms non-binary, transgender, and transsexual, and clarifies the conceptual frameworks at use in the dissertation: social constructionism and transfeminism. Chapter two is a review of peer-reviewed literature on therapy with children and adolescents who identify as transgender or non-binary. Special attention is paid to what knowledge is produced and challenged within each methodological category, and how non-binary youth and their families are described or excluded from the discussion overall. Chapter three is a narrative research study that answers the question: what are the stories parents of non-binary children tell about how they came to understand and affirm their child? Stories of parents confronting core beliefs, stepping into leadership, and feeling like they do not fit in or belong in ostensibly supportive spaces are presented. Relevance to narrative therapy is highlighted as well as the need for more research on family processes that enact support for transgender and non-binary children.

Master of Science, The Ohio State University, 2021, Computer Science and Engineering

Formal verification is a method of proving program correctness based on formal specifications and using mathematics. The goal of this study is to formally verify by mathematical proof the correctness of a Java implementation of a Binary Decision Diagram (BDD). Specifically, the verification of the implementation proves its correctness and makes it significantly less susceptible to errors, crashing, and undiscovered bugs that could be exploited. This verified BDD implementation can then be used to solve a wide variety of problems with a higher level of confidence than would be possible with an unverified implementation. The formal specification of the Java-based BDD component verified in this project was used to prove the correctness of a reference implementation. Each method in this reference implementation was represented in a reasoning table, and a rigorous set of proofs were written for each verification condition in the reasoning tables. These proofs and the method reasoning tables, taken together, form a single proof that establishes the correctness of the BDD reference implementation as a whole. In the development of formal correctness proofs for each method in the BDD component, several errors in the implementation and specifications were discovered and corrected. These methods had been tested using a comprehensive set of test cases but errors were discovered during the formal verification process, exhibiting the value of the proofs. An additional limitation related to the testing capabilities of the component design pattern used in the Java-based BDD component was also discovered in this work. The results of this thesis mean users can be confident in using this implementation based on its proven correctness.

Master of Fine Arts, The Ohio State University, 2021, Art

Monsters are powerful symbols of transformative agency, heavily ingrained in Western culture. With transmutating creatures living rent-free in our collective imagination, I have to wonder: why is it taboo for queer people to transform? Tracing a historical line from biblical angels, Greek mythology, the gothic novel, and contemporary horror cinema, I create a framework for understanding monsters as revered, transformative figures in important texts throughout the centuries. Just as LGBTQ+ activists reclaimed `queer' as a radical identifier, I reclaim `monster' as an uncompromising symbol of bodily agency, engaging with Queer readings and critical media theory along the way. Using my MFA Thesis artwork God Made Me (And They Love Me), I weave my soft sculpture beasties through historical imagery, religious text, folklore, and media pieces depicting `monster' and `monstrosity'.