PhD, University of Cincinnati, 2022, Engineering and Applied Science: Computer Science and Engineering

Identification of the mechanism of action (MoA) of a small molecule which causes pharmacological effects on cellular networks governing gene expression levels is an important field of study for the purpose of drug development and repurposing. While gene expression can be used for the prediction of small molecule MoA using traditional machine learning algorithms, these algorithms do not consider the underlying complexity of cellular level biological networks driving gene expression. In particular, capturing predictive features from the polarity of interaction in cell signaling networks where nodes in the network either activate or inhibit other nodes is still a challenging problem for the prediction of drug MoA. We propose an ensemble deep learning meta-algorithm for predicting small molecule MoA from gene expression data using unsigned and signed graph convolutional networks (GCN). We developed a GCN algorithm to extract features from signed networks and combined predictive probabilities with that of an unsigned GCN using stacking. Our ensemble methodology improves the overall predictive capabilities significantly when compared to unsigned or signed GCN.

Doctor of Philosophy, The Ohio State University, 2021, Electrical and Computer Engineering

The fields of computer vision and machine learning have made enormous strides in developing models which solve tasks only humans have been capable of solving. However, the models constructed to solve these tasks came at an enormous price in terms of computational resources and data collection. Motivated by the sustainability of continually developing models from scratch to tackle every additional task humans can solve, researchers are interested in efficiently constructing new models for developing solutions to new tasks. The sub-fields of machine learning devoted to this line of research go by many names. Such names include multi-task learning, transfer learning, and few-shot learning. All of these frameworks use the same assumption that knowledge should be shared across models to solve a set of tasks. We define knowledge as the set of conditions used to construct a model that solves a given task. By shared knowledge, we are referring to conditions that are consistently used to construct a set of models which solve a set of tasks. In this work, we address two sets of tasks posed in the fields of computer vision and machine learning. While solving each of these sets of tasks, we show how each of our methods exhibits a novel implementation of shared knowledge leading to many implications for future work in developing systems that further emulate the abilities of human beings. The first set of tasks fall within the sub-field of action analysis, specifically the recognition of intent. Instead of a data-driven approach, we construct a hand-crafted model to infer between intentional/non-intentional movement using common knowledge concepts known by humans. These knowledge concepts are ultimately used to construct an unsupervised method to infer between intentional and non-intentional movement across levels of abstraction. By layers of abstraction we mean that the model needed to solve the most abstract instances of intent recognition, is useful in developing models whi (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2019, Anatomy

Many undergraduate students enroll in gross anatomy courses to support future academic success. Therefore, gross anatomy education at the undergraduate level is tasked, in part, with preparing students for subsequent graduate and professional anatomy instruction. A current trend in anatomy education at the medical professional level is a reduction of hours allotted to gross anatomy curricula. Alternative pedagogies are becoming increasingly necessary as time devoted to gross anatomy education declines and hours are disproportionately allocated across gross anatomy lecture and laboratory components. Modernizing gross anatomy instruction by adapting alternative pedagogies at the undergraduate level may function to facilitate reduced required curricular hours while maintaining effective anatomy instruction that supports future gross anatomy encounters for students at all levels of experience. The purpose of the current study was to apply the instructional design principles of a blended active learning intervention to the gross anatomy education of undergraduate students. Blended learning models are characterized by a combination of traditional in-person instruction and technology-mediated online learning. The blended learning model implemented in the current study was derived from inverted lecture delivery, flipped classroom, and flipped learning. A flipped classroom typically consists of a pre-recorded lecture delivered online as preparatory work prior to attending class. In the flipped classroom students are restricted to one form of recorded lecture selected by the instructor. This differs from the inverted lecture delivery method, which employs multiple means of lecture transmission self-selected by the student based on their perceived learning style. A multi-modal approach to gross anatomy educational research was utilized in which the ADDIE instructional model and action research stages were integrated for the current study. Action research was of interest for th (open full item for complete abstract)

Doctor of Philosophy, Case Western Reserve University, 2019, Management

As digital technologies transform the economy, more teams than ever before are developing digital products in environments that are dynamic and unfamiliar to them. A digital product team typically does not a priori possess all the knowledge required to appropriately interpret and respond to its environment. Therefore, the team's ability to learn fast and continuously—to acquire as-required new knowledge and change cognition and action as a result—becomes critical to its success. Relative to traditional new product development (NPD), digital NPD is idiosyncratic, and consequently, team learning in digital NPD is different from team learning in traditional NPD. This research inquiry aims to develop a preliminary understanding of the emerging and poorly understood socio-technical phenomenon of team learning in digital NPD. The inquiry frames learning as a technology-constituted process and uses an unorthodox mixed methods research design—consisting of concurrent qualitative and quantitative studies that partially inform a third design science study—in order to explore three key facets of the multi-level phenomenon. Each study examines a different facet, is situated at a different level, and characterizes a different learning process mechanism. Meta-inferences are then inductively and abductively derived to provide a more generalized understanding of the phenomenon. Study 1 uses constructivist grounded theory development to discover an individual-level process mechanism called senseshaping used by the NPD team's product manager to craft a team response to a trigger event. Study 2 uses structural equation modeling to characterize the effects of the popular team-level design thinking mechanism used by the NPD team to learn and develop new products. Study 3 uses design science research methodology to develop and evaluate a technology-constituted team-level process mechanism called the Product-Assisted-Learning (PAL) Loop that enables learning from product feedback. Inte (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

By combining different facial muscle actions, called Action Units (AUs), humans can produce an extraordinarily large number of facial expressions. Computational models and studies in cognitive science have long hypothesized the brain needs to visually interpret these action units to understand other people's actions and intentions. Surprisingly, no studies have identified the neural basis of the visual recognition of these action units. Here, using functional Magnetic Resonance Imaging (fMRI), we identify a consistent and differential coding of action units in the brain. Crucially, in a brain region thought to be responsible for the processing of changeable aspects of the face, pattern analysis could decode the presence of specific action units in an image. This coding was found to be consistent across people, facilitating the estimation of the perceived action units on participants not used to train the pattern analysis decoder. Research in face perception and emotion theory requires very large annotated databases of images of facial expressions of emotion. Useful annotations include AUs and their intensities, as well as emotion category. This process cannot be practically achieved manually. Herein, we present a novel computer vision algorithm to annotate a large database of a million images of facial expressions of emotion from the wild (i.e., face images downloaded from the Internet). We further use WordNet to download 1,000,000 images of facial expressions with associated emotion keywords from the Internet. The downloaded images are then automatically annotated with AUs, AU intensities and emotion categories by our algorithm. The result is a highly useful database that can be readily queried using semantic descriptions for applications in computer vision, affective computing, social and cognitive psychology. Color is a fundamental image feature of facial expressions. For example, when we furrow our eyebrows in anger, blood rushes in and a reddish color (open full item for complete abstract)

EdD, University of Cincinnati, 2017, Education, Criminal Justice, and Human Services: Urban Educational Leadership

The goal of this action-oriented case study research is to illuminate and articulate the history and complexity of service learning at the University of Cincinnati as the program has evolved over time. Narrative inquiry and document interrogation were employed to solicit lived experiences and stories from a variety of both campus and community stakeholders, which were then analyzed through the theory of structuration. This study will strengthen the existing pool of institutional research of the social structuration of service learning programs in higher education, including how leaders may foster collaborative experiences and broadened subjectivities for all relevant stakeholders. Through detailing key watershed moments that have underscored the program's evolution, this study has illuminated important additions to theory, which may have implications for other service learning programs, for the field of urban education leadership, and for literature pertaining to campus-community organizing.

Master of Arts (M.A.), University of Dayton, 2015, Psychology, Clinical

The present study examined student outcomes across a semester of service-learning participation. The study examined two hypotheses: (1) students engaged in service-learning will have significant changes in community service self-efficacy (an in the related civic action construct) and in endorsement of myths and social stigma towards homelessness; and (2) the pre-to-post semester improvements will be greater for students engaged in structured DEAL Model reflection compared to students engaged in the less structured routine reflection. Undergraduate students (N= 30) were randomly assigned to either the DEAL Model reflection or routine reflection condition. Over the course of the semester, students were required to complete four reflections exercises, which differed in structure based on condition. While there were a number of nonsignificant findings, there was partial support for the hypotheses. Specifically, students' endorsement of myths and social stigma significantly decreased from pre-to-post assessment. Further results indicated that the DEAL Model reflection group had a significant decrease in endorsement of myths and social stigma, while the routine reflection did not have this significant decrease. Additionally, the DEAL Model reflection group had a significant increase in civic action from pre-to-post semester assessment. High pre-semester scores on community service self-efficacy measures may have created a ceiling effect that precluded an adequate assessment of pre- to post-semester changes in that construct. However, a retrospective measure of this same construct indicated that students strongly endorsed the notion that participation in the service-learning project substantially contributed to their perceptions of strong community service self-efficacy. The results are interpreted within the context of past theory and research. Recommendations for future research are provided, including future examination of qualitative data (i.e., written ref (open full item for complete abstract)

PhD, University of Cincinnati, 2013, Engineering and Applied Science: Electrical Engineering

Animals such as reptiles, amphibians and mammals, including humans, have a very complex mechanical body structure. The number of degrees of freedom in humans is estimated to be between 500 to 1400. Even the simplest actions performed by humans involve processing at multiple levels of the nervous system to control numerous degrees of freedom at the musculoskeletal level. Yet, humans and other higher animals can perform a large number of complex, goal-directed movements under a variety of different environmental conditions. Understanding this phenomenon is a great interest to biologists, engineers and computer scientists. Some of the important issues that need to be addressed are: What is the control strategy employed to handle such large degrees of freedom? How is this control strategy instantiated in the neural and musuloskeletal substrate of the animals? How is this control strategy used to develop skill learning? Extensive studies addressing the first question have revealed that, rather than using standard control-based methodologies involving continuous tracking of trajectories, animal movements emerge from the controlled combinations of pre-configured movement primitives known as motor synergies. These synergies are stereotypical patterns of activity across muscle groups and can be triggered as a whole with a controlled gain and temporal offset. The co-activity of a small set of synergies can produce a large repertoire of movements, thus greatly simplifying the control problem. Although the presence of motor synergies has been confirmed by extensive experimental studies on animal and human movements and the concept of movement primitives has been used for the control of fairly complex robots, the neural basis of motor synergies is still not well understood and there are no comprehensive neural models for them. In this dissertation, a hierarchical, modular neural model for motor synergies is introduced based on the principle that these functional modules r (open full item for complete abstract)

PHD, Kent State University, 2010, College of Education, Health and Human Services / School of Teaching, Learning and Curriculum Studies

The purpose of the study was to understand transformative learning in teachers' critical colleague relationships while participating in online, collaborative action research. This included generation of theory by teacher action-researchers relevant to their own practice, with a view to teacher transformation over time. The study addressed the primary research question, “What is the potential of critical colleague relationships to transform teacher action- researchers in a shared online learning environment?” The participants in the study were 11 in-service teachers in school districts in northeast Ohio, working in four critical colleague groups in online action research situations. Two of these groups were involved in a grant project through eTech Ohio, a professional development arm of the Ohio Department of Education, with the goal of integrating emerging technologies into their respective teaching practice. The other two groups were students in a master's degree program in cultural foundations of education at a mid-sized, research-based, Midwestern university; the online action research course for this group was a capstone project for their program. Earlier studies of the two courses were conducted prior to the dissertation research. Data collection consisted of transcripts of the online participation in both action research courses, followed by focus group and individual interviews. Data analysis used a grounded theory approach, based on Strauss and Corbin (1998) and situational analysis and mapping as developed by Clarke (2005). Findings included “inclusional flow” as the central category of interest, along with its subcategories of inclusional survival, vision, and liberation. This state of inclusional flow empowered the participants to engage in the process of interchanging perspectives and “sparking” new conversations, which led to positive, long-term effects on teacher practice, along with transformative learning in teacher professional development in both (open full item for complete abstract)

Ph.D., Antioch University, 2008, Leadership and Change

Implicit in leadership behavior is the ability to work with others, to be in relationship, and to collaborate. Contemporary theories about leadership have shifted from a focus on the individual “leader” toward the collective act of “leadership.” A concrete understanding of collaborative leadership remains somewhat underdeveloped in the literature and theoretically. This dissertation is a case study of organization's efforts to change from autocratic organizational leadership to a more collaborative working environment. Taking the form of a literary portrait, the study analyzes an example of action learning about collaborative leadership. The portrait will be of the agency's change, with special attention given to the issues facing the leadership team as it wrestles to change from top-down to collaborative leadership practice. The primary research question is: In today's shifting landscape, what practices and conditions will optimize the development of a collaborative working environment? Findings were that the development of a collaborative working environment can be optimized through the careful cultivation of the ten themes that emerged from the study: (1) on-going learning and continuous development, (2) flexibility, (3) trust, (4) respect/esteem/ positive regard, (5) willingness/commitment, (6) facilitative process (establishment of norms, ground rules/agreements, inclusivity, process capability/tacit knowledge of functional group process), (7) realistic optimism/positive personality/resilience/solution/strength/future focus, (8) communication skills, (9) social intelligence (ability to transcend the ego and to self-organize and motivate) and (10) an appropriate level of technical competence. The electronic version of this dissertation is available at the Ohiolink ETD Center http://www.ohiolink.edu/etd.

Doctor of Education (Ed.D.), Bowling Green State University, 2013, Leadership Studies

The purpose of this mixed methods embedded design study was to learn if engaging in a problem-based service-learning course could improve the demonstration of behaviors reflecting positive character traits in junior and senior high school students who are considered at-risk. Additionally, the investigation sought to determine the extent to which students could articulate the applicability of the problem-based service-learning course to their lives. A problem-based learning approach is one where students learn about a topic in the context of solving real-life problems. The service-learning methodology links academic learning to service that meets an authentic community need (Billig, 2002). This study successfully implemented a proactive curricular approach in an attempt to deter negative student behaviors as students learned how to display positive character traits in different situations. This embedded design study utilized mostly qualitative data with a quantitative component. The study was guided by three research questions and student discipline data was collected from both an experimental and control group. In addition to the discipline data, students were interviewed, observed, and they completed course assignments to assess whether students improved their behaviors reflective of positive character traits by developing their social skills, problem-solving skills, and coping skills over a one semester term. The experimental group completed the course while the control group did not. The first question researched was (a) Does participation in a problem-based service-learning course reduce behavior incidents requiring discipline intervention of junior and senior high school students considered at-risk? This question was answered through analyzing quantitative data. The second question (b) Does participation in a problem-based service-learning course improve behaviors reflective of positive character traits of junior and senior high school students c (open full item for complete abstract)

Doctor of Education (EdD), Wright State University, 2024, Leadership Studies

This study explored the experiences and perceptions of faculty members at an institution of military education participating in a Faculty Learning Community (FLC) focused on educational technology utilization during the 2022-2023 academic year. As the Department of Defense (DoD) emphasizes the importance of technology capabilities and workforce development, understanding faculty experiences in these settings becomes crucial for effective technology integration in teaching and learning. Utilizing a participatory action research (PAR) design, the study collected data through faculty artifacts, researcher reflexive journals, semi-structured interviews, bi-monthly meeting minutes, videos, and presentation materials. The research questions focused on the change of participants' understanding of how technology supports teaching practices, and their experiences in an educational technology learning community. Findings from this study provide insights aimed at facilitating effective practices for faculty development and contribute to the ongoing conversation about technology integration in military education settings.

PhD, University of Cincinnati, 2022, Education, Criminal Justice, and Human Services: Educational Studies

This four-article dissertation is about fostering learning relationships online during the COVID-19 pandemic with undergraduate teaching fellows and adolescent learners enrolled in a summer enrichment program (SummerSpark, pseudonym). I, along with six undergraduate students (teaching fellows), created a relational learning community (RLC) to deepen our teaching practice and process the impact of COVID-19 on our lives and the lives of the adolescent students enrolled in the program while developing strategies to improve relational connections with learners. Chapter 1 is an introduction to a pedagogical framework, Holding SPACE, developed to teach the emotional and relational concepts and principles that are known to effectively develop a thriving learning community. Chapter 2 describes how we created our RLC and the relational rituals that grounded and supported our learning. Chapter 3 is a co-authored paper on the teacher action research project that resulted from our RLC and the recommended strategies and action to deepen connections with learners online. Chapter 4 is a portrait that describes how a teaching fellow experienced, navigated, and understood learning relationships online using Lawrence-Lightfoot's portraiture and Mears' poetic narrative methodologies. This research brings to the fore the voices of undergraduate students learning to become teachers in a time that dramatically disrupted the educational field. Their stories and our teacher action research, frameworks, practices, and tools to support learning relationships online are shared, deepening and enriching the literature on relational learning.

PhD, University of Cincinnati, 2023, Education, Criminal Justice, and Human Services: Information Technology

In modern urban landscape, the safety and efficiency of both home and city environments are paramount. Action Recognition (AR) has emerged as a pivotal technology to enhance these domains, particularly in the realms of Smart Home and Smart City applications. This dissertation delves into the intricacies of AR, underscoring its transformative role in monitoring and ensuring safety across diverse contexts. Diving into the realm of deep learning, its transformative impact on action recognition over recent years becomes evident. Notwithstanding these advances, inherent challenges remain, particularly when addressing specific AR tasks that rely on limited datasets. To navigate these complexities, our research introduces a novel, resource-efficient framework combining transfer learning techniques with Conv2D LSTM layers for tasks such as Smart Baby Care. This initiative resulted in the creation of a benchmark dataset and an automated model tailored for recognizing and predicting baby activities, setting new standards in computational efficiency and performance. From the intimate confines of smart baby care within homes, we broadened our lens to encompass the bustling streets of urban landscapes. Complementing home safety, the safety of these urban environments became a pivotal focus of our research. Through an empirical analysis, we delved into the intricacies of accident detection. Identifying and analyzing prevailing techniques, taxonomies, and algorithms showcased the central role of AR in accident detection and autonomous transportation. Furthermore, by leveraging data from reputable sources like the NHTSA Crash Report Sampling System, we provided a holistic view of traffic accident trends, underlining the dire need for robust accident detection systems. Our seminal contribution is the introduction of the I3D-CONVLSTM2D model architecture, uniquely designed for accident detection in smart city with 87% mean average precision and 80% for detecting traf (open full item for complete abstract)

EDD, Kent State University, 2023, College of Education, Health and Human Services / School of Teaching, Learning and Curriculum Studies

This dissertation study explores the implementation of project-based learning (PBL) in first-grade classrooms and its impact on student engagement. The study emphasizes the importance of collaborative grade-level teams and teacher support in successfully implementing PBL. The study adopts a mixed-methods approach and takes place in a small rural district in the Midwestern United States. The findings indicate that collaborative grade-level teams have a positive impact on teachers' ability to implement PBL. While the quantitative data collected showed no statistical significance in the differences in motivation from the beginning to the end of the study, teachers perceive PBL as valuable and recognize its benefits in promoting student engagement. Student feedback demonstrates increased engagement, enjoyment, and growth in critical thinking and collaboration skills. The study highlights the need for ongoing professional development and support for teachers in PBL implementation. It suggests creating dedicated spaces to highlight student work and integrating PBL into early grades while addressing foundational skills. Further research is recommended to investigate long-term effects and the benefits of PBL across different grade levels and subject areas. The study provides insights into building a collaborative team approach to PBL and encourages teachers to engage in practitioner inquiry to improve their practices and increase student learning and engagement.

PhD, University of Cincinnati, 2023, Education, Criminal Justice, and Human Services: Educational Studies

This study evaluated the experiences of faculty-practitioners, graduate students, and a community partner within the context of a professional development (i.e., career education) course at a large, research-intensive institution. Over the course of a 16-week academic semester graduate information technology students worked in collaborative project teams to develop information technology-related resources for a 501(c)(3) not-for-profit organization located in Cincinnati, Ohio. Students worked in three separate teams charged with aiding the not-for-profit organization with (1) website development/enhancement, (2) mobile application consultation, and (3) database design. This study considered the impact of practice-based learning (PBL) through project assignments contributing to enhanced professional development pedagogy for instructors, heightened student career preparation, and reciprocity by providing access to information technology-related resources for the community-based organization while simultaneously aiding students in gaining professional experience. An action research approach was applied to the study's framework thereby involving multiple stakeholders in aspects of the study from inception to completion. Additionally, the study sought to create sustainable curricular change for the professional development curricula associated with the research. Findings of the study indicated students in the course said they were prepared to enter the workforce, course instructors claimed they could teach the course more effectively in the future, and the community-partner experienced benefits from access to new technology resources.

Doctor of Education , University of Dayton, 2023, Educational Leadership

This participatory action research study took the conceptual framework of Social Emotional Learning (SEL), collaboration merging through solid relationships to analyze the realities of the teacher on special assignment (TOSA) role and the job description. This study involved individual interviews and a focus group; both were coded using inductive coding. The results of this study revealed themes of SEL, collaboration, relationships, transitional needs, and future job recommendations. In collaboration with the director of student support services, a two-goal action plan centered around continuing SEL growth and improving this role in the future was made. Due to financial needs that hold significant impact, Westview cannot consider this action plan as their focus and area of need do not align at this time.

Doctor of Education , University of Dayton, 2023, Educational Administration

The Education Center serves many adult learners that have the goal of receiving their high school equivalency. Adult learners face many barriers when pursuing this goal. The research in adult education focuses on the barriers that learners face and what drives them to succeed. There is limited research on what learners' perception of knowledge is. This study was conducted utilizing five case studies to explore learners' lived experiences and how those experiences affected their perceptions of knowledge. Findings showed that the barriers and motivations that they experienced affected how they perceived what success was and that the organization and its instructors need to take into consideration all individual learners experiences, barriers, and motivations to create individualized learning plans. An action plan was created to guide the organization towards creating an equitable learning environment and to improve success rates for all learners.

Ph.D., Antioch University, 2023, Leadership and Change

This study is aimed at understanding how youth leadership development programs can be more inclusive and promote a broader range of leadership values, qualities, and behaviors by focusing on young people who have been disaffected by leadership development programs. The study design was intended to provide a creative space for youth to engage in meaningful conversations about their evolving concepts and expectations of leadership. Using critical youth participatory action research to engage a group of youth, cohort members co-created a new youth leadership development program while addressing their identified challenges and needs. Findings from this study highlight the importance of adults in youth programs and provide insights toward an ecosystem approach to youth leadership development. Practitioners, funders, and community leaders can create more inclusive and meaningful youth development opportunities and programs by understanding the youth program's ecosystem. This dissertation is available in open access at AURA (https://aura.antioch.edu/) and OhioLINK ETD Center (https://etd.ohiolink.edu).

Master of Computer Science (M.C.S.), University of Dayton, 2022, Computer Science

Reinforcement Learning (RL) is an area of machine learning that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. NetHack presents a challenging problem for RL. It has a very large action space and multimodal observation space while requiring an agent to be capable of planning hundreds of thousands of timesteps to achieve a difficult goal. MiniHack is presented by Facebook AI Research to provide a testbed to develop incremental solutions toward the monumental goal of completing an ascension in NetHack. It presents a powerful framework for designing RL environments in procedurally generated worlds. Toward success in MiniHack, this thesis describes a method for masking actions to reduce the action space of agents. This thesis shows that masking actions can provide an effective means to artificially reduce the action space of any agent. Reducing the action space has been shown to increase the sample efficiency of agents in environments with large action spaces to few relevant actions.