Category: Graduate

Javad Jomehpour: Listen to Scooter Riders

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https://youtu.be/Fw6sCnEMRPc

In this study, app store reviews from two major micro-mobility companies are investigated using machine learning techniques to identify the factors that influence rider satisfaction. The Latent Dirichlet Allocation model is applied to over 12,000 rider-generated reviews to identify twelve topics discussed within the reviews. These topics cover areas such as pricing, safety, customer service, refund, payment, app interface, and ease of use, to name a few. Using logistic regression, the most significant factors influencing rider satisfaction were identified. Moreover, name-based gender prediction analysis is employed to identify rider gender and then discover differences in review content and factors of satisfaction across gender. Findings contribute to the existing literature by demonstrating the use of app store reviews in a transportation mobility study. The development of a method to assess factors contributing to user or rider satisfaction offers the ability to evaluate e-scooter rider needs and barriers to access and participation.

Javad Jomehpour
Program: PhD in Civil and Environmental Engineering
Faculty mentor: Janille Smith-Colin

Konstantinos Kalfas: The dynamics of utility poles by considering soil-structure interaction (SSI)

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Winner: Civil and Environmental Engineering (Graduate)

Co-author: Nicos Makris

https://youtu.be/qbSOL76aK-8

Motivated from the large number of transmission and distribution utility poles that experience excessive vibrations during wind storms, this work sheds light to the eigenvalue analysis of a partially embedded flexural, prismatic column with embedded length, L, and exposed length, h, and accounts for the interaction with the soil of its embedded portion. The presentation will show that the dynamics of a partially embedded prismatic column depend solely on the embedment ratio, ε = L/h and a dimensionless stiffness that expresses the relative stiffness between the soil surrounding the embedded length (level of fixity) and the exposed portion of the flexural column. A partially embedded prismatic column exhibits a finite number of eigenmodes that are lower than its rigid-body mode; while, the associated eigenfrequencies are lower than the corresponding eigenfrequencies of the fixed-end cantilever. For a typical value of the embedment ratio ε = L/h = 0.15, the study uncovers that for any eigenmode n > 3, of the fixed-end cantilever, the partially embedded, prismatic column exhibits n + 1 eigenmodes. These rich dynamics result from soil-structure interaction and are associated with the way that the flexural patterns of the partially embedded column emerge from the ground.

Konstantinos Kalfas
Program: PhD in Civil and Environmental Engineering
Faculty mentor: Nicos Makris

Chengyu Ke: Iteratively Reweighted Group LASSO Based on Log-composite Regularization

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Co-authors: Miju Ahn, Sunyoung Shin and Yifei Lou

https://youtu.be/6f52fbME99A

This paper addresses supervised learning problems with grouping information on model coefficients given a priori. We focus on non-overlapping groups such that coefficients for each disjoint group shall be simultaneously either zero or nonzero. To deal with such group sparsity structure, we introduce a novel log-composite regularizer, which can be minimized by an iterative algorithm. In particular, our algorithm iteratively solves for a traditional group LASSO problem that involves summing up the L2 norm of each group until convergent. By updating group weights, our approach enforces a group of smaller coefficients from the previous iterate to be more likely to set to zero, compared to the group LASSO. Theoretical results include a minimizing property of the proposed model as well as the convergence of the iterative algorithm to a stationary solution under mild conditions. We conduct extensive experiments on synthetic and real datasets, indicating that our method yields superior performance over the state-of-the-art methods in linear regression and binary classification.

Chengyu Ke
Program: PhD in Operations Research
Faculty mentor: Miju Ahn

Tryna Knox: Reinventing Home, How Middle School Leaders Conceptualize STEM in the Context of Structured Program Implementation

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https://youtu.be/7CyzzNbTFuw

The purpose of this research was to examine how middle school leaders conceptualize STEM education program implementation and to more fully understand how their leadership practices are enacted within their situational context of an intensive structured STEM education program, subsequently referred to as the STEM Academy. The STEM Academy, a four-year project launched in 2016 was designed to promote STEM interests by developing teachers and leaders through intensive summer academies and providing coaching support throughout the school years. Findings reveal that school leaders conceptualize STEM education in similar ways across different campuses and describe challenges and barriers faced while navigating internal and external expectations during implementation of the STEM Academy. While some common themes emerged that led to interpretations across the four cases, each leader contributed a unique lens and perspective and no single, absolute, truth was ascertained from this analysis.

Tryna Knox
Program: PhD in Education
Faculty mentor: Leanne Ketter-Geller

Nancy Le: Moderated Mediation Analysis with Dichotomous Outcome and/or Mediator Variables

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https://youtu.be/s8srz0NfmuA

In this presentation, I will provide a brief history of moderated mediation analysis, introduce the types of moderated mediation models, and explain how moderated mediation models work with two types of variables: continuous mediator/outcome variables and categorical mediator/outcome variables. Moreover, I will explain why we need to treat categorical outcome variables differently, and provide an example using moderated mediation model with dichotomous outcome variable from the literature.

Nancy Le
Program: PhD in Education
Faculty mentor: Akihito Kamata

Bo Li: Chemiluminescent H2S and peroxynitrite probes synthesis

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Co-authors: Lucas Ryan, Briley Bezner and Husain Kagalwala

https://youtu.be/LHoCo4omytM

Hydrogen sulfide (H2S) and peroxynitrite are important biological signaling molecules that have been recognized alongside nitric oxide and carbon monoxide which could impact multiple physiological functions. To detect them, there has been a focus on developing fluorescent probes to target particular analytes; however, fluorescent probes lack sensitivity and depth penetration due to background autofluorescence and light scattering. Chemiluminescence does not require light excitation, which greatly reduces the amount of autofluorescence and photoactivation. In order to detect them in living systems with high sensitivity, a series of sterically stabilized 1,2-dioxetane chemiluminescent reduction-reaction based hydrogen sulfide probes have been synthesized.

Bo Li
Program: PhD in Chemistry
Faculty mentor: Alex Lippert

Lizuo Liu: Multiscale DNN for Stationary Navier Stokes Equations with Oscillatory Solutions

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Co-authors: Bo Wang, Wei Cai

https://youtu.be/VBxKOu0j2ug

In this talk, we develop new multi-scale deep neural network to compute oscillatory flows for stationary Navier-Stokes equation in complex domains. The multiscale neural network is the structure that can convert the high frequency components in target to low frequency components thus accelerate the convergence of training of neural network. Navier-Stokes flow with high frequency components in 2-D domain are learned by the multiscale deep neural network. The results show that the new multiscale deep neural network can be trained fast and accurate.

Lizuo Liu
Program: PhD in Mathematics
Faculty mentor: Wei Cai

Jiapeng Liu: Solve inverse problem with physics assisted Deep Learning

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https://youtu.be/xDllNa3txfg

Deep learning has been widely used for solving ill-posed inverse problem in the past few years. With sufficient training data, data-driven deep neural networks can serve as good approximation of mapping between input and solution. However, training set can be expensive to obtain, and consequentially, the network may not generalize well. Starting with the Deep Image Prior by Ulyanov et al, convolutional neural networks are proven to be strong prior that can solve inverse problems without training data. Recent works have explored the notion of using dataset to supervise the training process, physics priors can be added to the network architecture for even stronger constraints, and the input will be self-supervising the network. By iteratively updating the weights of the network the output generated by the network is forced to satisfy the physical model and that converges to the correct solution.

Jiapeng Liu
Program: PhD in Electrical Engineering
Faculty mentor: Prasanna Rangarajan

Cameron Matson: Benefits of MIMO in 3D UAV-to-UAV topologies

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Winner: Electrical Engineering (Graduate)

Co-authors: Syed Muhammad Hashir, Sicheng Song

https://youtu.be/Pf7At2Bz_AU

Unmanned Aerial Vehicles (UAVs) often lack the size, weight, and power to support large antenna arrays or a large number of radio chains. Despite such limitations, emerging applications that require the use of swarms, where UAVs form a pattern and coordinate towards a common goal, must have the capability to transmit in any direction in 3D space from moment to moment. In this work, we design a measurement study to evaluate the role of antenna polarization diversity on single-antenna and multi-antenna UAV systems communicating in arbitrary 3D space. To do so, we construct flight patterns where one transmitting UAV is hovering at a high altitude (80 m) to focus on the impact of heterogeneous drone-based antenna polarization without multipath effects. Then, a receiving UAV hovers at 114 different positions that span an ellipsoid surrounding the transmitting UAV with a radius of approximately 20 m along equally-spaced elevation and azimuth angles. To understand the role of diverse antenna polarizations and multiple antennas, both UAVs have a dedicated radio chain to a horizontally-mounted antenna and a dedicated radio chain to a vertically-mounted antenna, creating four wireless channels. With this measurement campaign, we seek to understand how single-antenna systems could optimally select an antenna orientation or multi-antenna systems could have the greatest gains.

Cameron Matson
Program: Master’s in Electrical Engineering
Faculty mentor: Joe Camp

Emily McClelland: Why men get higher than women: The biology and mechanics of sex differences in jumping performance

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https://youtu.be/-IJPdvT7QiQ

Men clearly outperform women in athletic events that require moving their body through space. However, the magnitudes of the difference varies by event. For Olympic running events men run 10-12% faster than women in events ranging from 100 meters to the marathon. In contrast, for the high jump men outperform women by nearly twice the running offset (20%), and in the conventional countermovement jump the sex difference is greater yet at 25%. These observations suggest an interaction between event mechanics and bodily differences between males and females. Running differences are well explained by close alignment between the additional proportion of the female body comprised of fat (+10%) with the 11% difference in performance. However, it is unclear why jumping performance differences are so much greater. Jumping differs from running in that performance depends largely on a single powerful contraction on take-off. Here, we evaluated the possibility that due to the mechanical differences between running and jumping, sex differences in height also factor into the sex difference in jumping performance. Results to date suggest that sex differences in height and body composition do account for the male-female jump differences. Therefore we conclude that sex differences in athletic performance are set by an interaction between bodily differences and mechanical demands of the event.

Emily McClelland
Program: PhD in Education-Applied Physiology
Faculty mentor: Peter Weyand

Christina McConville: Peritectic phase transition of benzene and acetonitrile into a cocrystal relevant to Titan, Saturn’s moon

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Winner: Chemistry (Graduate)

https://youtu.be/avwu36FZq4g

Titan, Saturn’s largest moon, is the only body in the solar system known to have stable bodies of liquid—lakes, rivers, and seas—that undergo dynamic processes similar to Earth’s hydrological cycle. To study the potential formation of minerals on Titan, we use a combination of structural characterization methods including high-resolution synchrotron powder X-ray diffraction (PXRD) and differential scanning calorimetry (DSC) to analyze the constituents present on Titan's surface and evaluate their potential for cocrystal formation. Among the compounds detected on the surface of Titan are two common laboratory solvents: benzene and acetonitrile. Here we report the phase diagram of mixtures of acetonitrile and benzene, which features incongruent melting and a peritectic phase transition of solid benzene and liquid acetonitrile into a 1:3 acetonitrile:benzene cocrystal. The crystal structure of this cocrystal was solved and refined from in situ diffraction data using synchrotron radiation. Additionally, to mimic the environment on Titan more accurately, we tested the stability of the structure under liquid ethane. These results provide new insights into the structure and stability of potential extraterrestrial minerals and contribute to a better understanding of the surface composition of Titan.

Christina McConville
Program: PhD in Chemistry
Faculty Mentor: Tomče Runčevski

Rebekah Napier-Jameson: RNA binding proteins coordinately control lifespan in C. elegans

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Co-authors: Victoria Schatzman, Adam Norris

https://youtu.be/rnGsL5AALpM

In order to identify genes that coordinately control gene expression with important phenotypic consequences, we performed a CRISPR/Cas-9 based Synthetic Genetic Interaction (CRISPR-SGI) screen in C. elegans. We focused on conserved neuronally-expressed RNA binding proteins, and identified many double mutants with unexpected fitness defects. In one notable interaction between the MBNL1/2 ortholog mbl-1 and the ELAVL ortholog exc-7, double mutants displayed a severely shortened lifespan (~70% decrease). We have used RNA-Seq data to investigate which RNAs may be uniquely dysregulated in the double mutant. nhx-6, a predicted Na/H exchanger, which was identified from our RNA Seq data contributes to the phenotype and is expressed in the intestine. mbl-1 and exc-7 are neuronally-enriched genes. Initial experiments have shown partial rescue of the lifespan phenotype with mbl-1 re-expression in the nervous or intestinal tissues of the double mutant but not muscle tissue. Shortly we will be conducting experiments to test whether exc-7 expression in the nervous system is the critical tissue affecting whole-worm lifespan. Through these studies we hope to identify how these RNA binding proteins are contributing to the lifespan phenotype seen in the mbl-1;exc-7 double mutants.

Rebekah Napier-Jameson
Program: PhD in Biological Sciences
Faculty mentor: Adam Norris

Samantha Navarro and team: How might we improve procedural justice for the Dallas Police Department?

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Winner: Combined Non-STEM (Graduate)

Co-authors: Hope Anderson, Ramisa Faruque, Kaci McCartan

https://youtu.be/K0uZqO1yv_M

Master of Arts in Design and Innovation students, Hope Anderson, Ramisa Faruque, Kaci McCartan and Samantha Navarro are working directly with the Dallas Police Department over the spring 2021 semester to approach the design question: How might we improve procedural justice for the Dallas Police Department? Following seven-step Human-Centered Design process, we will ultimately test our hypotheses through a series of prototypes. Our design solution will be submitted to DPD and has the potential for real-world implementation. This semester presents a unique challenge of navigating sensitive social and political issues on the heels of a heightened Black Lives Matter Movement and push to defund the police across the nation.

Hope Anderson, Ramisa Faruque, Kaci McCartan, and Samantha Navarro
Program: Master of Arts in Design and Innovation (MADI)
Faculty mentor: Jessica Burnham

 

Jamie Nguyen: Sexual Victimization, Sexual Orientation, and Engagement in Hookups among College Women

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Co-authors: Ernest Jouriles; Renee McDonald; Priscilla Lui; Lynne Stokes

Sexual victimization (SV) is a major concern on college campuses in the United States (US), and sexual minority college women are particularly at risk. Unfortunately, little is known about what contributes to the disparity in SV rates between sexual minority and heterosexual women. Engagement in the hookup scene may be one factor that can help account for this disparity. The current study examined associations among SV, sexual orientation, and engagement in hookups in a sample of 977 college women from 12 campuses across the US. Based on previous research suggesting that banning alcohol on campus may reduce the risk of SV, the study also investigated whether campus alcohol policy influenced the associations. Weighted regression analyses indicated that comfort with and engagement in hookups accounted for 32% of the association between sexual orientation and SV. Regarding campus alcohol policy, sexual minority women on alcohol-free campuses were more likely than heterosexual peers to report SV. Future research should seek to replicate and extend these findings in order to understand why engaging in hookups matters and investigate if it is possible to mitigate those underlying mechanisms. Additional research is necessary to better understand the disparity in SV rates between sexual minority and heterosexual college women and how campus alcohol policy can affect SV in general.

Jamie Nguyen
Program: PhD in Psychology
Faculty mentor: Ernest Jouriles

Denise Patton: Asset-oriented parent engagement for supporting the use of decontextualized language with preschoolers in LSES Latino families

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Winner: Education: Ed.D. in Ed. Leadership

https://youtu.be/PXZyg2faGTs

Parent participation in their preschoolers' language development helps build kindergarten readiness, particularly parents' use of decontextualized language (DL), language about abstract concepts or that does not reference the here and now. Thus, various parent training methods about DL have been developed. Most studies evaluating parent use of or training in DL focus on educated, middle-class, white families and/or parent training programs that are deficit-oriented in their design. This qualitative study, therefore, focused on Latino families of low socioeconomic status (LSES) and positioned them as assets in their preschoolers' language development. Accordingly, phase one of the study consisted of interviewing four LSES Latina parents regarding how they used language when talking with their preschoolers, as well as what more they preferred to learn about preschoolers' language development and how they preferred to learn it. Phase one findings suggest that LSES Latino families do use DL with their preschoolers and in similar ways; however, their interests in further training content and methods varied, except for preferring technological applications for learning more. The phase two interviews will garner the subjects' feedback regarding the resource's effectiveness in its dual aim of being asset-oriented and growing them as developers of their preschoolers' language skills.

Denise Patton
Program: Ed.D in Education Leadership
Faculty mentor: Alexandra Pavlakis

Kelsey Paulhus: Leading with the head or heart? Deciphering the specific roles of Kv1.1 in cardiac function, epilepsy, and SUDEP

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Co-authors: Krystle Trosclair, Man Si, Megan Watts, Kathryn A. Hamilton, Md. Shenuarin Bhuiyan, Paari Dominic, Edward Glasscock

https://youtu.be/3MYWcIJ2aJ0

Mutations in ion channel genes with brain-heart expression patterns have been proposed as risk factors for sudden unexpected death in epilepsy (SUDEP) since they can cause both seizures and lethal cardiac arrythmias. One such gene is Kcna1, which encodes voltage-gated Kv1.1 potassium channel α-subunits. Kcna1 global knockout (KO) mice recapitulate many features of human SUDEP including frequent generalized tonic-clonic seizures that cause cardiorespiratory dysfunction leading to sudden death in about 80% of animals. Neuron-specific Kcna1 conditional KO (cKO) mice also exhibit premature death, epilepsy, and cardiorespiratory dysregulation, but to a lesser degree than global KOs, suggesting that Kv1.1-deficiency in the heart may cause intrinsic cardiac dysfunction that increases risk of mortality. Here we restrict Kv1.1 deficiency to heart tissue using a newly generated Kv1.1 cardiac cKO mouse to elucidate the contribution of Kv1.1 both to overall cardiac electrophysiology and how cardiac-specific deficiency contributes to the cardiac abnormalities and SUDEP risk seen in global KO and neuron-specific cKO mouse models. Our findings indicate that while Kv1.1 plays a functionally significant role in cardiomyocytes, the cardiac and sudden death phenotypes observed in the global KO and neuron-specific cKO mice are largely brain-driven.

Kelsey Paulhus
Program: PhD in Biological Sciences
Faculty mentor: Edward Glasscock

Mark Pierce and Celestina Rogers: How Schools, Shelters, and Service Providers Support Students Experiencing Homelessness During the COVID-19 Pandemic

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https://youtu.be/ShKgb_4WEd8

We have stumbled into one of the largest accidental experiments in the history of education. The COVID-19 pandemic forced districts to flip their traditional instructional programming from in person learning to distance and hybrid models. This research will be an embedded case study bounded by a conurbation of southern metroplex cities, suburbs, and rural areas encompassing 11 counties and a population of 7.5 million. ​It will consist of up to 50 semi-structured, open ended interviews with shelter workers, parents, teachers, administrators, counselors, and students 18 and older. The study will seek to understand how the COVID-19 quarantine affected the educational and social emotional lives of students experiencing homelessness in multiple settings and to ascertain how the future of distance learning may enhance social and organizational capital for students who are homeless and highly mobile. How can education writ large enhance the positive aspects of this event while mitigating the negative aspects to help fill in the gaps in distance learning and involvement for the benefit of homeless and highly mobile students? The evolving educational landscape that is responding to the pandemic may reveal how connectivity can enhance social and organizational capital as well as how it creates deficits in social and organizational capital.

Mark Pierce and Celestina Rogers
Program: PhD in Education
Faculty mentor: Alexandra Pavlakis

Robyn Pinilla: Creating a Bridge Between Research and Practice for Valid Assessment Use

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Winner: Education (Graduate)

Co-author: Elizabeth Adams

https://youtu.be/pLa9jEGlWBk

While the educational measurement and assessment community asserts that tests themselves are not valid but that inferences made based on scores require validation (Cizek et al., 2008; Kane, 2013), this message has not been well translated to educators. A test's development, purpose, and use should align to interpret scores and make informed decisions properly. However, usage in practice does not always align with intended purposes. Researchers could help prepare teachers to use tests in valid ways, specifically with novel assessment formats necessitated by the COVID-19 pandemic. Traditionally, the measurement and assessment community placed the onus of valid use and interpretation on the end-user, the teacher. We herein propose streamlining a process in which researchers make validity evidences accessible and understandable to teachers. We examine what sources of validity evidence support classroom assessment use, how teachers can access this information in a meaningful way, and what sources of validity evidence seem superfluous or missing from the extant literature. This research proposes a method for researchers to facilitate valid use and interpretation of tests by gathering sources of validity evidence in a practitioner friendly format to put the end-users, teachers, at the forefront of the test validation process.

Robyn Pinilla
Program: PhD in Education
Faculty mentor: Leanne Ketterlin Geller

Tiffini Pruitt-Britton: Measuring High School Students’ Funds of Knowledge for Learning Mathematics

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Winner: Education (Graduate)

Co-author: Candace Walkington

https://youtu.be/PGWT0oZxQKw

Mathematics experienced by students can be derived from the contextually situated "real world" experiences of the educator, which is typically White and middle class and not a reflection of the demographics of many classrooms in the United States. Activities where students find connections to their lives and interests have shown promise in enhancing student performance and experiences in mathematics classrooms. In this study, mathematics funds of knowledge are assessed in a novel survey instrument, reinforcing the salience of relating math experiences to students' lives and acknowledging skills and knowledge originating from experiences outside of the math classroom.

Tiffini Pruitt-Britton
Program: PhD in Education
Faculty mentor: Annie Wilhelm

Kim Pryor: Teacher Training: Does it Translate?

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Probing the impact of multiple socialization experiences on early-career faculty’s approach to teaching

https://youtu.be/xk7K0Wd8M18

Faculty socialization and development occurs before and during the early career, as faculty gain a sense of identity and belonging in academia. Despite the modern professor wearing many hats, socialization processes often deprioritize teaching development. Still, early-career faculty learn to teach because they must, and do so through myriad means in sometimes divergent contexts: their graduate, other early-career and employing institutions. They also approach teaching from increasingly diverse backgrounds and encounter increasingly diverse students. Colliding faculty and student diversity and lacking teaching development suggests possible tension and stress but also growth as faculty navigate teaching in the early career. Yet the lived process of teaching development is largely neglected in socialization research. This study addresses how early-career faculty are shaped as teachers within an increasingly diverse higher education context. Using interview and document data from faculty at one public 4-year research institution, this qualitative case study probes how early-career faculty conceptualize teaching identity, how they experience teaching development and how these experiences contribute to their development. In addressing these questions, this study elucidates teaching development experiences and outcomes of early-career faculty working at diverse institutions.

Kim Pryor
Program: PhD in Education
Faculty mentor: Sondra Barringer

Ankita Puri: Copper, nickel and palladium complexes bearing bidentate redox-active ligands with tunable H-bond donors and acceptors.

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https://youtu.be/r3ZCZzJshoo

In this study, we explore the structure and spectroscopic properties of a family of copper, nickel and palladium complexes bearing bidentate redox-active ligands that contain H-bonding donor and H-bonding acceptor groups. Single crystal X-ray crystallography shows intramolecular H-bonding interactions between the two ligand scaffolds can be finely tuned by different factors including metal center, ligand substitution, solvent of crystallization as well as counterion of the metal complex. Interestingly, the ability to control the intramolecular H-bonding interaction forces the complexes to adopt a certain geometry which can eventually permit to control the reactivity and catalytic performance of these complexes.

Ankita Puri
Program: PhD in Chemistry
Faculty mentor: Isaac Garcia-Bosch

Molly Robinson: Adapting Weighted Gene Co-Expression Network Analysis for Next Generation Sequencing

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Co-author: Elyssa Sliheet

https://youtu.be/lyZzQQbHpJY

New technologies such as single-cell RNA-sequencing (scRNA-seq) have become vital to the understanding of cell type heterogeneity of the brain. One limitation of this technique is that the resulting datasets are sparse. That is, genes often have read counts of zero in a given cell. Computational challenges arise when sparse data sets are analyzed with Weighted Gene Co-Expression Network Analysis (WGCNA), a technique that has been used to study the underlying genetic network of bulk data sets. This project aims to study how sparsity degrades the performance of WGCNA. This is done by modifying datasets where the method has been successfully applied. Gene clusters, or modules, of the generated network can then be tracked from the original dataset across varying levels of sparsity. This gives insight into how the network construction is altered when a sparse dataset is used. We will then study imputation and smoothing techniques to recover performance. Finally, we will seek to determine significant statistical features of the data that predict model performance.

Molly Robinson
Program: PhD in Mathematics
Faculty mentor: Andrea Barreiro

Catherine Rochefort: Risk Factors Predicting Exercise Avoidance: Comparisons across a Two-Part Exercise Outcome

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Co-authors: Michael Chmielewski & Austin S. Baldwin

https://youtu.be/Mu_pQ1D4mQU

Nearly 25% of US adults report engaging in no regular exercise at all. To date, there are no data identifying risk factors for exercise avoidance. Using cross-sectional data from Amazon’s MTurk and a student participant pool (N=1277), we examined potential risk factors for exercise avoidance. We modeled physical activity as a two-part outcome: exercise avoidance (engagement in 0 minutes of exercise) and exercise amount (non-zero weekly exercise minutes). We conducted bivariate logistic regressions to identify predictors of exercise avoidance and then a multivariate model to identify predictors contributing unique variance. To examine whether predictors are unique to exercise avoidance, we examined associations with exercise amount in a multivariate gamma regression. In bivariate models, age (p=.02), enjoyment (p<.001), self-efficacy (p<.001), mindfulness (p=.01), conscientiousness (p<.001), and neuroticism (p=.02) predicted exercise avoidance. In the multivariate model, age (p=.02), enjoyment (p<.001), self-efficacy (p<.001), and conscientiousness (p=.03) predicted unique variance in exercise avoidance. All predictors except conscientiousness were associated with exercise amount (ps<.001) but with more modest effects. We identified several risk factors for exercise avoidance. In most cases, the risk factors had a more meaningful effect on exercise avoidance than exercise amount

Catherine Rochefort
Program: PhD in Psychology
Faculty mentor: Austin Baldwin

Olga Romero: Latina Principals’ Beliefs and Motivations to Implement STEM Programs in Elementary Urban Schools

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https://youtu.be/T_mNJnH_ByM

This research aimed to investigate why some Latina principals decided to implement Science, Technology, Engineering, and Math (STEM) programs in the urban elementary schools they lead. This research specifically explores the motivations and belief systems that school principals embraced as they move forward in the challenging task of implementing a rigorous, non-traditional STEM curriculum in urban schools that have minimal resources, training, and implementation support. The study also aims to seek out an explanation of what drives these principals to go above and beyond the established district expectations for their schools. Using an ethnography approach in my qualitative study method, I examined the belief systems, motivations, and experiences of seven Latina principals who have successfully implemented STEM programs in a large urban city in the Southwest elementary schools.

Olga Romero
Program: Ed.D in Educational Leadership
Faculty mentor: Dawson Or

Andres Roque: The Relationship Between Alcohol Consumption and Depression with Moderating Effects of Experiential Avoidance

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Co-authors: Noelle Smith; Alicia Meuret

https://youtu.be/WpHoHq92PKk

There is a well-established relation between alcohol use and depression (Boden & Fergusson, 2011; Fergusson, Boden, & Horwood, 2009). One possible moderator of the relationship between alcohol and depressive symptoms is experiential avoidance, or the attempt to escape or avoid negative experiences (Hayes, 2016). Avoidance of perceived social threats can also lead to depressive symptoms (Holahan et al., 2005) and greater drinking to cope with social interactions. Students (104) completed 6 weekly surveys on their behaviors, emotions, and psychological experiences. Depressive symptoms were assessed using the Beck Depressive Inventory (BDI; Beck et al., 1996) and the Acceptance and Action Questionnaire (AAQ-II; Bond et al., 2011) assessed experiential avoidance. Participants reported their alcohol drinking behaviors. Time-Varying Covariates in Multilevel Growth Models were utilized. Across participants, there was a significant association for days binged and alcohol daily use on depressive symptoms, in that the more days participants used alcohol the lower their depressive symptoms. Experiential avoidance moderated the relationship between alcohol daily use and depressive symptoms, in that when participants are higher than their average on experiential avoidance, higher than average daily alcohol use was positively associated with higher depressive symptoms.

Andres Roque
Program: PhD in Psychology
Faculty mentor: Alicia Meuret

Dayna Russell Freudenthal: The Development of the Project GROW Expressive Vocabulary Assessment

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Co-authors: Jennifer Stewart, Ph.D.; Stephanie Al Otaiba, PhD.; Brenna Rivas, Ph.D.

https://youtu.be/pBrjNYl_Xao

According to the National Reading Panel (2000), researcher-developed measures best assess vocabulary instruction's impact because they are more sensitive to gains than standardized measures. This study details the initial development of a proximal vocabulary assessment for a robust kindergarten vocabulary intervention (Project GROW) for students at-risk for reading difficulties. The purpose of the Proximal Project GROW Expressive Vocabulary assessment is to measure a student's expressive vocabulary knowledge of specifically targeted Tier Two, social emotional learning (SEL) focused academic vocabulary words. The assessment, similar to those used in prior research (Coyne et al., 2007; Baker et al., 2013), has been developed to evaluate the effects of explicit vocabulary instruction closely aligned to the intervention and examine student response to instruction. This assessment focused on student acquisition and mastery of child-friendly definitions of age-appropriate academic vocabulary identified and explicitly taught in the context of the unit storybook via evidenced-based instruction, including explicit vocabulary instruction and dialogic reading. The measure will be piloted as a pre-and post-test for prototype units of the intervention this Spring.

Dayna Russell Freudenthal
Program: PhD in Education
Faculty mentor: Stephanie Al Otaiba

Marc Sager: Association between Food Security and Student Success

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https://youtu.be/M9RUSFaMscw

The purpose of this paper is to create a research design to find a correlation between food security status and student success, as well as measure how school funding per student and the location of participants' high school moderates student success. This research design employs regression analysis and multiple regression analysis to calculate correlational coefficients and determine associations. Future directions for research include (a) observing an interrupted time series intervention quasi-experiment of food retailers being built in food deserts, and measure the impact the natural intervention has on students' GPA, and (b) depending on the results and findings of this study, another direction for future research is to qualitatively examine the students that reside in food deserts, but are considered food secure.

Marc Sager
Program: PhD in Education
Faculty mentor: Anthony Petrosino

Ishna Satyarth: Application of Neural Networks in Quantum Chemistry

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Winner: Computer Science (Graduate)

https://youtu.be/NcELhfqCZXo

Understanding the motion of electrons in a molecule is a big piece of the puzzle to understand the quantum world that these sub-atomic particles live in. This complexity increases several folds when correlation of pairs of electrons are interpreted in the wave function. Current methods of including pair correlation are too expensive to apply to large molecules. Our goal is to reduce the error in the Tensor Hypercontraction approximation, which can reduce the cost of such calculations. Artificial Intelligence and in particular the application of neural networks can prove to be a savior in such situations of many unknown parameters. A neural network works like a collection of brain cells where each neuron is responsible for processing a single piece of information and increasing the number of neurons allows adding complexity in a systematic way. Since there are more than 22 different input variables accounting for the energy error, we will be using a multilayer perceptron model. Using an Artificial Neural Network, we have tried to structure them into a systematic network and tried to decipher how each variable contributes towards the outcome of total energy or bond strength. We are currently working on collecting as many data points to train our Neural Network, so that we can get the most accurate results in the future.

Ishna Satyarth
Program: PhD in Computer Science
Faculty mentor: Devin Matthews (Chemistry)