Student Presentations

Fidelia Nawar (U): Covid-19 Open Research Dataset (CORD-19) Extractor

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Winner: Undergraduate Top 3
Winner: Lyle (Undergraduate)

https://youtu.be/ocCNIUjZaFQ

The Covid-19 Open Research Dataset is a growing resource of coronavirus research and scientific papers on Covid-19. CORD-19 is designed to facilitate the development of information retrieval systems through its collection of structured full-text papers. This study aims to contribute to the SMU AI Club's efforts to develop a search engine that parses through CORD-19 articles in order to extract relevant data about protein, compound, chemical information, and more. In the study, we describe the mechanics of dependency parsing, highlighting challenges and key design decisions, and discuss tools and upcoming shared tasks related to the search engine project. We hope this resource will introduce a new way to search for desired data related to Covid-19 and further bring together the computing and biomedical community on campus.

Fidelia Nawar
Major: Computer Science
Faculty Mentor: David (King Ip) Lin

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

Savannah Ostner (U): Distinguishing hypo- vs. hyper- mentalizing in social anxiety and traits related to autism spectrum disorders

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https://youtu.be/ZgxSINZcqcE

Social anxiety symptoms and traits related to autism spectrum disorders have both been linked to deficits in social cognition, particularly impaired Theory of Mind (ToM) or mentalizing ability. Traits associated with autism and social anxiety symptoms will be assessed in an undergraduate population using the Autism Spectrum Quotient (AQ), the Social Phobia Scale (SPS), the Social Interaction Anxiety Scale (SIAS), and the Leibowitz Social Anxiety Scale (LSAS). This study will assess the impact of social anxiety symptoms and traits related to autism on mentalizing ability in subclinical populations. Further, the type of mentalizing errors made will be examined using the ecologically valid Movie for the Assessment of Social Cognition (MASC). The study will also explore whether the type of stimuli, either emotional or cognitive, has an effect on the type of mentalizing errors that are made.

Savannah Ostner
Major: Psychology; Minors: Neuroscience, Biology, Cognitive Science
Faculty mentor: Ben Tabak

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

Mushfequr Rahman (U): Cultural Competence in Sexual and Mental Health Programs across South Asian and Arab American Communities

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https://youtu.be/o_Eht9V4mFg

The COVID-19 pandemic has exacerbated existing disparities across demographic lines such as race, gender, and religion among others. For example, domestic violence cases against women, mental health services, and the need for interpreters in healthcare have increased after the onset of the pandemic. Mitigating these disparities require culturally competent programs to properly care for diverse communities. In my research, I examine how organizations serving South Asian American and Arab American communities incorporate cultural competency in their sexual and mental health programs, especially in light of the pandemic. There is scarce literature on mental and sexual health about these communities, emphasizing the need for such research. This research is conducted with the help of Arab Community Center for Economic & Social Services (ACCESS) in Dearborn, Michigan and the South Asian Sexual & Mental Health Alliance (SASMHA) in Washington, D.C.

Mushfequr Rahman
Majors: International Studies, Health & Society; Minors: Biological Sciences, Spanish
Faculty mentor: Rachel Ball-Phillips

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)

Bibiana Schindler (U): Hegemony over Human Rights: The Politics of U.S. Genocide Recognition in the al-Anfal Campaign

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Winner: Dedman I (Undergraduate)

https://youtu.be/AmR1IZSwS3w

The United States is often perceived as the moral authority of the world, defending human rights and supporting fundamental American values abroad through its foreign policy. However, despite this elevated status in the political arena, the United States often distances itself from issues pertaining to human rights such as genocide, taking decades to sign the United Nations Genocide Declaration and consistently failing to recognize crimes against humanity and genocides as they occur. The rationale behind this moral failure can be understood through the lens of the 1980s Kurdish genocide in Iraq known as the al-Anfal Campaign. The United States provided Iraq with supplies and support during the Iran-Iraq War, failing to fully acknowledge Iraq's crimes until after their relationship deteriorated in the early 1990s. The United States' awareness and evaluation of Iraq's bombing campaigns can be analyzed through numerous recently declassified documents. The findings of this research suggest that the United States weighs its interests against its values in its foreign policy decisions. It appears that the U.S. often chooses its strategic interests over its moral obligations, using genocide recognition as a political tool to maintain its hegemony.

Bibiana Schindler
Majors: History and Psychology; Minors: Public Policy & International Affairs, Russian Area Studies
Faculty mentor: Sabri Ates

Bhesta Shahim (U): COVID-19 Tracking in Indigenous Communities

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https://youtu.be/TPcLW-BOeqk

Indigenous Peoples experience alarming rates of inequities and systemic discrimination and also experience disproportionate rates of malnutrition and lack of access to quality healthcare, housing, and clean water. These realities make Indigenous Peoples especially vulnerable to COVID-19 and its effects; however, as COVID-19 continues to spread, Indigenous Peoples are largely being left out of the conversation. Due to the need for data and information disaggregated by Indigenous Peoples, Cultural Survival (CS) is producing a map, using Google Maps technology, to show the situations Indigenous communities are facing as a result of COVID-19, including documenting COVID-19 cases and related human rights violations. Through my research work with Dr. Smith-Morris and Cultural Survival, I have contributed to collecting this important data as well as supervising other students on this work.

Bhesta Shahim
Majors: Human Rights, Health & Society
Faculty Mentor: Carolyn Smith-Morris

Megan Sham (U): Immigrant Identity Through Food Culture

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Winner: Dedman II (Undergraduate)

https://youtu.be/-NygcjjXiK0

The main goal of this research study is to look at integration of Chinese-American immigrant families through the specific lens of their domestic culinary practices (cooking at home) as a lens of understand the food practices of immigrants in the United States. The participants in this study will include SMU students and their families, as well as other members of the Chinese-American immigrant community in the Dallas/Fort Worth Area and Houston.The study aims to understand how immigrant communities in the United States adapt to and resist mainstream American culture, starting with food and cooking in the home. Interviews will be conducted to gather qualitative information concerning attitudes towards cooking certain cuisines and the effects on the family unit. The research will be funded partially by the John G. Tower Center's Henry S. Miller Undergraduate Research Fellowship, as well as the Fry Undergraduate Research Award from the SMU Anthropology Department.

Megan Sham
Majors: Anthropology
Faculty Mentor: Nicolas Sternsdorff Cisterna

Faith Sheedy: Faith and Spirituality On-Campus: Exploring the Spiritual Needs of Students

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Co-author: Laura Bell

https://youtu.be/uYhyAVC7yC4

This project will focus on analyzing student engagement with their spirituality through the Office of the Chaplain and Religious Life as well as other faith-based campus resources. The project will use both qualitative and quantitative methods to understand and gauge if the spiritual needs of students are being met and how SMU can support of students' spiritual lives. Utilizing Astin, Astin, and Lindholm (2010) national study of undergraduates' spiritual growth we seek to understand the spiritual and faith needs of on-campus residential students. Results may inform the work of religious and spiritual life centers.

Faith Sheedy
Program: Master’s in Theological Studies
Faculty mentor: Dustin Grabsch

Megan Simons: Transition-potential coupled cluster

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https://youtu.be/oTt_Ut-7E4o

The problem of orbital relaxation in computational core-hole spectroscopies, including x-ray absorption and x-ray photoionization, has long plagued linear response approaches, including equation-of-motion coupled cluster with singles and doubles (EOM-CCSD). Instead of addressing this problem by including additional electron correlation, we propose an explicit treatment of orbital relaxation via the use of “transition potential” reference orbitals, leading to a transition-potential coupled cluster (TP-CC) family of methods. One member of this family, in particular, TP-CCSD(12/ 1 2 ), is found to essentially eliminate the orbital relaxation error and achieve the same level of accuracy for the core-hole spectra as is typically expected of EOM-CCSD in the valence region. These results show that very accurate x-ray absorption spectra for molecules with first-row atoms can be computed at a cost essentially the same as that for EOM-CCSD.

Megan Simons
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Devin Matthews

Jase Sitton: Indirect Bridge Monitoring Using Crowdsourced Smartphone Data from Passing Vehicles

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https://youtu.be/mcIlXNE2kl4

United States bridge infrastructure is aging, with more than 231,000 bridges currently in need of repair, rehabilitation, or replacement. In light of the urgent needs surrounding aging bridge infrastructure, and in the absence of sufficient funding to address all required bridge repairs, it is crucial to identify which bridges have the most immediate need for the allocation of available funds for bridge repairs. Typically, structural health monitoring comprises the installation of permanent sensors on a bridge superstructure and the collection of data for analysis to determine bridge condition. These systems, however, are often expensive and logistically difficult to install on a large number of bridges. Smartphones contain a variety of sensors, including accelerometers and gyroscopes, that may be used to record bridge vibration response data from within vehicles as they traverse bridges. This data may then be used isolate the bridge's vibration response and make bridge condition assessments at regular intervals, and bridges requiring immediate attention can be flagged.

Jase Sitton
Program: PhD in Civil and Environmental Engineering
Faculty mentor: Brett Story

Elyssa Sliheet: Genetic Network Analysis

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Co-author: Molly Robinson

https://youtu.be/OiPjD0fuQvI

There exists the need to develop and study the effects of novel immunotherapies for the treatment of various cancer types. Unlike chemotherapy and radiotherapy which directly target the cite of a tumor, immunotherapies serve as catalysts to activate the body's immune response. Studies have shown the benefits of immunotherapies. However, there is still need to computationally analyze genetic data to identify relevant biomarkers as related to clinical significance for the overall advancement of targeted medicine. In this research, we construct and analyze important properties of a protein-protein interaction network. These properties shed light on potential biomarkers relevant to patient immune response and sensitivity.

Elyssa Sliheet
Program: PhD in Mathematics
Faculty mentor: Andrea Barreiro

Zilin Song: Quantifying Energy Contribution in Enzyme Catalysis using Machine-Learning based Regression Analysis

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Winner: Theoretical and Computational Chemistry (Graduate)

https://youtu.be/3IXmP6v7jRM

The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non- linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

Zilin Song
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Peng Tao

Siavash Tabrizian: A sampling-based branch and cut algorithm for two-stage stochastic mixed integer programs

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Winner: Operations Research (Graduate)

https://youtu.be/6E7GQBXBa3c

Stochastic mixed-integer programs are among the most challenging class of optimization problems that finds many applications in practice. In this presentation, we describe a novel algorithmic framework for solving two-stage stochastic mixed-integer programs using internal sampling.

Siavash Tabrizian
Program: PhD in Operations Research
Faculty mentor: Harsha Gangammanavar

Micah Thornton: Examining Uses of DFT distance metrics in SARS-CoV-2 Genomes

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Co-authors: Monnie McGee

https://youtu.be/bFW9xMpdSp0

The Fourier Coefficients (FC) of a genomic sequence can be calculated according to a method proposed earlier this decade by Yin et al. Here we are concerned with the efficacy of these coefficients in capturing useful information about viral sequences. The FCs are rapidly computable and comparable which allows for speedy real-time numerical analyses of sequences. In this work we investigate using the FCs as summaries of SARS-CoV-2 sequences by applying regional classification procedures, and graphical examination. Specifically we extract geographic submission location from sequences submitted to the GISAID Initiative, and attempt to use the FCs to classify these sequences in addition to displaying them visually utilizing dimensionality reduction. We show that the FCs may serve as useful numerical summaries for sequences which allow manipulation, identification, and differentiation via classical mathematical and statistical methods not readily applicable to character strings. Further we argue that subsets of the FCs may be usable for the same purposes, indicating a reduction in storage requirements. We conclude by offering extensions of the research, and potential future directions for subsequent analyses and further theoretical development of techniques specific to the FCs and suggesting different kinds of series transforms for discretely indexed signals like genomes.

Micah Thornton
Program: PhD in Biostatistics
Faculty Mentor: Monnie McGee