Nada Alzaben: End-to-End Routing in SDN Controllers Using Max-Flow Min-Cut Route Selection Algorithm

we present a novel max-flow min-cut based algorithm to solve the flow routing problem in the Software Defined Network Controller. Routing using traditional shortest path first algorithms often results in bottlenecks that cause performance degradation including higher energy use, reduced throughput, and increased slowdown. Our algorithm uses the max-flow min-cut algorithm to identify potential bottlenecks in order to avoid them in the flow routing decisions. Our simulations show that our max-flow min-cut based algorithm improves the network performance by minimizing the mean wait time by 15.1%, minimizing the mean slowdown by 6.1 %, minimizing the maximum completion time by 9.6 %, and maximize the mean throughput by 18.3 % compared to the Shortest Path algorithm. Explicitly considering congestion in determining routes, such as with our Max-Flow Min-Cut algorithm, is necessary to maximize performance.

Nada Alzaben
Program: PhD in Computer Science
Faculty mentor: Daniel Engels

Lauren Ammerman: Reversing Multidrug Resistance in Cancer through Iterative Identification of P-glycoprotein Inhibitors

Winner: Biological Sciences (Graduate)

co-authors: James McCormick (co-first author), Chanyang Park, Courtney Follit, Jesiska Lowe, Pia Vogel, and John Wise.

Multidrug resistance (MDR) describes the intrinsic or acquired resistance of cancers to diverse chemotherapeutics and is arguably one of most significant barriers to cancer treatment. As a mechanism of MDR, cancers commonly overexpress ATP-binding cassette transporters such as P-glycoprotein (P-gp). P-gp harnesses the power of ATP hydrolysis to efflux cytotoxic compounds across the cell membrane. Inhibition of P-gp can re-sensitize cancers to chemotherapeutics, but many P-gp inhibitors are also transport substrates of P-gp. Consequently, high compound dosages can be required to inhibit P-gp, and this can result in toxic off-target effects. To identify potential P-gp inhibitors that are not transport substrates, we iteratively screened millions of compounds against dynamic P-gp targets using massive parallel docking experiments. Hits from computational screens were then subjected to QSAR and purchased for testing. Compounds were assessed for their ability to reverse MDR using two sets of paired, human cancer cell lines – two chemotherapy resistant, P-gp overexpressing lines, and two chemotherapy-sensitive, non-P-gp overexpressing lines. Compounds were then tested for inherent toxicity against a non-cancerous human cell line. Lastly, we determined if our putative inhibitors are P-gp substrates using LC-MS/MS intracellular accumulation assays. We report a global hit rate of 15%.

Lauren Ammerman
Program: PhD in Biological Sciences
Faculty mentor: John Wise

Madison Arcemont: Computational Intestate Succession

Winner: Combined-Non-STEM (Graduate)

As new technologies develop, we continue our search for ways that this technology can improve our world, and particularly our legal system. One rarely thought of system with room for improvement is that of intestate succession, the court's management of the estates of individuals who die without a will. Computational law, the implementation of the law through computer code, is emerging as a field that can solve a wide range of issues in the law, including in the estate planning field. In particular, smart contracts, programs that carry out agreements procedurally, can be used to make the process of intestate succession more efficient, affordable, and accessible for the heirs of an individual who dies without a will. This paper investigates the necessity, feasibility, and challenges associated with implementing computational intestate succession and argues that states should implement such technology. While the field of estate planning has been slow to accept technological solutions, the trend towards acceptance shows that computational solutions to long-standing problems may be accepted in the near future. To demonstrate the feasibility of computational intestate succession, this article concludes with a sample program for the smart contract written in Lexon, a natural language program.

Madison Arcemont
Program: Juris Doctorate
Faculty mentor: Carla Reyes

Saumya Sakitha Ariyarathne: Stochastic Market Clearing Formulations and Price Interpretations

In this presentation we will present alternative formulations of stochastic market clearing problem which are based on different algebraic representations of non-anticipativity constraints of multistage stochastic programming. These formulations result in prices which have alternative interpretations under different power system settings. We will present these interpretations along with computational results for well known testbeds.

Saumya Sakitha Ariyarathne
Program: PhD in Operations Research
Faculty mentor: Harsha Gangammanavar

Hedieh Ashrafi: Selection, Scheduling of Project Portfolios under profit uncertainty and limited available Scientists by using Adaptive Robust Optimization

Co-author: Aurelie Thiele

We present a model for the selection and scheduling of R&D projects with several phases.The initial model contains two main stages development and commercialization. The goal of this model is to maximize the net present value under constraints of scientists' availability and uncertain profit. This nonlinear mixed-integer model is NP-hard and not tractable for large-scale problem instances where we use adaptive robust model, Hence, we develop a strong Mixed Integer Programming model and a heuristic algorithm. Then, we show the performance of these algorithms in terms of running time and optimality gap in experiments.

Hedieh Ashrafi
Program: PhD in Operations Research
Faculty mentor: Aurelie Thiele

Yulan Bai: Compact Formulations of Network Flow Problems

Co-authors: Eli Olinick, Ronald Rardin, Yuanyuan Dong, Andrew Yu

The triples formulation is a compact formulation of multicommodity network flow that provides a different representation of flow than the traditional, widely used node-arc and arc-path formulations. In the literature, the triples formulation has been applied successfully to the minimum cost multicommodity flow problem with piecewise linear cost functions in complete, undirected graphs, and the maximum concurrent flow problem. In this study we show that the triples formulation of a freight logistics application known as the backhaul profit maximization problem (BPMP) can be solved significantly faster than the existing model in the literature, which is based on the node-arc model. We also demonstrate the effectiveness of applying the triples formulation to the uncapacitated, single-commodity, fixed charge network flow problem.

Yulan Bai
Program: PhD in Operations Research
Faculty mentor: Eli Olinick

Rachael Becker: The Impact of Online Discussion Forums in Introductory Statistics

This study aims to examine the impact that online discussion activities, designed to encourage collaboration and question posing, have on students' understanding of statistical concepts in an undergraduate statistics course. The sample is composed of 82 undergraduate students enrolled in an introductory statistics course. Techniques were utilized to match students that participated in the online discussions to those that did not participate in the online discussions. The differences between pre and posttest grades on the LOCUS assessments were compared to determine if participation impacted student learning outcomes.

Rachael Becker
Program: PhD in Education
Faculty mentor: Tim Jacobbe

Mary Lena Bleile: Imputation of Counterfactual Tumor Volumes

Co-authors: Steve Jiang, Dan Nguyen, Debabrata Saha, Michael Story, Casey Timmerman, Robert Timmerman, Yixun Xing

Dropout is a statistical problem which occurs when an experimental unit that one is taking serial measurements from becomes unavailable for further measurements, prior to the end of the study. One common instance of dropout is found in tumor growth experiments performed on animal subjects: some animals are sacrificed when they are in too much pain, or bad condition. Unlike traditional missing data problems in time series data, this issue poses unique statistical problems due to the fact that the resultant dropout process results in a monotonic missingness pattern: if we observe missingness at a time t, then we necessarily have missingness at all timepoints t* >t. We introduce a novel method for imputation of tumor volume counterfactuals: we build a multivariate growth curve with random effects as inspired by Heitjan, et al (1993), and apply Bayesian methods in order to acquire a random sample for each parameter, in concordence with the literature on multiple imputation. One can then leverage conditional distribution theory to acquire a complete dataset from each of the random posterior samples. We additionally supply an R package for ease of execution of our method.

Mary Lena Bleile
Program: PhD in Biostatistics
Faculty mentor: Daniel Heitjan

Chelsea Carson: Broad Autism Phenotype and Relationship Satisfaction in Parents of a Child on the Autism Spectrum: The Role of Partner Discrepancy

Winner: Psychology (Graduate)

Co-authors: Naomi Ekas, Chrystyna Kouros

Previous research has linked poor relationship satisfaction with parenting a child with Autism Spectrum Disorder (ASD). Parents of children with ASD, however, also have higher levels of Broad Autism Phenotype (BAP) traits themselves—that is, they evidence subclinical levels of autism characteristics including communication difficulties, rigid personality traits, and emotional aloofness. Therefore, children’s ASD characteristics may not fully account for why these couples are at greater risk for marital discord. This study tested the extent to which BAP traits in parents of children with ASD, and discrepancy between partners in BAP, predicted their relationship satisfaction while controlling for their child’s ASD characteristics. Participants were 117 families with a child with ASD who were recruited to participate in a study about family dynamics. Couples completed questionnaires on their BAP traits, relationship satisfaction, and their child’s ASD characteristics. Husbands were higher in total BAP, aloofness, and pragmatic language. Husbands’ total BAP was associated with lower relationship satisfaction for husbands. Discrepancy between husbands and wives in total BAP and pragmatic language was associated with lower relationship satisfaction for husbands. These findings provide preliminary support for the relevance of partners’ discrepancy in BAP within romantic relationships.

Chelsea Carson
Program: PhD in Clinical Psychology
Faculty mentor: Chrystyna Kouros

Diane Chao: Effect of reward motivation on directed forgetting in younger and older adults

Winner: Psychology (Graduate)

Co-authors: Sara N. Gallant, Holly J. Bowen

An important feature of the memory system is the ability to forget, but aging is associated with declines in the ability to intentionally forget. Despite known cognitive deficits, sensitivity to affective manipulations are maintained in older age, for example, reward motivation can improve older adults' memory. Using a directed forgetting paradigm, we tested whether reward motivation could improve intentional forgetting in young and older adults. Participants were shown a sequence of words with instructions to remember (TBR) or forget (TBF) to earn a high ($.75) or low ($.01) reward. For older adults, there was no evidence that reward motivation improved cognitive control as high value reward anticipation did not improve directed forgetting. Instead, the findings are in line with hypotheses, that high value reward anticipation leads to better memory regardless of the TBR or TBF cue. Reward may bolster memory in an automatic fashion, overriding cognitive control of encoding processes.

Diane Chao
Program: PhD in Psychology
Faculty mentor: Holly Bowen

Dazhuo Chen: Geographic Design of Sports Leagues to Optimize Driving Time and Competitiveness

Club sports in metro areas are popular nowadays, however there are key concerns for organizers, which are reducing driving time due to teams commuting to facilities in different regions while keeping league divisions competitive. A three-step approach is adopted to solve this problem. Driving time data between each location is analyzed initially, and clubs are split into several groups accordingly. Teams are assigned to groups based on their location and ranking. And these two processes are merged in the end to find the best solution. From the result, this optimization model is able to arrange games in a way that not only shortens the travel time for players, but also maintains an acceptable level of competition.

Dazhuo Chen
Program: PhD in Engineering Management
Faculty Mentor: Eli Olinick

Yuanting Chen: Growth and Decay of Coherent Structures Interacting with Random Waves

 

High-amplitude coherent structures have been observed in nonlinear wave systems as diverse as fluids, plasmas and optical waves in matter. We explore the interaction of disordered waves with coherent solitary waves in a nonintegrable version of the nonlinear Schrodinger equation. We show that statistical mechanics explains growth or decay of the coherent structures in detail.

Yuanting Chen
Program: Ph.D. in Mathematics
Faculty mentor: Benno Rumpf

Nusaiba Chowdhury: Barriers to Refugees Achieving Self-Sufficiency

Refugees resettled in the United States are expected to attain self-sufficiency (employment) as soon as possible, however they face numerous barriers to this goal. This research is the result of interviews with refugee serving staff and their perceptions of barriers to refugees achieving self-sufficiency.

Nusaiba Chowdhury
Program: PhD in Anthropology
Faculty mentor: Neely Myers

Alexis Delgado: Unusually Long C-C Bonds

Co-authors: Alan Humason, Robert Kalescky, Marek Freindorf, and Elfi Kraka

For decades chemists have attempted to create a molecule with the longest covalent C-C bond possible. This has typically been done through steric effects or molecular strain. More recently the diamino-o-carborane analog, di-N,N-dimethylamino-o-carborane, has been observed to have a unusually long C-C bond which has been attributed to negative hyperconjugation effects (i.e.charge transfers). In our work we computationally analyzed the C-C bonds for a unique set of 53 molecules including clamped bonds, highly sterically strained complexes, electron deficient species, and the di-N,N-dimethylamino-o-carborane molecule in order to consider all routes for obtaining the longest C-C bond. We derive local vibrational stretching force constants for targeted C-C bonds to quantify the intrinsic strengths of these bonds. Our systematic study quantifies for the first time that whereas steric hindrance and/or strain definitely elongate a C-C bond, electronic effects can lead to even longer and weaker C-C bonds. Within our set of molecules the electron deficient ethane radical cation, in D3d symmetry, acquires the longest C-C bond with a length of 1.935 angstroms followed by di-N,N-dimethylamino-o-carborane with a bond length of 1.930 angstroms. However, the C-C bond in di-N,N-dimethylamino-o-carborane is the weakest compared the ethane radical cation; revealing that the longer bond is n

Alexis Delgado
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Elfi Kraka

Bonnie Etter: Ceci n’est pas une pipe

Winner: Anthropology (Graduate)

Co-author: Molly Murphy Adams

I present an in depth artifact analysis of a pipe bowl, found in the Southern Methodist University Archaeological Research Collections

Bonnie Etter
Program: 
Anthropology
Faculty Mentor: Sunday Eiselt

Tyler Evans: Swelling as a stabilizing mechanism in irradiated thin films (the sequel)

Winner: Mathematics (Graduate)

Co-author: Scott Norris

The fields of nanoscale pattern formation and nanostructural engineering are still in their infancy (relative to many other scientific areas). Much research is still centered around identifying and quantifying the relevant nanoscale mechanisms responsible for experimentally-observed results, since the same physical forces operating at the nanoscale may look very different than at the macroscale. Here, we provide further results on a recently-identified candidate mechanism (swelling, or radiation damage) that could explain the observed angle-independent lack of nanostructuring in thin films amorphized at high energy. We present new analytical and numerical results, characterization of the mechanism in its full parameter space, and an unexpected, mathematically-interesting bifurcation.

Tyler Evans
Program: PhD in Mathematics
Faculty mentor: Scott Norris

Niloofar Fadavi: An active-set method for two stage stochastic quadratic programming

In this study we examine two stages stochastic quadratic programming problems, where the second stage itself is a quadratic programming problem with linear constraints with uncertain right-hand sides. We develop the active-set strategy obtain a estimation for the second stage. This approximation is used to design a computationally gradient solution algorithm to solve stochastic quadratic programs. We will present the convergence and numerical analysis of the algorithm.

Niloofar Fadavi
Program: PhD in Operations Management
Faculty mentor: Harsha Gangammanavar

Reza Farsad Asadi: Miniaturized Vacuum Tubes

Co-authors: Tao Zheng, Jaime da Silva

A Nanoscale Vacuum-Channel Transistor (NVCT) is a transistor based on field emission that the electron channel is a vacuum. These transistors are nanoscale versions of vacuum tubes. Because the channel is a vacuum, they can potentially be faster than traditional solid state transistors. Also, they can be expected to be less susceptible to harsh environments compare to the solid state transistors. However, they have shown instability over time and they require high vacuum to operate. This project on investigating the lifetime and instability of field emitters over time.

Reza Farsad Asadi
Program: PhD in Electrical Engineering
Faculty mentor: Bruce Gnade

Paul Foster: Re-engineering Education: Building on L.S. Vygotsky’s Mind in Society (1978)

Customizing Learning using Embedded Assessment

Paul Foster
Program: PhD in Education
Faculty mentor: Leanne Ketterlin-Geller

Pete Furseth: Sales Force Territory Design: A case for geographic B2B sales territories

Salesforce territory design is a process that most sales organizations labor over on an annual basis. Often business-to-business sales organizations align on geographic sales territories. Usually, this involves grouping sales units (geographic units) into larger geographic groups, called sales territories. This paper will explore how to create an optimal sales territory design that balances two fundamental constraints, equitability in the addressable market and geographic compactness. Equitability ensures that each salesperson has the same opportunity to sell and achieve sales quota. Compactness ensures that the sales territory includes sales units that are close to one another and, more specifically, adjacent to one another. This optimal territory design is then applied to a business-to-business sales organization.

Pete Furseth
Program: PhD in Operations Research
Faulty mentor: Eli Olinick

Han Gao: Study of Radiation Tolerance of GaN-based Devices

There is increasing interest in the radiation resistance of wide-bandgap semiconductor devices, both for power transistors and RF applications. Ga-based devices are especially interesting because they have shown high resistance to many different types of radiation and to many different doses. In general, the magnitude of the damage, as evidenced by the drain-to-source leakage depends on the linear energy transfer (LET) and drain-source voltage (𝑉_𝐷𝑆). In this project we are studying the effects of radiation on GaN-based devices, both experimentally and theoretically, to improve the understanding of the operation of such devices in different radiation environments.

Han Gao
Program: PhD in Electrical Engineering
Faculty mentor: Bruce Gnade

Holly Grubbs: The Principal’s Leadership Impact utilizing Distributed Leadership Practices that drive School Improvement

Winner: Education: Ed.D. in Education Leadership (Graduate)

The role of the principal on the campus has shifted due to the significant workload of managing the school and the increased amount of accountability for teaching and learning. Through a solid vision and focused mission, the school's culture and student learning can achieve success. However, for a school principal to succeed in building the capacity of the teacher and reach the high expectations for student learning, a team of leaders must be in place. Developing an organization is not about delegating the work, but also about creating a team that is collaborative and able to work together through effective communication. While principals may struggle with the federal, state, and local accountability system, it is the success of the campus leadership team that establishes a focused mission for the day-to-day work impacting the teaching and learning for student success. For this reason, principals should look at distributed leadership and the focus of how to lead and inspire those who cross their path. In reviewing Bolden's (2011) synthesis of research on distributed leadership and how it can impact the leadership practice for educational leaders, I found it to be about the communication that takes place between the leaders and followers who are doing the work. The research sets a foundation for a distributed perspective for leading a school organization to success.

Holly Grubbs
Program: Ed.D in Educational Leadership
Faculty mentor: Dawson Orr

Uroob Haris: Light-Mediated Microprinting

Techniques for nanofabrication and high resolution lithography are increasingly in demand owing to applications of nanomaterials in technology and therapeutics. This poster will cover research towards a microprinting technique that combines chemistry with structured light patterning to achieve micrometer resolution pattern printing on solid surfaces.

Uroob Haris
Program:
 Chemistry
Faculty Mentor: Alex Lippert

Sabrina Hetzel: Modeling the Spread of COVID-19: A University Study

Understanding, containing, and eradicating COVID-19 is an enormous problem for the scientific community to tackle, and there are myriad facets that need to be solved. Our group in particular wishes to explore the viral spread in a university setting since it is an ideal climate for a COVID-19 outbreak. Up until recently, we aimed to understand how initial mass testing of the student body at the start of the semester, and continual testing throughout the semester reduces the spread of the virus. Looking into the future, we wish to explore the conditions and implications for a nontrivial equilibrium state for the infected population.

Sabrina Hetzel
Program: PhD in Mathematics
Faculty mentor: Alejandro Aceves

Rhonda Hodge: Health Disparities and Covid-19 in Underserved Communities

"ABSTRACT In the United States, COVID-19 has infected over 27 million people; more than 456,000 have died. Like most social, financial, and health emergencies, Black, brown and Indigenous communities have been disproportionally affected by this current pandemic. COVID-19 laid bare the underlying systematic racism that affects these communities. Issues of inadequate healthcare, low wages, dangerous workplaces or conditions, blue collar jobs, and pre-existing medical conditions such as high blood pressure, diabetes, heart disease, and obesity are contributing factors. Now with various treatments —even a vaccine, the reality of the data still points out the disparities in these communities. Research shows that communities of color are lagging behind in having access to the vaccines. Past discriminatory practices, particularly in Black communities have deep negative impacts, rendering these communities not only more vulnerable to the virus, but also to avoid the vaccine. Their concerns must be addressed to help develop a sense of trust and acceptance for available treatments and vaccines. On the ground relationships, relying on trusted community partners such a churches, medical personnel, and fraternal organizations can be utilized. With a focused approach to what is a long-standing problem, communities of color can be successful in their fight against Covid-19."

Rhonda Bellamy Hodge
Program: Master’s of Liberal Studies
Faculty Mentor: Kate Montgomery

Brooke Istas: Literature Review of Adult Mathematics Anxiety in the United States

Students who have a higher level of mathematics anxiety seem to perceive mathematics as a negative experience. What key features of adults are influenced by their own mathematics anxiety and/or mathematics avoidance? By using eleven peer-reviewed studies, within the last five years, to synthesize literature on adults in the United States, two themes appear. The first theme is situated around a student's perception of mathematics. Students who have a higher level of mathematics anxiety seem to perceive mathematics as a negative experience. The second theme centers around the impact of stress on a student who is learning mathematics. A few of the studies did indicate a correlation between the higher the level of stress to more mathematics anxiety was reported. One of the studies used electroencephalography (EEG) to measure stress on the brain. This review will look at these studies for understanding of the current research on mathematics anxiety and identify areas for further research.

Brooke Istas
Program: PhD in Education
Faculty mentor: Candace Walkington

Shuang Jiang: BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies

Winner: Biostatistics (Graduate)

Co-authors: Quan Zhou, Xiaowei Zhan, Qiwei Li

The coronavirus disease of 2019 (COVID-19) is a pandemic. To characterize its disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model builds on a Bayesian Poisson segmented regression model that 1) capture the epidemiological dynamics under the changing conditions caused by external or internal factors; 2) provide uncertainty estimates of both the number and locations of change points; and 3) has the potential to adjust for any time-varying covariate effects. Our model can be used to evaluate public health interventions, identify latent events associated with spreading rates, and yield better short-term forecasts.

Shuang Jiang
Program: PhD in Biostatistics
Faculty mentor: Xiaowei Zhan

Xiaoxian Jing: Parton distributions, nuclear deeply inelastic scattering, and electroweak precision measurements at the LHC

Winner: Combined-STEM (Graduate)

Parton distribution functions (PDFs) for quarks and gluons inside the proton are needed for predicting a variety of processes at the LHC, including Higgs boson production and searches for new physics beyond the Standard Model. PDF parametrizations are obtained by fitting a large number of cross-sections from many experiments at different (x,Q^2). In the global analyses of PDFs by CTEQ collaboration, we find that deeply inelastic scattering experiments on nuclear targets provide important constraints on combinations of PDFs relevant to the LHC electroweak precision measurements. What is the role of these experiments in the LHC era, and how influenced are they by nuclear effects? I explore this complex question using a new statistical indicator called "PDF sensitivity" for analyzing the impact and compatibility of experiments in a PDF fit.

Xiaoxian Jing
Program: PhD in Physics
Faculty mentor: Pavel Nadolsky