Master’s & Doctoral Defenses
The Public presentation portion of a defense is open to everyone and is an especially valuable opportunity for graduate students to experience the process firsthand.
Note: All information is provided by the academic units.
Title: Leveraging Tiled Display for Big Data Visualization using D3.js
Program: Master of Science in Computer Science
Advisor: Dr. Steven M. Cutchin, Computer Science
Committee: Dr. Jerry Fails, Computer Science, Dr. Maria Soledad Pera, Computer Science, and Dr. Catherine Olschanowsky, Computer Science
Date: June 29, 2018
Time: 1:00 p.m.
Location: City Center Plaza, Room 352
Read Ujjwal Acharya's Abstract Here
Data visualization has proven effective at detecting patterns and drawing inferences from raw data by transforming it into visual representations. As data grows large, visualizing it faces two major challenges: 1) Limited resolution i.e. a screen is limited to a few million pixels but the data can have a billion data
points. 2) Processing of this data becomes computationally challenging for a single node system. We present a system that addresses both of these issues for efficient big data visualization. In the presented system, a High Pixel Density and Large Format display was used so that fine details of the data can be visualized on the screen. Apache Spark and Hadoop were used because it allowed us to use a cluster for the computational part of the system. To test the system, a global wind flow simulation was created using the Global Surface Summary of the
Day dataset and visualized using web browsers with D3.js code. We conducted both a performance evaluation and a user study to measure the efficiency and effectiveness of the system. It was seen that the system was most efficient when visualizing data using streamed bitmap images rather than streamed raw data. The performance of the system did not meet our target (30 Frames Per Second) but was still efficient in performing big data visualization. The results of the user study concluded that the system is effective and easy to use for data visualization. The outcome of our experiment suggests that the current state of browsers may not be as powerful as required to perform heavy 2D data visualization on the web and still needs more development for visualizing data of large magnitude.
Title: Delinquency Predictors: Offending to Commitment
Program: Master of Arts in Criminal Justice
Advisor: Dr. Lisa G. Bostaph, School of Public Service
Committee: Dr. Laura King, School of Public Service and Dr. Shaun Gann, Criminal Justice
Date: June 20, 2018
Time: 12:00 p.m.
Location: Library, Room 170
Read Kaitlyn Pederson's Abstract Here
The United States processes millions of adolescents through the juvenile court system annually. Throughout these hearings and upon adjudication, it is ultimately up to a judge to decide the juvenile’s disposition. Although research on juvenile delinquency has identified a variety of factors linked to youth offending, research is limited in terms of variables predicting a juvenile’s dispositional outcome. The current study examined a number of predictive variables for youth offending to determine if they also influence a juvenile being committed to state custody in Idaho. This analysis consists of pre-screen evaluations obtained by the Idaho Department of Juvenile Corrections. The factors acquired from these reports are evaluated in terms of their effect on adolescent disposition.
Title: High-Cycle Fatigue Properties of the Human Meniscus
Program: Master of Science in Mechanical Engineering
Advisor: Dr. Trevor Lujan, Mechanical and Biomedical Engineering
Committee: Dr. Clare Fitzpatrick, Mechanical and Biomedical Engineering, and Cheryl Jorcyk, Biological Sciences, and Mahmood Mamivand, Mechanical and Biomedical Engineering
Date: June 22, 2018
Time: 11:00 a.m.
Location: Micron Engineering Center; Room 301
Read Madison Krenz's Abstract Here
The meniscus is a soft fibrous tissue that distributes cyclic loads across the knee joint. It is frequently injured, and one likely mechanism for failure is a high rate of repeated exposure to low-magnitude loads, known as high-cycle fatigue. These fatigue failures depend on properties that relate cycles to failure to material strength and composition. The objective of this study is to measure the high-cycle fatigue properties in human meniscus. Results from this study may help identify safe load limits to prevent
meniscal tears, and can provide useful data for groups developing meniscal replacement devices.
Title: The Regulation of Notch Signaling by Src Kinase and Polyphenolic Compounds
Program: Doctor of Philosophy in Biomolecular Sciences
Advisor: Dr. Allan Albig, Biological Sciences
Committee: Dr. Eric Hayden, Biological Sciences and Brad Morrison, Biological Sciences and Matthew Ferguson, Physics
Date: July 10, 2018
Time: 10:00 a.m.
Location: Multipurpose Building, Room 108
Read Bryce LaFoya's Abstract Here
Cellular signaling pathways provide cells with the means to sense their environment and communicate with other cells. The Notch signaling pathway is comprised of a set of protein machines which work in unison to coordinate cellular processes in response to stimuli coming from neighboring cells and changing microenvironmental conditions. Being an important mode of cellular communication, Notch signaling is crucial to many processes involved in development and disease. During Notch activation, information about the extracellular environment is fed into the cell and relayed to the nucleus through a number of biochemical processes. The information-rich messages carried by Notch signaling is used to make genetic decisions through alteration of gene expression which ultimately controls cellular physiology. Critical to Notch function, are a series of regulatory steps which serve as points of integration
where other sources of information are fed into the Notch pathway. Through a series of experiments, we have uncovered novel regulatory mechanisms by which Notch signaling is controlled. Through bettering our understanding of this critical mode of cellular communication, we prime science with the knowledge which may one day fuel the development of new therapeutic strategies to combat Notch-related diseases.
Title: Empirical Investigations of RNA Fitness Landscapes: Harnessing the Power of High-Throughput Sequencing and Evolutionary Simulations
Program: Doctor of Philosophy in Biomolecular Sciences
Advisor: Dr. Eric Hayden, Biological Sciences
Committee: Dr. Matthew Ferguson, Physics and Dr. Elton Graugnard, Materials Science and Engineering
Date: July 11, 2018
Time: 9:30 a.m.
Location: Micron Business & Economics Building, Room 1208
Read Devin Bendixsen's Abstract
Fitness landscapes represent the mapping of genotype (sequence) to phenotype (function or fitness). The architecture and structure of fitness landscapes are a key determinant of evolutionary exploration and navigation. RNA enzymes (ribozymes) are an attractive model system for the construction of empirical fitness landscapes. Ribozymes function as both a genotype (primary RNA sequence) and a phenotype (catalytic function). Applying novel high-throughput assays with data-driven evolutionary simulations, several ribozyme fitness landscapes were constructed and assessed to answer important evolutionary questions. The fitness landscape at the intersection of two ribozyme functions (self-cleavage and self-
ligation) revealed that extensive functional overlap might promote the evolution of novel traits (innovations). Comparison of low-dimensional and high-dimensional fitness landscapes found that
increased dimensionality in RNA fitness landscapes had the potential to circumvent fitness valleys, however indirect pathways also harbored stasis genotypes isolated by reciprocal sign epistasis
(mutational interactions). Ancestral sequence resurrection was used to resurrect a phylogenetic fitness landscape of a natural self-cleaving ribozyme that is highly conserved in mammals. This led to the identification of a single high-activity ancestral sequence that was highly conserved throughout taxa indicating a selection pressure for ribozyme functionality. Analysis of published RNA fitness landscapes showed a predominance of negative epistasis in vitro and in vivo. Construction and analysis of empirical RNA fitness landscapes provides tractable insight into evolutionary processes, mutational pathways and the predictability of evolution.
Title: New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems using Coarse-Grained Molecular Dynamics
Program: Doctor of Philosophy in Materials Science and Engineering
Advisor: Dr. Eric Jankowski, Materials Science and Engineering
Committee: Dr. Peter Mullner, Materials Science and Engineering, Dr. Scott T. Phillips, Materials Science and Engineering, and Dr. Carla E. Reynolds, Materials Science and Engineering
Date: July 12, 2018
Time: 9:00 a.m.
Location: Micron Engineering Center; Room 106
Read Stephen Thomas's Abstract Here
This research aims at developing new computational methods to understand the molecular self-assembly of reacting systems whose complex structures depend on the thermodynamics of mixing, reaction kinetics, and diffusion kinetics. The specific reacting system examined in this study is epoxy, cured with linear chain thermoplastic tougheners whose complex microstructure is known from experiments to affect mechanical properties and to be sensitive to processing conditions. Mesoscale simulation techniques have helped to bridge the length and time scales needed to predict the microstructures of cured epoxies, but the prohibitive computational cost of simulating experimentally relevant system sizes has limited their impact. In this work, we develop an open-source plugin for the molecular dynamics code HOOMD-Blue that permits epoxy crosslinking simulations of millions of particles to be routinely performed on a single modern graphics card. Using these capabilities, we are able to use ensembles of epoxy processing pathways to obtain realistic bond kinetics and relaxation times that sensitively depend on stochastic bonding rates and a diffusive drag parameter respectively. This work also demonstrates the first implementation of fully customizable temperature-time curing profiles and the largest cross-linked structures obtained using molecular dynamics simulation. We evaluate coarse-grained models based on Dissipative Particle Dynamics (DPD) and compare with Lennard-Jones(LJ) models for their suitability to study glassy dynamics which is important for modeling epoxies or any other glassy material. We find that “hard” particle potentials such as the LJ potential are necessary to model glassy materials and characterize multiple methods for measuring the glass transition temperature (Tg) in simulations. We find that variations in temperature-time curing profiles result in significant differences in the final cured morphologies. Finally, we apply our general techniques to the specific DGEBA/DDS/PES system and validate our predicted glass transition temperatures against experiment.