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Fall 2022: OU Engineering Presents Dissertation Excellence Awards

February 10, 2023

Fall 2022: OU Engineering Presents Dissertation Excellence Awards

Recipients of the Engineering Dissertation Award

Ten Gallogly College of Engineering students at the University of Oklahoma were selected to receive Engineering Dissertation Awards, a $5,000 award created to encourage doctoral students to graduate with excellence. The award helps scholars near completion of their Ph.D., says Zahed Siddique, the college’s associate dean for research who heads the committee. 

Established in 2018, the Engineering Dissertation Award is made possible by the Thomas Ira Brown, Jr. Endowed Scholarship. Brown (1926-2016) created a new market for electronic control of industrial gas turbines. He earned a bachelor’s degree in electric engineering from OU in 1950. 

Fall 2022 recipients are:  

Tanvir Ahad, School of Aerospace and Mechanical Engineering, recommended by Jie Cai, Ph.D.

Topic: “Investigating creativity in engineers and effects of indoor environment quality (IEQ): A temporal approach” 

Research: “Investigations of creativity have been an intriguing topic for a long time, but assessing creativity is extremely complex. Creativity is a cornerstone of engineering disciplines, so understanding creativity and how to enhance creative abilities through engineering education has received substantial attention. Fields outside of engineering are no stranger to neuro-investigations of creativity and although some neuro-response studies have been conducted to understand creativity in engineering, these studies need to map the engineering design and concept generation processes better. Using neuroimaging techniques alongside engineering design and concept generation processes is necessary for understanding how to improve creativity studies in engineering. Recently, a growing number of studies have revealed that some types of indoor environmental stimuli can enhance human creativity. Further, specific temporal dynamics of cognitive processes are crucial for generating creative ideas. However, how the temporal dynamics of creativity are influenced by the indoor environment remains unclear. This research found that each stage of the temporal dynamics of creativity may be differently correlated with neural function. Further, indoor environmental factors may have various, and sometimes contrasting, effects on the temporal dynamics of creativity. Despite recent progress, there are significant gaps in understanding the effects of indoor environmental quality (IEQ), especially air quality and factors related to visual, thermal and acoustic comfort that are closely tied to performance on cognitive tasks. This is due to the lack of understanding of the effects of IEQ on human physiological and neural responses. Nonetheless, this is the first study to clarify the influence of indoor environmental settings on the temporal dynamics of creativity from the perspective of neuroscience.” 

Sergio Pineda Castillo, Stephenson School of Biomedical Engineering, recommended by Chung-Hao Lee, Ph.D.

Topic: “Development of patient-specific shape memory polymer for the treatment of intracranial aneurysms”

Research: “My research is focused on developing biomaterials for the treatment of intracranial aneurysms (ICAs), a malformation of the brain arteries that can be fatal. Current methods to treat ICAs suffer from a limited capacity to treat patients effectively, which leads to repeated surgeries. In my project, I am developing a patient-specific device that will be tailored to each individual patient. This will guarantee that the aneurysm sac is 100% occupied by the biomaterial and will, ultimately, prevent rupture of the aneurysm. This individualized medicine approach has the potential to improve the long-term efficacy of endovascular aneurysm therapy, making it safer and durable. My work has been focused on the development of the biomaterial’s properties, from a materials science perspective. I have performed experimental research to demonstrate the use of shape memory polymers and their thermomechanical properties in the occlusion of in vitro aneurysms. My findings will be the groundwork for the next stages of the project where we will test the device in animal models before clinical translation.”

Jasmine DeHart, School of Computer Sciencerecommended by Dean Hougen, Ph.D.

Topic: "Visual privacy mitigation strategies in social media networks and smart environments”

Research: “Massive amounts of visual data, such as images and videos, are shared and collected daily across various technologies and environments. With the popularity and advancements of social media network platforms and smart environments, there is an unprecedented challenge with discovering, reducing, and protecting the visual data of individuals in these environments. The visual privacy concerns that arise can happen intentionally or unintentionally by the individual, others in the environment, or the company. By understanding the visual privacy implications of these environments, we can inform the infrastructure design of the data collection process all the way through the system's deployment.  However, there are significant research challenges in ensuring visual privacy in social media networks and smart environments without dismissing an individual's subjectivity towards visual privacy, the influence of visual privacy leakage from individuals in the environment, and the environment's infrastructure design and ownership. This dissertation employs user studies, machine learning, network science, and statistics to explore social media networks and smart environments and their visual privacy risks. This dissertation contributes to the advancement of visual privacy solutions in social media networks and smart environments, advocating for the need for responsible visual privacy mitigation methods in these environments. It also strengthens the ability of researchers, stakeholders, and companies to protect individuals from visual privacy risks throughout the machine learning pipeline.”

Alejandra Gomez, School of Chemical, Biological and Materials Engineeringrecommended by Steven Crossley, Ph.D.

Topic: “Conversion of renewable oxygenates compounds to high-value products using reducible oxides as catalysts”

Research: “Sustainable upgrading of biomass for the generation of fuels and chemicals requires the development of processes that can efficiently remove oxygen from a variety of oxygenated compounds. My research focused on studying different catalyst supports for the deoxygenation of biomass-derived compounds by combining material synthesis, catalyst characterization, and reaction kinetics to gain a deeper understanding of the surface reactions that govern the overall process. Recently, I have developed a kinetic model that reveals the possible reaction mechanism and nature of the active catalytic site for the deoxygenation of carboxylic acids on molybdenum oxide catalysts.”

Seren Hamsici, Stephenson School of Biomedical Engineering, recommended by Handan Acar, Ph.D., Qinggong Tang, Ph.D., and Stefan Wilhelm, Ph.D.

Topic: “Engineering a peptide-based tool for biomedical applications”

Research: “The structural organization and functional capabilities of natural materials have led to numerous technological and scientific developments. Creating materials that can address enduring problems in biomedical engineering involves adapting engineering concepts found in biological models. By taking inspiration from amyloid beta aggregation, we engineered peptide building blocks called CoOPs 'co-assembled oppositely charged peptides' with multiple functions to enhance the chemical and structural diversity. The change in intermolecular interactions was utilized as a controllable adjuvant and showed differential production of antigen-specific antibodies.”

Tahere Hemati, School of Electrical and Computer Engineering, recommended by Binbin Weng, Ph.D.

Topic: “Design and fabrication of active optical gratings based on low-cost midinfrared emitters for gas sensing”

Research: “In recent years optical gratings, due to their high flexibility and small size nature, have attracted significant attention and are considered promising candidates for gas sensing. To the best of our knowledge, the research emphasis on the gas sensing development has been mainly focused on the near-infrared spectral range. However, compared with the near-infrared region, molecular species in the mid-infrared range show intrinsic absorption bands with much larger absorption coefficients. Therefore, the optical sensors operating in the mid-infrared range offer much higher device sensitivity. Generally, the development of grating-based mid-infrared gas sensing research is still in its early stage and not ready for commercialization. However, the small size, weight, power consumption, and cost (SWaP-C) features of these sensors can show great potential to stimulate the newly emerging technology such as the “Internet of Things, which heavily relies on modern SWaP-C sensor devices.”

Sai Kiran R. Maryada, School of Computer Science, recommended by Dean Hougen, Ph.D., and Bin Zheng, Ph.D.

Topic: "Developing robust machine learning-based CAD models to assist disease diagnosis and prognosis"

Research: "Developing Computer-aided-detection (CAD) schemes have been an active research topic in medical imaging informatics (MII), with promising results in assisting clinicians in making better diagnostic decisions in the last two decades. Optimizing every step in the CAD pipeline assists with state-of-art image processing, and machine learning/deep learning algorithms help build robust CAD schemes. My research illustrates multiple studies investigating the feasibility of developing CAD schemes based on machine learning and deep learning. In addition, two novel deep learning architectures have been developed based on Convolutional Neural Networks (CNN) & U-Net, which have been evaluated for enhanced accuracy. Furthermore, I have developed an algorithm to create synthetic data that helps in improving deep learning algorithms' performance."

Colton Ross, School of Aerospace and Mechanical Engineering, recommended by Chung-Hao Lee, Ph.D.

Topic: “Experimental tissue biomechanics, image-based analysis, and finite element simulation of the tricuspid valve”

Research: "I have focused on refining our understanding of the tricuspid valve function and tissue biomechanics through experimental and computational work. For the experimental component, I have performed biaxial mechanical testing and microstructural analysis of the tricuspid valve leaflets in healthy and disease-emulating conditions, which is useful for accurate implementation of these tissue behaviors into computer simulations. For the computational analyses, I have used medical images to construct simulations of patient-specific tricuspid valve behaviors, providing detailed predictions of the stresses and strains the tissues experience in the body. With these studies and developments, the groundwork has been set for future progress toward an individualized medical guidance framework of tricuspid valve disease and treatment."

Zuyuan Zhang, School of Computer Science, recommended by Sridhar Radhakrishnan, Ph.D.

Topic: “Causal failures and edge augmentation in networks”

Research: “Node failures have a terrible effect on the connectivity of the network. In traditional models, the failures of nodes affect their neighbors and may further trigger the failures of their neighbors, and so on. However, it is also possible that node failures would indirectly cause the failure of nodes that are not adjacent to the failed one. In a power grid, generators share the load. Failure of one generator induces extra load on other generators in the network, which could further trigger their failures. We call such failures causal failures. In this dissertation, we consider the impact of causal failures on the network’s connectivity in terms of the number of connected components and their sizes. More specifically, we formally define causal failures in a given network and propose two problems that address the network’s robustness and vulnerability, respectively. The first problem that corresponds to robustness aims to find the maximum number of causal failures while maintaining a connected component with a size of at least a given integer. More specifically, we are looking into the number of causal failures we can tolerate yet have most of the system connected with ? being used to parametrize. The second problem deals with vulnerability, wherein we aim to find the minimum number of causal failures such that there is at least k connected components remaining. We are looking for the set of causal failures that will result in the network being disconnected into k or more components. We show that the decision versions of both problems are NP-complete and correspondingly provide integer linear programming. Given the hardness of both problems, we design polynomial-time heuristic algorithms to solve them approximately. Finally, we present an illustrative example to compare the performance of heuristics with that of integer linear programming and analyze the scalability of the proposed algorithms by performing experiments on another two networks. Moreover, we also consider adding edges to the original network (augmentation problem) in such a way that the network remains connected after applying each of the causal failures, or the largest connected component in the disconnected network is at least a given specified size ??n (?-giant component), where n is the number of nodes in the original network.”

Zhihao Zhao, School of Electrical and Computer Engineering, recommended by Samuel Cheng, Ph.D., Shangqing Zhao Ph.D., and Bin Zheng, Ph.D.

Topic: “Enhanced capsule-based networks and their applications”

Research: Current deep models have achieved human-like accuracy in many computer vision tasks, but they still suffer from significant weaknesses. To name a few, it is hard to interpret how they make decisions, and it is easy to attack them with tiny perturbations. However, human's vision system is robust to these problems based on parsing an object into a part-whole hierarchy. In my dissertation, the multiple layers of a model parse an object into a part-whole hierarchy like humans, and thus achieves what human vision system achieves, such as equivariance, model interpretability, and adversarial robustness.”

By Lorene A. Roberson, Gallogly College of Engineering