Summer 2022: OU Engineering Presents Dissertation Excellence Awards
Eight Gallogly College of Engineering students at the University of Oklahoma were selected to receive Engineering Dissertation Awards, a $5,000 award 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.
Summer 2022 recipients are:
Adrien Badré, School of Computer Science, recommended by Chongle Pan, Ph.D.
About Adrien Badré: “Adrien has proven to be an outstanding interdisciplinary researcher. He published his first research paper in “IISE Transactions on Healthcare Systems Engineering” in 2020 during his master’s degree studies. His work demonstrated how a decentralized decision-making framework, using Ethereum smart contracts, enhances patient transfer in a hospital system. He then published two highly impactful first-author papers on applying interpretable machine learning to predictive genomics in my lab. The first paper, “Deep neural network improves the estimation of polygenic risk scores for breast cancer,” demonstrated that neural networks improve the polygenic score prediction of breast cancer using large-scale genomics data. This study was published in the “Journal of Human Genetics” in 2021 and has been cited 14 times. The same year, he was awarded the 2020-2021 School of Computer Science Doctoral Research Award highlighting his exceptional work. Subsequently, he extended his research and proposed a novel, more interpretable neural network architecture. This architecture, named LINA, enhances the understanding of disease with genetics components, such as breast cancer."
Gokhan Gunay, Ph.D., Stephenson School of Biomedical Engineering, recommended by Handan Acar, Ph.D., Qinggong Tang, Ph.D., and Stefan Wilhelm, Ph.D.
Topic: "Peptide aggregation induced immunogenic cell death"
Research: "In this dissertation, an engineering platform called co-assembly of oppositely charged peptides (CoOP) and its applications in ICD and DAMP release is studied. Inspired by the aggregation induced-cell membrane damage of Ab, we introduced two oppositely charged peptide pairs called EFFIIE and KFFIIK forming [II] peptide. We first studied the cellular consequences of [II] peptide and found that [II] peptide aggregation induces ICD and DAMP release in several healthy and cancer cell lines. Leveraging the DAMP-releasing effect of [II] peptide, we developed a quadrivalent influenza vaccine and found significantly elevated IgG1 and IgG2a responses, corresponding to the engagement of Th2 and Th1 immunity, respectively. Next, we showed the controllability of [II] peptide aggregation, which ultimately leads to control over DAMP release and the immunogenicity of dying cells. Different aggregation profiles lead to differential membrane damage and DAMP release profiles. When cells were treated with [II] peptide at different aggregation states, the dying cells led to protection in a prophylactic vaccination model against breast cancer, and differences in aggregation status led to differential protection levels. Lastly, we used [II] peptide aggregation model in a three-dimensional spheroid model of breast cancer and compared this activity with known chemotherapeutics. [II] peptide-treated spheroids displayed superior DAMP release and dendritic cell activation. Successful ICD and DAMP release stimulation is a fundamental and challenging strategy to induce a robust antigen-specific immune response. [II] peptide aggregation is a controllable tool that can be used as an adjuvant in vaccination strategies and a treatment modality against cancer."
Zhimin Jiang, Ph.D., School of Aerospace and Mechanical Engineering
Topic: “Building cluster control to enable grid reliability and efficiency support"
Research: “Power system operators actively seek solutions to increase electric grid power flexibility and inertia, to accommodate deeper renewable integration. Buildings account for 75% of the total electricity use in the US and have great potential for grid reliability support at various time and spatial scales. Due to the limited bidding power of individual buildings, grid services are often provided by a fleet of small buildings managed by tailored coordination strategies. I developed two families of control methods for building cluster energy management based on the control time frequency and inter-building coordination mode: (1) dictatorial load modulating control strategies formulated under a specific context of distribution voltage regulation, and (2) market-based load shifting control achieved through a game-theoretic control framework.”
Sai Teja Kanneganti, Ph.D., School of Computer Science, recommended by Phillip Chilson, Ph.D., Dean Hougen, Ph.D., and Jin-Song Pei, Ph.D.
Research: Machine learning (ML) is becoming increasingly sought after in diverse domains. Unfortunately for this objective, most ML research has focused too much on improving performance on evaluation metrics such as accuracy to the exclusion of other qualities like interpretability. However, to make important decisions, ML models need to be interpretable. The goal of interpretable machine learning (IML) is to build models that are understandable to users. One approach to IML is to have meaning to each of its components. Thus, IML aids in building models that are trustworthy and improve fairness in artificial intelligence. In informed ML, prior knowledge is explicitly integrated into the ML pipeline/training process. Interactive ML enables ML models to be interactively steered by people and is more advantageous for tasks where human knowledge is needed in the analysis process. This work proposed the I3 framework that brings together the ideas of being informed, interactive, and interpretable. In this work, we reintroduced, highlighted, and established the larger picture to one approach in the context of being informative, interactive, and interpretable. This approach is used to approximate the kinematics of a robotic arm using interpretable artificial neural networks (ANNs) and this approach leads to training success, good generalization, and interpretability.
Haopeng Liu, Ph.D., School of Aerospace and Mechanical Engineering, recommended by Yunpeng Zhu, Ph.D., and Jie Cai, Ph.D.
About Liu: After earning a Ph.D. from OU, Haopeng Liu joined the Center for Environmental Energy Engineering at the University of Maryland as a research assistant in the modeling and optimization consortium group in January 2023. His thesis presented an efficient and robust gray-box dynamic modeling approach for vapor compression systems to support control optimization. His research interests include dynamic modeling of HVAC systems with specific applications for improving energy efficiency and controlling variable-speed HVAC equipment. The research work is targeted at generating a generalized simulation tool to facilitate the optimal design of HVAC systems and could bring a significant impact on the HVAC industry and research community.
Farid Omoumi, M.D., Ph.D., School of Electrical and Computer Engineering, recommended by Hong Liu, Ph.D.
Topic: “Subjective evaluation of the in-line phase-sensitive imaging system in breast cancer screening and diagnosis”
Research: “Breast cancer is the most common non-cutaneous cancer among women. The phase-sensitive breast x-ray imaging is a promising and novel imaging technique that can improve cancer detection sensitivity and specificity while reducing the radiation dose. Many studies have investigated the overall performance of phase-sensitive imaging systems by objective analyses. However, human radiologist is still end-users in diagnostic radiology. Hence, I evaluated this prototype imaging system using subjective observer performance and preference studies to further optimize the imaging technique according to human performance in accurately diagnosing the potential pathologies in breast tissue.”
Monique Shotande, Ph.D., School of Computer Science, recommended by Andrew Fagg, Ph.D.
Research: “"The dynamics of locomotion involve a fine-tuned, continuous feedback loop between processes in the brain, functioning of the muscles, and interactions with the environment. Neurological or motor disabilities can often disrupt this loop, altering muscle activation patterns and corresponding behavior. To maintain some level of function, the brain and body adapt atypical locomotive strategies that are often sub-optimal, which can negatively impact overall health and inhibit continued motor learning. Therefore, it is crucial to accurately and holistically characterize and diagnose motor behavior when providing interventions. I propose an approach for comprehensively describing novel variations in motor behavior within and across individuals with and without an innovative surgical amputation. In addition, I propose an approach for relating brain activity to motor behavior in infants at risk of Cerebral Palsy."
Leili Soltanisehat, Ph.D., School of Industrial and Systems Engineering, recommended by Kash Barker, Ph.D.
Topic: “Risk-based evaluation and management of cyber-physical-social systems during the pandemic crisis”
Research: “This research focuses on risk-based evaluation and management of cyber-physical-social systems during a pandemic crisis. The novel coronavirus (COVID-19) epidemic has caused serious challenges for the world’s countries. The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of the best mitigation policy. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socio-economic impact of control strategies. To maximize the effectiveness of controlling policies during a major crisis like a pandemic, we propose two mathematical frameworks which optimize the contorting policy while considering three sets of important factors, including the epidemiologic, economic, and social impact of the pandemic. The proposed formulation is implemented on a dataset that includes 11 states, the District of Columbia (including the states in New England and the mid-Atlantic), and 19 industries in the United States. In the second formulation, the economic impact is measured using the supply-side multi-regional inoperability input-output model, accounting for the inoperability of each industry to satisfy the demand of final consumers and other industries, due to its closure. This will give a holistic view of the impact of the pandemic policy on the country's health, economy, and social justice aspects.”
By Lorene A. Roberson, Gallogly College of Engineering
