Upcoming Doctoral Dissertations

" The field of active imaging has advanced rapidly in recent years, driven by its critical role in the emerging autonomous era. Light Detection and Ranging (LiDAR) systems have been significantly developed to provide accurate three-dimensional representations of the environment. However, this is only a first step toward comprehensive scene analysis, as true understanding requires more than just spatial awareness—it also necessitates an understanding of the material properties of objects." Read more...

" This work focuses on the computational complexity of the Diophantine problem for orientable and nonorientable quadratic equations over wreath products of finitely generated abelian groups. The Diophantine problem for quadratic equations naturally generalizes fundamental (Dehn) problems of group theory such as the word and conjugacy problems. Furthermore, there is a deep relationship between quadratic equations and compact surfaces, which makes quadratic equations an interesting object of study. In this work, we aim to provide a complete classification of cases, depending on the relationship between characteristics of the top group and characteristics (such as genus) of a given equation, when the Diophantine problem is NP-complete or polynomial-time decidable. " Read more...

" Over the past decade, lithium-ion batteries have evolved into indispensable energy sources for portable devices, electric vehicles, and large-scale energy storage systems. Currently, commercially available batteries predominantly utilize a limited set of cathode types composed of transition metals (TMs) and lithium (Li) frameworks. However, even as researchers continually refine these layered frameworks, they often suffer from irreversible phase transitions, oxygen release, and lattice distortion under prolonged cycling and at extremely high voltage." Read more...

" Quantum technologies rely on robust single photon sources, whose emission can be precisely controlled for use in secure quantum communication, sensing and scalable on-chip photonic circuitry. Achieving such control typically requires quantum emitters (QEs) that feature narrow spectral linewidth and high brightness. Equally important is the ability of dynamic high-speed manipulation of these QEs to tune their emission energies on demand for photonic circuit integration to enable on-chip technologies. While 2D semiconductors such as hexagonal boron nitride (hBN) and transition metal dichalcogenides (TMDs) have proven fertile ground for discovering new quantum light sources, scalable fabrication of QEs with desired properties for quantum information technology and integrating them with advanced tuning strategies remains a critical challenge as being addressed in my work. " Read more...

" The complex nature of flow and transport in porous media has led to substantial research across various disciplines in recent decades. One of the key porous media properties that controls fluid flow is permeability. In subsurface environments, the spatial variability of permeability, along with the existence of natural fractures and anisotropic properties of the media, result in highly unpredictable flow patterns. A comprehensive investigation of permeability across scales— from pore to field scale —is essential for enhancing fluid behavior models in applications such as carbon sequestration, groundwater management, and energy storage. " Read more...

" Stereolithography is a popular additive manufacturing method used broadly in producing complex and geometrically designed objects. This technique has also become the preferred method for creating functional composites due to the choice of available reinforcement materials and the potential to distribute, concentrate, or align reinforcement phases by design. External factors such as electric, magnetic, and acoustic fields can control the arrangement of fillers, allowing the synthesis of composites with unique and anisotropic mechanical or thermal properties. The main challenges for producing lightweight components are optimal placement and alignment of reinforcing fibers." Read more...

" Sustainable product design requires new perspectives and methods to achieve environmental, social, and economic success. The multi-agent, multi-variable, complex environment in which stakeholders of a product coexist creates a complex system, which is known as the wicked problem of sustainability. Sustainable product design is an approach to addressing sustainability challenges through product development processes and tools. The number of sustainable design methods has been increasing rapidly in recent years, but their adoption is limited, and many of these methods exclusively focus on the environmental impacts of products rather than taking a holistic perspective that includes social and economic sustainability. " Read more...

" This dissertation focuses on the application of artificial intelligence, computational optimization, and advanced materials to enhance the design, monitoring, and resilience of civil infrastructure. Traditional methods for infrastructure design and maintenance are inefficient and labor-intensive, prompting the need for AI-driven solutions to automate and optimize structural performance. " Read more...

" Mobile robot perception and navigation have gained increasing attention in recent years, driven in part by the growing focus on autonomous driving. However, there are still challenges in both single and multi-robot scenarios, stemming from challenging environmental conditions, limited communication bandwidth, and the complexities of fostering efficient collaboration among neighboring robots. One valuable tool for addressing these challenges is the factor graph, a graph structure widely employed for modeling probabilistic inference problems. In this thesis, we transform common perception and navigation problems into factor graph optimization problems, offering innovative solutions that advance beyond the current state-of-the-art barriers in both single and multi-robot scenarios." Read more...

"The revolution in sensing and monitoring technology has attracted interest in energy harvesting to reduce the dependence on depletable batteries. Piezoelectric transduction is an effective approach to harvest energy from ambient vibrations, which are available through different environmental sources, for the purpose of developing self-powered and micro sensors. The design of a harvester is defined by an objective function under specific constraints, which are mostly geometrical depending on the available space to place such a harvester. Yet, attaching a piezoelectric energy harvester to an oscillating body element like a fish tail for the purpose of fish migration monitoring can impact its performance which needs to be assessed " Read more...

" Resolving two closely spaced point sources is essential in imaging and sensing. Resolution quality primarily depends on how small a separation can be distinguished and how reliable these distinctions are. Historically, resolution has been constrained by the Abbe-Rayleigh diffraction limit, a practical boundary tied to wavelength and aperture size, making overlapping signals difficult to discriminate by standard intensity measurements. Recent studies have successfully demonstrated super-resolution for two equal-brightness, incoherent sources. Yet, realistic factors like unequal brightness, partial coherence, noise, and signal losses remain critical challenges for practical super-resolution." Read more...

" Since its inception in the early 1960s, Light Detection and Ranging (LiDAR) has rapidly advanced, demonstrating its capabilities across various fields. On the other hand, elastography is an important tool for biomedical diagnoses to differentiate between healthy and cancerous tissue. To date, no research has combined LiDAR technology with elastography, and this dissertation presents the first successful attempt to explore this uncharted field. " Read more...

" Given the escalating impacts of climate change on coastal communities worldwide, there is a pressing need for enhancing coastal flood mitigation strategies in the face of increasing storm intensities and sea-level rise. Traditional approaches to coastal defense, typically reliant on hard infrastructure, have shown limitations in terms of environmental impact, sustainability, and adaptability to changing conditions. These limitations underscore the necessity for innovative solutions that integrate the resilience of natural systems with the protective certainty of engineered structures. However, a significant gap exists in quantitatively assessing the resilience of such hybrid systems during extreme events and in designing optimized characteristics considering the economic aspects of coastal defense projects. " Read more...

" Polymer-based electrolytes address the critical challenge of creating high-performance, safe electrolytes for electrochemical devices by offering enhanced safety and mechanical stability compared to traditional liquid electrolytes; however, they often fall short in achieving comparable ionic conductivity. To address this limitation, this project introduces an innovative design strategy leveraging the self-assembly of polymer-grafted nanoparticles to form efficient ion-conducting pathways for improved ionic transport. " Read more...

" Recent advancements in lithium-ion battery technology have made zero-emission electric vehicles (EVs) a reality, increasing the demand for next-generation batteries like all-solid-state batteries (ASSBs). These batteries, leveraging nonflammable solid electrolytes and lithium metal, offer higher safety and energy density. However, the introduction of solid-state electrolytes leads to solid–solid interfaces, where mechanical degradation from volume changes in the cathode during cycling significantly limits battery lifespan. Additionally, the low conductivity of solid-state electrolytes reduces the overall capacity. " Read more...

" Light olefins are essential chemical building blocks in the petrochemical industry, with ethylene serving as a key feedstock for producing polyethylene, ethylene oxide, and other important plastics and intermediates. Conventional ethylene production via steam cracking is energy-intensive with high carbon intensity. Ethane oxidative dehydrogenation (EODH) is a promising catalytic alternative to reduce operating temperatures and hamper side reactions. Despite the advantages, the formation of carbon dioxide as a byproduct remains a major obstacle for the broad industrial application of EODH, highlighting the need for effective catalysts with improved catalytic performance. Nickel oxide (NiO)-based catalysts have demonstrated potential for EODH at relatively low temperatures, with dopant incorporation (forming MNiO) leading to enhanced ethylene selectivity. However, the nanoscale, mechanistic role of dopants remains unclear due to limited understanding of active surface structures and dopant interactions with ethane-derived intermediates." Read more...

" Accurate prediction of Tunnel Boring Machine (TBM) performance is essential for ensuring that tunneling projects are completed on schedule and within budget. However, the generalization ability of machine learning (ML) to other projects is still under-explored. This study assesses the generalization ability of ML techniques for predicting penetration rate (PR) or advance rate (AR) in similar and dissimilar geological conditions, explores whether incremental learning (IL) supports long-term adaptation and impacts generalization. " Read more...

" The next couple of decades are gearing up to be pivotal in reducing our energy reliance on fossil fuels. The environmental consequences of fossil fuel combustion grow increasingly dire, and their reserves are diminishing. To get in front of this, sustainable alternative sources of energy are intensely sought out. Among these, biomass-derived fuels (known as biofuels) show the most potential due to their compatibility with our existing internal combustion engine designs, offering a transitional bridge between current systems and a greener future. However, biomass is initially broken down into raw form called biooils which is unusable due in part to its high oxygen content." Read more...

" This dissertation focuses on expanding the understanding of the wave attenuation of constructed oyster reefs. As nature based solutions, such as constructed oyster reefs, become the preferred method for coastal protection, quantitative guidance and information is needed to ensure appropriate application. This dissertation seeks to address this research need through (1) a systematic review, (2) field observations, and (3) a novel design approach. " Read more...

" The molecular features distinguishing tissue-specific stem cells from cancer-associated stemness remain poorly characterized. Here we are investigating the molecular events associated with dedifferentiation-induced oncogenic stemness within the intestinal epithelium, utilizing a Smad4 knockout:β-catenin gain-of-function (Smad4KO:β-cateninGOF) conditional mutant mouse model. The intestinal epithelium is spatially organized, delineating the proliferative crypt compartment from the differentiated villus compartment. The crypt compartment houses the stem cells which have been shown to be the cell-of-origin of colon cancer. " Read more...

"Open quantum systems are an intriguing area of quantum mechanics that focus on systems interacting with their environment. These systems are essential in understanding real-world applications like quantum computing, as they help researchers manage environmental influences on quantum states; analyze noise and information loss in quantum communication channels; improve the accuracy of quantum sensing. Among open quantum systems, non-Markovian systems, characterized by memory effects and feedback from the environment to the system, are specifically of interest due to their potential to preserve and revive quantum entanglement. All of these involve tracking and manipulating open quantum systems." Read more...

" This dissertation presents a comprehensive study on the design, simulation, fabrication, and testing of nanostructured sapphire optical fibers (NSOFs) embedded with gold nanorods (GNRs) for high-temperature optical sensing applications. Leveraging the thermal and chemical resilience of single-crystal sapphire and the plasmonic sensitivity of gold nanostructures, this work aims to overcome the inherent limitations of traditional silica fibers in harsh environments. A systematic finite-difference time-domain (FDTD) simulation framework is developed to explore how the aspect ratio (AR) and tilting angle (TA) of embedded GNRs influence their localized surface plasmon resonance (LSPR) response under temperature variations and hydrogen exposure. Special emphasis is placed on source-free sensing enabled by thermal energy harvesting, where the blackbody radiation from high-temperature environments excites plasmonic oscillations without the need for external illumination. " Read more...

" Catalytic conversion of hydrocarbons plays a critical role in the production of fuels, chemicals, and materials, contributing significantly to the development of cleaner and more sustainable chemical processes. Among these, selective oxidation and hydrogenation reactions are of particular importance, enabling the transformation of simple feedstocks into valuable intermediates while minimizing energy input and undesired byproducts. The performance of such reactions is largely governed by the physicochemical properties of the catalysts employed. In recent years, metal nanoparticles have emerged as promising catalysts due to their high surface area, tunable surface structures, and ability to form unique active sites when supported on appropriate materials." Read more...

" The three essays in this dissertation explore optimal execution strategies under capital ratio constraints, addressing critical challenges in financial regulation and risk management. These essays explore static and dynamic liquidation strategies under capital ratio chance constraints in both single-agent and multi-agent settings, highlighting their implications for regulatory compliance. " Read more...

" The role of extracellular matrix adhesion components in modulation of the treatment sensitivity of ovarian cancer (OC) cells is not well understood. Analysis of ovarian cancer TCGA gene expression data sets revealed an inverse correlation between genes involved in cell-cycle progression and extracellular matrix interactions including laminin-binding receptor integrin β4, a major component of extracellular matrix adhesion. Gene ontology analysis also showed that in patient populations with low integrin β4 expression, cell cycle-related programs were activated, while in populations with high expression of integrin β4, the activation of these cell cycle programs was lower. " Read more...

" Orbital debris is a pressing problem which presents a danger to global space operations and a barrier to continued development of the space economy and space infrastructure. As research continues regarding orbital debris, there is a need for tools to understand the system-level implications of orbital debris solutions. This research considers the orbital debris problem as a dynamic process. Based on dynamic system theories, time-series variables of the numbers of orbital debris, orbital objects, and object launches are causally linked, which means they share a common system attractor manifold. " Read more...

" Multiple Myeloma (MM) is a plasma cell malignancy arising in the bone marrow. Despite advancements in therapies, this disease becomes refractory such that relapse and death are inevitable. Immune evasion and drug resistance are partly due to MM-osteoblast cell interactions in the bone marrow microenvironment (BME). We conducted in-vitro studies that provided insights potentially leading to a treatment-relevant hypothesis. Several human MM cell lines (MM.1S, U266, NCI-H929) representing the spectrum of genetic lesions seen in this type of cancer, were co-cultured with osteoblasts for two days to assess proliferation in response to two proteasome inhibitors, bortezomib, and carfilzomib. " Read more...

" This dissertation investigates asset return predictability from both cross-sectional and time-series perspectives. It emphasizes machine learning methods to improve the economic significance of predictions. Traditional empirical approaches mainly rely on linear models and in-sample analysis. However, these methods often lack out-of-sample robustness and fail to capture complex interactions among predictors. This dissertation consists of three essays addressing these limitations. " Read more...

" The three essays presented in my dissertation proposal explore biases and decision-making patterns in financial investments from various perspectives, showing how institutional preferences, artificial intelligence, and cultural dynamics influence investment behavior and outcomes." Read more...

" Despite its normative appeal, portfolio theory often faces significant challenges in practical implementation due to estimation errors. These frictions manifest as model risk, the uncertainty stemming from the parameters used in the model's input -- commonly known as estimation risk. This dissertation introduces reinforcement learning (RL), a machine learning technique designed for sequential decision-making, as a novel approach to mitigate estimation risk in asset allocation and pricing. Overall, it highlights two key contributions of RL in finance. First, it demonstrates RL's ability to address estimation risk while preserving tractability in risk-managed portfolios. Second, it shows the value of embedding domain knowledge or equilibrium principles into both asset allocation and pricing. " Read more...

" Cognitive abilities develop as individuals accumulate life experiences from interacting with their complex environments. This progression is influenced by various events and objects encountered along the way. However, understanding how humans formulate perceptions in response to diverse external stimuli poses ongoing challenges in behavioral research, especially within the constraints of traditional laboratory settings. These environments often suffer from limited budgets, reliance on simplistic features, and the inability to fully capture the interrelationships among stimuli. " Read more...