Marine heatwaves have altered ecosystems globally, including changing community composition and facilitating the spread of invasive species. In south Puget Sound, Washington (USA), non-native Pacific oysters (Magallana gigas) have been farmed extensively for almost a century and grown in enhancement sites, however, they have only recently recruited in the wild. This study explores how the appearance of Pacific oysters was related to spatially (eight sites) and temporally (decade) warmer summer water temperatures in south Puget Sound and compares oyster persistence across five sites where recruitment occurred. The largest recruitment event from 2012-2020 was in the summer of 2015, in the middle of the east Pacific Blob marine heatwave which led to warm water temperatures off the west coast of North America. Throughout the study period, the number of oyster recruits each year was positively correlated with warmer water temperatures. Oyster population densities differed across the five sites where recruitment occurred and generally declined after 2015, but showed no site by year interactions, which is consistent with spatially-variable recruitment and similar post-recrutiment survival. Mean oyster shell heights also differed among sites, which could reflect different growth trajectories or recreational harvest patterns. This study supports the claim that warming sea surface temperatures may interact with species introductions to change modern biogeography. 

Economic sanctions as a means of pressuring nations to improve human rights protections or end human rights abuses have become an increasingly common practice in recent years. Although the efficacy of sanctions for humanitarian ends remains under heavy scrutiny, the United States Department of State continues to both implement new sanctions and enforce existing policies of this kind. However, there is a notable discrepancy between regimes accused of human rights abuses by non-governmental organizations and those receiving these types of sanctions. This research aims to unveil potential factors that may explain this gap. I theorize that while investment and trade may protect a country from economic sanctions in an effort to keep certain markets open, past or present adherence to communist ideology increases the likelihood of receiving sanctions on the grounds that communism remains a perceived threat. To test my theories, I identify a set of countries that are currently verified by third party organizations as human rights violators. At this point, I conduct multivariate regression analysis to observe the relationship of both economic interest and conflicting ideology and the presence of sanctions citing human rights violations. While I expect to find that while both factors contribute to the presence of sanctions as outlined, I also theorize that economic interest will have a greater influence. This study serves to identify specific influences on sanctions that will enrich future discourse on their implementation.

The measurement problem is the challenge of reconciling the probabilistic and deterministic aspects of Quantum Mechanics. In this study, our primary aim is to unravel the measurement problem by investigating the possibility that quantum collapse occurs according to the probabilities presented by Born's Rule due to a perturbation on the system, appearing during the measurement process. Our group employs a multifaceted approach, where we examine the interplay between time dynamics and classical limits, alongside the influence of time-(in)dependent perturbations. Computational simulations serve as our primary tool in this exploration. One part of our group worked with a 3-well system with a time-independent perturbation, another part looked at a 2-well system with a time-dependent perturbation, and the last part saw what a time-independent perturbation does to a 2D-well system. We anticipate that our investigations will uncover critical parameters that are expected to yield probabilities consistent with Born's Rule, a foundational principle of Quantum Mechancis. This research points towards a potential reconsideration of quantum collapse as a dynamical, affected by the perturbation introduced by measurement. While preliminary, these findings contribute to the ongoing discourse in Quantum Mechanics and may offer insights for future theoretical developments and applications.

As computers have progressively gotten smaller and more powerful, we approach a physical barrier to further progress. Due to this, expanding resources have been devoted to developing quantum computing where the obstacles to progression are not physical. This technological advancement would have significant implications for various technological fields. Entities with access to this new technology would not only be able to benefit from it but would be capable of abusing it. These misuses could pose significant ethical risks, such as undermining the security and privacy of data and creating new forms of financial inequality. This literature review reveals the incoming disruption from quantum computing and the dissension between government, the public, and industry to ensure the responsible and beneficial use of quantum computing. Industry wants to develop and commercialize quantum computing as quickly as possible to gain an advantage in applicable industries. Government wants to balance the opportunities and risks that quantum computing poses for national and global security, but at the cost of expedited development. Though regulation and policy usually lag behind technical development, quantum computing's significant force will compel government and industry to adjust policy. The key future implications of this research are to develop and implement cross-disciplinary ethical principles and standards for designing and adopting quantum computing technologies. Achieving this by anticipating the possible impacts of quantum computing on existing systems and infrastructures, and developing strategies and policies to mitigate the risks and maximize the benefits. To ultimately support and promote research and innovation in quantum computing that defines a common good, and seeks to fulfill it.

In this research project, we delved into the realm of gate-based quantum computation with a focus on qudit-based quantum computation. In the era of Noisy Intermediate-Scale Quantum (NISQ) computation, there are many avenues for physical implementations of qudits, such as trapped ions, superconducting circuits, and photonic systems. We primarily studied trapped ion qudit-based computation, investigating the notion of universality and how arbitrary gate operations can be simulated by experimentally realizable transformations in such systems. More quantitatively, we analyzed the fidelity under the assumptions of rotation angle errors in trapped ion implementations of quantum gates. We proved several lower bounds for various connectivity graph designs applicable to the 5-level calcium ion under this model of assumptions. Our techniques also generalize to physical systems with more than 5 levels. Currently, our attention is directed toward understanding the impact of entanglement on the aforementioned dynamics and studying the notion of universality for multi-qudit systems. A related question we are trying to answer is how qubit circuits can be converted into qudit circuits to reduce a well-defined notion of "circuit complexity".

Quantum dots are nanometer scale semiconductor particles that have been extensively studied over the past decade. Colloidal quantum dots are dispersed in solution, and so can be easily deposited on a surface. This allows them to act as highly versatile quantum sensors. I am studying cadmium selenide quantum dots doped with manganese (Mn:CdSe). They possess a spin of 5/2, meaning they have six spin states, each corresponding to a different quantized energy. These six energies can be probed with photoluminescence spectroscopy, and theoretically appear as six distinct peaks in the spectrum. This allows us to use spectral analysis to read the spin state of a dot. Due to the Zeeman effect, the spin state energies are sensitive to applied magnetic fields. A simple sensing procedure first initializes the spin state, allows it to evolve under some magnetic field, and reads out the final spin state. My work focuses on the initialization and readout of the spin. For this purpose, I previously built a monochromator to characterize the quantum dots under pulsed excitation at various wavelengths, power, and temperature. I am measuring their properties using photon counting correlation measurements, photoluminescence spectra, and lifetime measurements. The goal of these results is to characterize the properties of these Mn:CdSe quantum dots to lay the groundwork for their development as a highly sensitive quantum sensor.

In the context of computer-aided design, researchers have studied how to reconstruct an input geometry in CAD by decomposing it into CAD primitives. Such reconstruction is useful for creating CAD designs for manufacturing applications. What we want to study is also object decomposition but towards a different goal: understanding object affordances and interactability. For example, a handle of a basket can be grasped or hung from a sticky hook, and we recognize this affordance or functionality because it has a certain shape (e.g. hook or rod). Prior research has identified eight types of shape primitives that are common in everyday objects, but the existing tagging process requires a high degree of modeling expertise. We aim to create a more automatic and easy-to-use tagging tool. Our proposed research is to develop user-in-the-loop methods for tagging shape primitives given an object geometry. This takes advantage of human intuition for how objects function and interact. We start with building an interface, where users sketch over the input mesh to indicate the region for fitting and select the type of primitive to be fit. On top of this, we plan to crop the selected mesh data to generate a reduced mesh that encompasses only the area selected by the user. Finally, we utilize differentiable rendering techniques to automatically optimize the shape parameters of user-selected primitives to fit our reduced mesh data. With this tagging tool, we can enable more people without modeling expertise to tag objects. Data generated with this tool can support future research that studies object affordances with learning, as well as improve applications in robotics, product design, and assembly design like FabHacks.

Nilpotency degrees of finite-dimensional quadratic algebras carry essential information for their combinatorial and homological applications. It is known that the maximal nilpotency degree a finite-dimensional quadratic algebra with n generators can contain is at least n+1 for all n > 2. However, the optimality of this bound is still unknown. I propose a geometric visualization of the algebraic varieties of all quadratic algebras with n generators in degree d to find the true optimal bound. I then utilize this visualization to construct a linear program that deterministically determines whether a finite quadratic algebra with n generators exists that has a nilpotency degree of at least d. Thus far, I have verified that this algorithm will give the desired optimal bounds and have completed an implementation using Sage. I expect to find the true optimal bound on the maximal nilpotency degree of a finite-dimensional quadratic algebra with three generators shortly. However, the algorithm will require revisions for higher values of n due to scalability issues caused by its computational complexity. Knowing this optimal bound would solve several open problems in ring theory, including bounding the computational complexity of computing the global dimension of Koszul algebras. Finding a bound that extends to all algebras, including non-quadratic algebras, would also bound the computational complexity of determining if a finitely presented graded algebra is finite-dimensional

Fractal dimension, a measure of geometric complexity, finds application in image analysis, biology and medicine, neuroscience, geology and various other fields, yet existing methods often lack adaptability to finite data sets. Using ideas rooted in geometric measure theory, such as Hausdorff measure and Frostman’s Lemma, this research introduces a novel approach to compute fractal dimensions for finite sets, addressing limitations of traditional methods. Using Python, we developed and tested an algorithm to validate known sets such as the unit interval, square, cube, and fractal objects including the Cantor set and Sierpinski triangle. Comparative analysis was also conducted on established methods, including box-counting and correlation integral algorithms, to demonstrate the algorithm's accuracy in determining fractal dimensions. Pivoting towards data sets, we expect to use the computed fractal dimension of real data as a tool for assessing data and optimizing data compression. Our methods offer an improvement as most existing techniques use statistical methods that are limited to integer dimensions. In addition, recent studies have shown that fractal dimension values can be useful as features in machine learning. We also improve upon the calculation of the local dimension of regions in a data set, allowing for additional insights into complex data sets. This includes identifying regions of high complexity, and we expect to show that this allows for the more effective use of algorithms such as principal component analysis. All of these are increasingly important in our society due to the abundance of high-dimensional datasets in both the physical and social sciences. Overall, the benefits of studying novel ways of calculating the dimension of large data sets include efficient representation of data, improved interpretability, and decreased computational burden, as well as detecting certain features in data such as regions of high complexity.

The Mid-Pleistocene Transition (MPT) was a major climatic shift in Earth’s history occurring between 1.2 and 0.7 million years ago. During the MPT, Earth’s glacial cycles shifted from a high frequency (~40 kyr), low amplitude cadence to a low frequency (~100 kyr), high amplitude cadence which has dominated since the MPT. While we are able to observe the MPT in benthic 𝛿¹⁸O records, our current ice core record only extends back 800 kyr and does not include a preservation of the entire MPT. COLDEX, a multi-institution collaboration, is seeking to find a region in Antarctica where a continuous deep ice core may preserve the MPT in order to better understand the underlying mechanisms that caused it. Ice at this depth, however, is subject to much different conditions than the ice cores that comprise our current record. My study aims to analyze how atmospheric gases, namely CO₂ and the 𝛿O₂/N₂ ratio (used to identify precessional cycles for dating ice cores), diffuse in Antarctic ice of 1 to 1.5 million years old. I focused on the COLDEX survey region between the South Pole and Dome A. I employed two models: 1) a one-dimensional steady state model which calculates the temperature and age of the ice with respect to depth, and 2) a gas-diffusion model which uses the temperature- and age-depth relations to calculate the amplitude of the gas signals in the ice through time. The input parameters for these models are measured using aerial radar, provided by COLDEX, or interpolated accordingly. So far, I have found that CO₂ is relatively well preserved in the region, while the 𝛿O₂/N₂ ratio is much less well preserved. This suggests that finding an ideal region for a deep ice core drill site, on the basis of gas diffusion, may be difficult.

Viscous Thread Printing (VTP) is a novel manufacturing technique that allows foam production using traditional Fused Deposition Modeling (FDM) printers. This printing technique takes advantage of an everyday phenomenon called Viscous Thread Instability (VTI), which can be observed when honey is drizzled onto pancakes. Similarly, molten filament buckles onto itself and creates a coiling pattern when extruded from enough height. These coils create cellular structures that have shown potential improved durability and expanded applications such as in medical scaffoldings. However, as a relatively new technique, VTP has been limited to producing single-stiffness (uniform density) foams in previous works, and it remained unproven whether we can produce VTP foams containing multiple densities. Drawing inspiration from biological structures with variable porosity such as bones and balsa woods, we hypothesized that we could create multi-density VTP foams by manipulating predominant VTP parameters that affected the size of the coils. This way, we can vary the pore sizes, and thus the density and stiffness, of a single cellular structure while preserving high structural integrity. Such structures would have many applications such as in robotics and prosthetics, such as customizable orthotics and limbs for soft robots. Beyond enabling this technique, we further investigate a novel methodology to simulate the printing process of variable density VTP foams and measure the foam's material properties. This allows for an easier and more sustainable exploration of the design of VTP foams without wasting any filament, which would make VTP foams more accessible in industry and research settings.

The backsplash galaxies of the Milky Way are galaxies that have once entered the virial radius of the Milky Way but reside outside of which today. As a backsplash galaxy enters the Milky Way, its gravitational interaction with the Milky Way causes its star forming material to be stripped away and causes it to appear to be more diffused and older. The evolution and properties of a backsplash galaxy depend significantly on the properties of its dark matter halo as it makes up the majority of its mass. In my research, I use cosmological simulations of Cold Dark Matter (CDM) and Self-Interacting Dark Matter (SIDM) of Near-Milky Way halos done by my mentors and their colleagues to identify and analyze the properties of backsplash halos during their evolution and compare the results across the two dark matter models. Significant differences between the results from the CDM and the SIDM models are anticipated, with the major difference caused by the interactions between the SIDM particles allowing the exchange of energy and momentum between particles, causing the energy to transfer between regions of the halo, resulting in altered density profiles which influences the tidal evolution history. After the analysis of both models are completed, the results can be compared and matched to observational data of the candidates of backsplash galaxies of the Milky Way, and conclude in each model’s ability to make accurate predictions. This research contributes to the ongoing investigation of the properties of dark matter particles and the analysis of the evolution of backsplash galaxies.

Adipose tissue, also known as body fat, is vital in storing energy. It is composed of adipocytes and present in different depots. In this study we focused on omental (OM) adipose tissue, which is found between organs near the stomach, and subcutaneous (SC) adipose tissue, which is found under the skin. Functional differences have been observed among adipose depots. SC adipose tissue is responsible for insulation while OM adipose tissue has endocrine functions. OM adipocytes have been observed to be smaller and more variable in size compared to SC adipocytes in individuals with obesity. I hypothesized that the size of both OM and SC adipocytes is associated with increasing BMI. I tested this hypothesis using SC and OM adipose tissue biopsies collected during elective surgeries from metabolically healthy participants (20 females, 11 males) with a range of ages (25-65 years) and BMIs (21-56). Afterwards, the tissues were fixed and stained with H&E. I drew 2-5 squares per slide and counted the number of adipocytes within each square. The size difference between OM and SC adipocytes was tested using a Wilcoxon signed-rank test and a significant difference (p=0.004) was observed. A correlation between BMI and the size of OM (p = 0.008) or SC (p = 0.009) adipocytes was detected with weighted linear regressions. Sex was not observed to be a significant covariate. These findings expand on prior data by including lean individuals, and patients with obesity who are otherwise metabolically healthy. The results show there is a clear difference in the size of adipocytes. The adipocyte size in both depots correlated with BMI. This data shows that progressive obesity and adipose tissue enlargement is due to the enlargement of the adipocytes rather than an increase in the number of adipocytes in both OM and SC depots.

As deep learning vision models become more prevalent, understanding the adversarial risk associated with them is important for maintaining safety and security. A common adversarial approach, evasion attacks, involve adding perturbations to the input data until it is correctly classified by humans but misclassified by machine learning models. Previous methods for physical-world evasion attacks include placing stickers, projecting artificial light sources, and casting shadows to mask the target object. The use of shadows, a naturally occurring phenomenon, is likely to remain undetected by people, and is therefore the focus of this project. Past shadow-based evasion attacks restrict the shadow design to more inconspicuous shapes, like triangles and other simple polygons. By designing a sculpture that can detract attention from the shadows it casts, this project aims to determine whether more complex shapes can be more successful at masking the target object. To compare the effectiveness of the shapes under the black-box setting, we use the same task as previous shadow-based evasion attacks, traffic sign classification, with the LISA and GTSRB datasets. To test the attack method in a simulated environment, I use SketchUp to create various sculpture designs that cast the selected 2D shapes. A model of the sculpture is then tested in a real-world setting, evaluating both general and scheduled attacks in indoor and outdoor environments. Because previous shadow-based evasion attacks are more effective when using polygons with more sides, we expect that complex shapes will result in a higher attack success rate.

Trapped ion quantum computing (TIQC), with its large decoherence times and small operation times relative to other physical quantum computing architectures, has garnered significant attention in the public and private sectors. Planar Paul traps, which simultaneously utilize radio frequency and static voltages in a two-dimensional electrode array to spatially confine ions, are the primary candidates for trapping ions for TIQC due to their manufacturability and ability to shuttle ions between multiple trapping zones for quantum logic gates and memory storage. The growing relevance of this technology necessitates educating students about the advanced electrodynamics of ion trapping and ion shuttling. Therefore, I developed a macroscopic planar Paul trap which utilizes 50µm diameter proxy-ions along with high voltage (HV) alternating currents (AC) at 60 Hz and HV direct currents (DC). These are applied to a 5-rail electrode geometry to demonstrate ion shuttling and ion-group splitting along a linear trapping axis. The goal is to educate students on the electrodynamics of ion traps by allowing them to experiment with the tunable trapping parameters, such as AC voltage amplitudes, DC voltage magnitudes, and applied shuttling waveforms and observe the changes in the dynamics of the proxy-ions relative to theoretical predictions. I designed the trap by implementing the recommended relative electrode dimensions into COMSOL Multiphysics and optimizing the geometry by maximizing the pseudopotential confinement while simultaneously minimizing electrode surface area. Afterwards, I utilized an analytic model of a 5-rail planar Paul trap, along with the method of Lagrange multipliers, to optimize the voltage magnitude and waveform of the segmented electrodes for smooth, effective shuttling and ion-group splitting. I then integrated an HV relay circuit and the 5-rail electrode geometry onto printed circuit boards to allow for student-controlled ion shuttling via an Arduino microcontroller.