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Embodied Stories: Painting the Figure
Get PDFThis thesis explores the enduring significance of figure painting as a means to record both a personal and cultural narrative. Figure painting reflects not only artistic evolution but also shifting societal values, technological advancements, and emotional expression. Through historical analysis and my own artistic practice, this paper traces the development of figurative painting - from ancient cave art to contemporary work - and examines how artists have used the human body to convey themes of identity, memory and connection. With reference images from my personal photo archive, my work reimagines everyday moments among family and friends, transforming them into open-ended narratives through expressive brushwork, vibrant color, and abstraction of the figure. The gaze of the subjects, the interplay between photography and painting, and the genre scenes all play a vital role in contemporary figure painting. Ultimately, this thesis argues that figure painting remains an innate human practice that links the personal and the universal, documents the passing of time, and invites viewers to reflect on their own lives
Containers of Becoming, Embodied Thresholds: Wearable Art As Performance
Get PDFMy interdisciplinary art practice investigates wearable sculpture as a tool for emotional processing, identity negotiation, and embodied performance. Beginning with early works like a cape made from thrifted scarves—exploring how different wearers transform the same garment—I have expanded their role into vehicles for personal and collective meaning. Drawing from the work of artists including Nick Cave, Stan Brahkage, Lucy McRae, Lucy Orta, and Rebecca Horn, I examine how bodily concealment, distortion, and constraint can foster new modes of self-discovery.
Over time, my practice has evolved to incorporate performance, time-based media, and psychological frameworks. For example, my choreographed dance Enneagram: Beings of Light combined wearable sculptures, improvised choreography, and D.W. Winnicott’s theory of the intermediate space to explore fluid identity formation. My research also engages Jacob L. Moreno’s psychodrama and Victor Turner’s theory of liminality, positioning the act of wearing as both performative and therapeutic.
These inquiries culminate in my thesis project, Vessels of Protection, a multi-channel video installation that merges sculpture, movement, and immersive soundscapes. Tent-like garments act as sculptural sanctuaries, disrupting natural movement and creating liminal spaces where wearers can engage with memory, grief, and transformation. Through embodied performance and layered projections, the work challenges conventional notions of protection, proposing garments as containers for emotional exploration rather than mere physical shielding.
Across my practice, I position wearable art as a catalyst for catharsis, offering both wearers and viewers an opportunity to inhabit spaces of vulnerability and imaginative becoming
Illustrated Nonfiction in the Screen Age
No full textThe addictive qualities of our phones and incessant demands on our attention pose consequential challenges to individuals and the collective. In the age of screens, what are illustrated nonfiction books uniquely positioned to offer modern audiences? This essay seeks to understand how the genre can serve as one possible antidote to the infinite scroll. I examine the qualities of three illustrated nonfiction texts: Abbi Jacobson’s I Might Regret This, Shaina Feinberg and Julia Rothman’s How We Got By, and Nora Krug’s Diaries of War. The essay considers how the slower pace of the reading experience, the careful curation of image and text, and the factual nature of the stories may serve as a reparative force for our frayed nerves
Interpreting IMU Data: Applications in Baseball Injury Analysis and Prevention
Get PDFBiomechanical analysis of baseball pitching has traditionally relied on laboratory-based motion capture and high-speed video. However, wearable IMUs, containing accelerometers, gyroscopes, and sometimes magnetometers, have the potential to revolutionize on-field data collection, enabling real-time kinematic and kinetic assessments in game or practice scenarios. These technological advances are particularly crucial as they allow for continuous monitoring of the substantial mechanical stresses imposed on pitchers\u27 elbows and shoulders, helping identify potential injury risks such as UCL tears, rotator cuff strains, and tendon overuse before they become severe.
This paper provides a high-level framework for integrating IMU data with tendon-remodeling principles specific to baseball pitchers. It begins with a detailed overview of IMU sensor physics—covering sensor fusion algorithms (Kalman filters, gradient descent methods), orientation computation, and noise-filtering techniques vital for high-speed throwing motions. Next, an inverse dynamics approach is proposed to estimate joint reaction forces and soft-tissue stress, enhancing previous methods with additional equations and filtering implementations. Building on established research, the paper demonstrates how repeated loading data from IMUs can inform understanding of tendon adaptation and potential injury risk over time. Finally, a discussion of data acquisition protocols, error sources, and future directions for multi-sensor arrays and AI-based analytics, offering a roadmap for performance optimization and injury prevention. Through the integration of mathematical rigor with applied biomechanics, this paper aims to equip researchers, clinicians, and coaches with a powerful toolkit for deploying advanced wearable technology in baseball pitching
Solar Design Considerations
Get PDFThis paper will discuss the design considerations for solar panel systems and the solar power generation process that occurs within solar modules. It will go in order of how the energy is transferred from the Sun to the solar panels and then to a public interconnection circuit. It will begin with solar irradiation and describe the intricacies of light, then describe how solar panels are positioned to maximize the irradiation captured and describe irradiation design terms, then describe the processes involving material science and heat transfer once the irradiation contacts the solar panels. The last part will present my solar design and simulation I made in the software Helioscope
A History of Textuality: Foucault in the Margins of My Masculinity
Get PDFGraduate winner: 2nd place, 2025, 38th Annual Carl Neureuther Student Book Collection Competitio
Understanding the Relationship Between PM2.5 and Aerosol Optical Depth
No full textAmbient fine particulate matter (PM2.5) is the leading global environmental determinant of health, with millions of attributable deaths annually. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing offers information to fill these gaps worldwide when augmented with information from a chemical transport model. More specifically, this satellite-derived PM2.5 is obtained from satellite retrieved aerosol optical depth (AOD) by applying the modeled PM2.5 to AOD relationship (η). Uncertainties, however, exist in the estimation of PM2.5, particularly from the modeled η. This dissertation focuses on understanding the spatial distribution of η and model developments for improved simulation of it. This dissertation includes three studies. The first aim of this study is to interpret aircraft observation and an aerosol microphysics scheme for insight into aerosol size representation in a chemical transport model (CTM). Size representation strongly affects mass scattering efficiency and, therefore, the PM2.5 to AOD relationship. An improved representation of mass scattering efficiency is developed by combining aerosol measurements from aircraft campaigns over the U.S. and South Korea and a CTM with an aerosol microphysics scheme, GEOS-Chem-TOMAS. The simple aerosol size parameterization proposed here significantly improves the agreement between modeled AOD and ground-based measured AOD globally. The second study aims to understand the spatial pattern and driving factors of the relationship by examining η from both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6,870 ground-based PM2.5 measurement sites and satellite retrieved AOD. The GEOS-Chem global chemical transport model, in its high performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. The spatial correlation of observed η with the driving factors reveals that the spatial variation of η is strongly influenced by aerosol composition and aerosol vertical profile. Sensitivity tests were done to quantify their effects on η spatial variability. Building on the second study, the third study aims to understand the global spatial pattern of sulfate, a major PM2.5 component, and to examine emission uncertainties in emission inventories. This study leverages sulfate measurements from the Surface PARTiculate mAtter Network (SPARTAN) and the GEOS-Chem chemical transport model. Three major global emission inventories, the Community Emissions Data System (CEDS), the Emissions Database for Global Atmospheric Research (EDGAR), and the Hemispheric Transport of Air Pollution (HTAP) - are examined. Simulation with CEDS generally reproduced the global sulfate distribution measured by SPARTAN. HTAP and EDGAR emission inventories exhibit weaker performance due to regional biases. SPARTAN data supports recent developments in CEDS and reveals potential regional biases in all three emission inventories. The three studies fill gaps in understanding and modeling η spatial distribution and highlight directions for future model development efforts
Safe Robot Planning Through Reachability Analysis of Human Behaviors
Get PDFThis paper introduces a novel approach to human-robot interaction that enables robots to safely navigate shared spaces by actively gathering information about human internal states. We model human behavior as influenced by latent variables representing attentiveness and driving style, which cannot be directly observed by the robot. Our key contribution is a sampling-based reachability analysis method that integrates belief updates over human internal states with adaptive interval refinement, allowing robots to maintain probabilistic safety guarantees while efficiently planning their actions. Unlike passive observation approaches, our framework enables robots to purposefully execute probing actions that clarify human internal states, accelerating belief convergence. We implement our approach in an autonomous driving scenario at intersections, where the robot vehicle can actively test whether a human driver is attentive or distracted through subtle movements. Experimental results demonstrate that our method significantly outperforms passive estimation techniques in accuracy and convergence speed while maintaining safety constraints. The framework generalizes to various human-robot interaction domains where understanding human internal states is critical for effective collaboration and safety
Disentangling Safety and Accountability in Criminal Justice Policy
Get PDFIn this Article, I argue that the U.S. criminal system and debates about criminal justice reform reflect an elision of two largely distinct social functions: ensuring public safety and imposing accountability for harmful conduct. Despite deep disagreement about the specifics, most commentators seem to accept that these are both important social functions. Abolitionists claim that the criminal system doesn’t keep people safe and provide accountability. Instead, it harms—and perhaps is meant to harm—people from marginalized communities and protects the interest of socially dominant groups. Reformers contend that the criminal system can and should serve the safety and/or accountability interests, but it currently doesn’t. Meanwhile, defenders of the status quo claim that criminal legal institutions serve these two core state functions (and perhaps others).I argue that it’s a mistake to imagine that the same institutions could or should fulfill both of those functions. I contend that the contemporary U.S. criminal system often relies on a foundational problem: entangling the safety function and the accountability function. Imposing some degree of stigma might be a desirable feature of an accountability-based system, but is it actually necessary in a system focused on public safety? Similarly, forms of surveillance and social control might be defensible features of a system focused on public safety, but are they actually necessary to ensure accountability? The answer to both of these questions should be no. But with criminal legal institutions understood as advancing both functions, we are left with a troubling, incoherent, and often-counterproductive amalgam of the problematic features of both. Ultimately, we all won’t agree on the best way to ensure public safety or to hold people accountable. But, taking seriously the distinction between those ends might help set the stage for more fruitful debates about what features of contemporary penal administration should be preserved, reformed, or abolished
Adaptive Noise Estimation and Denoising with Deep Learning for NMR Spectroscopy
No full textNuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique widely used for molecular structure elucidation in chemistry, biology, and medicine. However, spectral accuracy is often degraded by noise—particularly in low acquisition time settings—resulting in reduced resolution and obscured chemical features. While traditional noise reduction techniques such as signal averaging can improve spectral quality, they require longer acquisition times, limiting their utility in real-time and high-throughput applications.
This thesis presents a deep learning-based denoising framework designed to enhance the quality of complex-valued NMR spectra. The proposed model, built upon a U-Net architecture, incorporates both real and imaginary components of the signal to preserve phase information and spectral fidelity. A core innovation of this work is the integration of a noise level estimator that predicts the noise intensity present in the input signal. This estimated noise level is then used to condition the denoiser, enabling the model to adapt dynamically to a wide range of noise scenarios.
The framework was evaluated across two primary settings: (1) synthetically generated noisy spectra created by adding machine-derived noise to clean Free Induction Decay (FID) signals, and (2) real experimental spectra obtained from Bruker spectrometers under uncontrolled acquisition conditions. Quantitative metrics such as Signal-to-Noise Ratio (SNR), Peak Height-to-Noise Ratio, and Normalized Mean Square Error (NMSE) were used to assess performance. Results demonstrate that the adaptive framework consistently improves spectral clarity, outperforming traditional denoising baselines and unconditioned deep learning models.
By combining noise-aware learning with complex-valued signal processing, this framework offers a robust and scalable solution for enhancing NMR spectra without increasing acquisition time. These findings contribute to the growing intersection of deep learning and spectroscopic analysis, advancing the potential for automated, high-throughput NMR applications in both research and industry