Implementation of an Automated PID Control for Detection of Liquid-Liquid Phase Separation in Droplets

Liquid-liquid phase separation in aerosol particles can influence cloud droplet formation and therefore affect climate predictions. In this work, a relative humidity (RH) control system was optimized to reproduce the conditions of Freedman et al. (2015) and monitor phase changes in organic-inorganic droplets in real time using optical microscopy. A PID-controlled valve regulated the balance of wet and dry air in a sealed chamber. To improve performance, standard bubblers were replaced with fine-pore bubblers for more efficient humidification, and a mobile app interface was developed for easier system tuning and operation. Step-test data were used to model system response and guide PID tuning. The optimized system now provides the stability and reproducibility needed to determine the RH values at which liquid-liquid phase separation occurs.

Performance-Based Design of Shallow Foundations Considering Soil Variability and Climate Change Effects
Traditional geotechnical design relies on deterministic methods and prescriptive safety factors that often fail to account for inherent soil heterogeneity and dynamic environmental changes. This research proposes a shift towards a Performance-Based Design (PBD) framework for strip footings, by integrating stochastic modeling with climate-driven hydro-mechanical coupling. The methodology utilizes Smoothed Particle Hydrodynamics (SPH), an advanced mesh-free numerical approach capable of modeling highly nonlinear behavior and large-deformation processes. The framework is developed in three phases: (1) establishing a deterministic baseline validated against classical analytical benchmarks; (2) incorporating Random Field Theory to quantify spatial soil variability and include it on settlement analysis; and (3), including hydro-mechanical coupling to simulate soil behavior under transient rainfall infiltration. The hydro-mechanical coupling is necessary to evaluate wetting-induced collapse and the degradation of shear strength and stiffness caused by extreme rainfall events. The primary contribution of the work is a comprehensive set of reliability-based design abaci that will enable practicing engineers to directly correlate foundation geometry with specified probabilities of exceeding serviceability limit state thresholds related to settlement, based on stochastic distributions of soil properties and evolving rainfall pattern likelihoods. By explicitly integrating geotechnical uncertainty and climate resilience, this work provides a quantitative tool for risk-informed decision-making, ensuring that shallow foundation systems remain functional and safe under increasingly variable environmental and subsurface conditions.

Definitions of Death and Medical Marginalization

In this project, I examine how the medical establishment developed its definition of death in order to argue for a more pluralistic approach to end-of-life decision making. I analyze competing definitions of death–including higher brain death, whole-brain death, and cardiopulmonary death–and explore the considerations that led the medical community to adopt whole-brain death as the accepted definition. I focus on the controversy surrounding Jahi McMath, an African-American girl, and the difficulty her family faced when they rejected the whole-brain definition of death to show that, in the medical field, brain death is not currently understood through a pluralistic approach, and it is a view that reflects white secular values of the medical establishment. I provide a brief history on the racism, mistreatment, and marginalization of African-Americans in the medical field that has limited their participation in defining core concepts within the medical field. I discuss strategies for creating a more inclusive approach to clinical decision making. Brain death is one aspect of the medical field, but it provides evidence of a much larger, systemic issue that must continue to be addressed. In order for our society to become more inclusive and progressive, we should allow people to choose between whole brain death and cardiopulmonary definitions of death, in order to accommodate the values and concerns of a broad range of patients.

Building and Documenting a Reproducible Low-Cost Open-Source Optical See-Through AR Headset for Computing Education (Work in Progress)

Students in introductory computer science courses often struggle to understand how algorithms and data structures evolve over time, as traditional flat-screen visualizations require learners to mentally track abstract state changes, limiting spatial and embodied engagement. Augmented reality (AR) offers a promising way to make these processes visible and interactive, yet existing optical see-through head-mounted displays are often too costly for widespread classroom adoption.

To address this barrier, this project focuses on building and systematically documenting a low-cost, open-source optical see-through AR headset for computing education. Inspired by Project North Star, we adapt and refine an existing open-source design with an emphasis on accessibility, reproducibility, and classroom deployment. The system incorporates modular 3D-printed structural components, commodity micro-displays and driver boards, and a repeatable optics calibration workflow based on checkerboard alignment patterns to correct distortion and misalignment.

A working hardware prototype has been constructed, and we will share detailed documentation including a reproducible bill of materials, wiring diagrams, 3D-printable models, and step-by-step assembly and calibration procedures. The calibration workflow includes representative results and troubleshooting guidance to support replication. While interactive AR software is still under development, this work establishes a transparent and replicable foundation that lowers financial and technical barriers for educators seeking to adopt optical see-through AR systems in computing classrooms.

Plexa: Empowered AI Education

AI in education is a hot topic, and for good reason. Professors and Universities are searching for the answers of how they can prepare their students for the future, but the tools available do not provide for their needs. Existing tools fall short in at least one of a few key ways: They are not pedagogically forward, they do not respect data provenance, they are expensive and opaque. Plexa provides educators with the ability to create highly customizable lesson plans for their students to use in a controlled environment, so that they may promote AI literacy and prepare their students for careers in specific disciplines and lives in the age of AI. Chats are restricted to lesson instances; no free form chats. This structure encourages accountable usage, and keeps the focus of the tool on pedagogy. The tool is local first: all data is stored on premise, with the ability to run models within the institution’s network, or outsource compute to any provider of their choice. Plexa will always be fully free, open-source, and permission-less-ly licensed so institutions can examine/modify the code and deploy their own versions without any license related headaches or paying a cent. With this framework, I present a new way forward for what AI education can look like.

Talking AI and Talking Money
From your homework to stock picks, AI search engines can do it all. In Q3 of 2025 alone, the proportion of S&P 500 firms discussing AI during earnings calls was 62%! Regardless of industry, AI will be affecting how a business operates and functions. At the same time, managers are grappling with different views on AI, ranging from optimism to concern. In this project, we analyze corporate disclosures by reading extractions of AI discussions and classifying these extractions into costs and benefits of AI. In our findings, positive AI-related keywords tend to emphasize growth and potential—such as opportunities, expansion, and innovation—whereas negative sentiment toward AI is more often associated with words like bias, concerns, and infringement. Next, we generate textual rules that extract costs and benefits of AI from 10-Ks, 10-Qs and earnings calls and calculate AI sentiment scores for each firm. Finally, we examine the trends of the AI sentiment across time and different sectors.

Establishing the Effectiveness of the OpenBCI EEG System in Identifying Physiological Markers of Healthy Brains

Parkinson disease (PD) is the second most common neurodegenerative disorder in the United States, affecting 1.1 million people. However, ~20% of PD patients are misdiagnosed because diagnosis often relies on subjective motor assessments by doctors. Electroencephalography (EEG) is a non-invasive, accessible tool that records neural activity using electrodes placed on the scalp, and can be used to improve diagnostics with objective neural biomarkers of PD. The cognitive neuromodulation lab bought the OpenBCI EEG system last spring, and this project sought to identify the reliability of the system. Three experiments were conducted with healthy volunteers. The data for each experiment was preprocessed using EEGLAB to isolate neural data from electrical noise. Then, MATLAB’s signal processing toolbox was used to extract the neural features. The first two experiments looked for robust neurological biomarkers of health data identified with more advanced EEG systems. With subjects alternating between eyes open and eyes closed states in the first experiment and performing thirty trials of finger tapping in the second experiment, two nonmotor biomarkers and one motor biomarker were successfully identified. The third experiment had patients perform finger tapping and spiral drawing, bilaterally, replicating motor tasks in PD assessments. With this data, three biomarkers known to differ between PD and healthy subjects were identified in this healthy cohort, consistent with the data in the literature. Having identified these biomarkers, the reliability of the OpenBCI system is verified and a comparative study between healthy subjects and PD patients will be conducted to identify novel PD biomarkers.

Historical Gradient of Host-Pathogen Dual Transcriptomics in White Nose Syndrome
White Nose Syndrome (WNS) is a cutaneous fungal disease caused by the fungus Pseudogymnoascus destructans (Pd) . Since its arrival in 2006, WNS has spread rapidly across North America (NA), resulting in mass mortality in hibernating bats and threatening extinction in some species. These findings aim to provide insight into how Pd pathogenesis changes over time to identify potential targets for the prevention and treatment of WNS.

Unlocking Potential: The Case for Crypto Assets in the U.S. College Endowment Funds

Since 2017, crypto assets have emerged as a prominent and transformative financial
instrument globally. While major financial institutions such as BlackRock, Fidelity, and Franklin
Templeton have widely adopted these digital assets and legislation surrounding crypto holdings
has become increasingly clear, college endowment funds have remained hesitant to embrace
them. Known for their traditionally conservative investment strategies, most endowment funds
have yet to integrate crypto assets into their portfolios. To date, only a few prominent
institutions, such as Ivy League schools and Emory University, have included crypto assets in
their treasuries or invested in crypto projects as private equity.
However, this cautious approach began to shift in 2025, as endowment funds started to
seriously explore the potential of crypto assets. With their capacity to deliver high returns,
mitigate risks inherent in traditional markets, and diversify investment portfolios, crypto assets
align closely with the strategic goals of endowment funds. These goals include fostering
intergenerational equity and engaging with younger generations.
This paper will analyze the current state of college endowment fund assets, including key
investment considerations such as Environmental, Social, and Governance (ESG) factors,
propose diverse strategies for integrating crypto assets, and highlight the potential benefits of
incorporating digital assets into college endowment fund portfolios.

Comparing Functional and Anatomical Division of the Subthalamic Nucleus as a Predictor of Clinical Outcomes in Deep Brain Stimulation

The subthalamic nucleus (STN) is one of the most common targets for deep brain stimulation (DBS), a treatment for alleviating the motor symptoms of Parkinson disease (PD). DBS involves implanting electrodes into the STN to deliver electrical stimulation, with the goal of reducing motor symptoms and improving quality of life for patients with PD. However, its success strongly depends on where within the STN the stimulation occurs. This study aims to determine which segmentation method more closely correlates with motor symptom improvement in DBS.

40 PD patients who previously underwent bilateral STN DBS were included for analysis. Outcomes were measured as percentage improvement across rigidity, tremor, bradykinesia, and overall motor symptom. Anatomical segmentation was performed by dividing each STN into six regions using its center of mass as a reference point. Functional segmentation was derived using the Accolla atlas, which labels the STN into motor, associative, and limbic zones. The atlas was registered to each STN and then the volume of tissue activation relative to the total volume of the STN was calculated to quantify stimulation.

Results showed that functional motor zone activation weakly correlated with rigidity improvement, while other functional zones showed no significant associations. In contrast, anatomical dorsal STN stimulation significantly correlated with rigidity and distinguished responder groups. The dorsal anatomical region demonstrated stronger clinical relevance.