Radicalization and Exodus: Analyzing the Impact of Governmental Extremism on Migration Patterns in Venezuela

Venezuela’s political and economic crisis has led to one of the largest migratory movements in Latin American history. Over the past two decades, millions of Venezuelans have fled due to political repression, economic collapse, and humanitarian emergencies. This project examines general migration patterns from Venezuela between 1999 and the present. Using data from the United Nations Department of Economic and Social Affairs (UNDESA) and World Population Prospects, the study analyzes how political radicalization and economic instability have influenced migration trends over time. The findings provide crucial insights into the scale and nature of this mass displacement and its implications for both migrants and host nations. The findings indicate a significant correlation between governmental radicalization, economic collapse, and mass migration. The research categorizes migration trends into three political phases: the Chávez era (1999-2013), Maduro’s early years (2013-2018), and the ongoing humanitarian crisis (2018-present). Data analysis reveals that migration patterns directly correlate with major political events, including oil price fluctuations, hyperinflation, and governmental crackdowns on opposition. Furthermore, the analysis reveals that migration has transformed from a gradual economic outflow to an urgent survival mechanism, with entire families fleeing due to food insecurity, hyperinflation, and violence. Additionally, the project highlights gendered migration trends, with a consistently higher proportion of female migrants, emphasizing the importance of gender-sensitive migration policies. It also suggests that women face unique socio-economic vulnerabilities and pressures in Venezuela that drive their displacement

Dynamic Workload Management for Highly Concurrent Database Management System

Modern database management systems must balance high throughput with low-latency query execution across diverse and dynamic workloads, where queries vary significantly in complexity, resource demands, and latency sensitivity. This challenge is further compounded in multi-tenant environments, where the system must concurrently serve a wide range of query types while adapting to workload fluctuations and ensuring stable performance. Achieving this balance requires intelligent scheduling mechanisms that can effectively classify, prioritize, and allocate resources to queries in a manner that aligns with both user expectations and system-level performance goals. This thesis surveys both academic and industrial advancements in workload and resource-aware query scheduling techniques designed to meet varying performance requirements. Drawing inspiration from heuristic scheduling strategies and recent developments in industrial systems, the thesis presents the design and implementation of a dynamic workload management module for AsterixDB. The proposed scheduler accommodates both user- and system-level requirements by enabling semantic query classification, user-defined prioritization, and fairness-aware scheduling policies tailored to different workload scenarios. To evaluate its effectiveness, we simulate three representative workload patterns under high query concurrency and conduct a comprehensive experimental analysis. The results demonstrate the importance of fine-grained query characterization, memory-aware scheduling, and feedback-driven control in supporting responsive and multi-tenant workload management in highly concurrent database systems

High-Temperature Inert-Environment Thermomechanical Testing Furnace

High-temperature materials testing is critical for frontier technologies, especially in the aerospace and power generation sectors. The objective of this senior design project is to research and develop an inert-environment sealed furnace for a lab mechanical tester to reach 2000°C for the Santa Clara University Materials Science Lab. Two groups of project members were created to address engineering design and manufacturing issues in various subsystems of the final vision: Team A focuses on structural analysis and fabrication, instrumentation, and electrical design, while Team B focuses on thermal, busbar, and fluid cooling design. Financial and logistical aspects of the project have been taken into consideration, and the design, analysis and verification of various components of the system without direct testing of a final product will inform future teams on the next steps

Empowering Tanzanian Education: Personalized and Accessible Test Preparation

Only 20% of students performed well enough on their secondary exams to continue their A-level studies. This challenge exists due to a lack of resources, classroom overcrowding, and absenteeism of the instructors. The project focuses on improving the pass rate for these national exams; we have built an intelligent quiz generating platform that helps Tanzanian students prepare more effectively for exams by meeting user needs including active recall, answer explanations, increasing difficulty, and filtered studying. Students can select the subject, form level, topic, and the difficulty level, and the platform provides different types of questions, including true/false, multiple choice, and free response, accordingly, along with more difficult incorrect answers. Furthermore, our platforms leverages AI to provide answer explanations to students, ensuring that students are not confused on why a specific answer is correct. Over the first 30 days after deploying, we have had 64 active users, and 1,600 page accesses

Dr. Cynthia Mertens, Law, interviewed by

In this powerful and moving interview, Dr. Cynthia Mertens reflects on her journey from growing up in the Bay Area to becoming a trailblazing lawyer, professor, and fierce advocate for justice. A graduate of Stanford and UC Law San Francisco, she candidly recounts the discrimination she faced as a woman in law—stories of “ladies day” in law school, firm meetings held in men-only clubs, and disrespectful male students as a female professor—all of which only deepened her commitment to uplifting the marginalized. Her leadership was instrumental in founding the Katharine and George Alexander Law Center at Santa Clara University, a lasting legacy of her devotion to community-centered advocacy. Mertens’ professional accolades include numerous teaching and service awards from Santa Clara, as well as regional recognition as a “Silicon Valley Woman of Influence” and a “Bay Area Woman of Honor.” Equally impactful is her profound care for students: she led immersion trips around the world to examine human rights and personally housed and mentored international students who couldn’t afford law school, several of whom she now considers family. Even in retirement, she continues this work, currently housing a student who just completed his final exams. Her compassion, strength, and lifelong dedication have left an indelible mark on the Santa Clara community. Dr Mertens is a phenomenal speaker, teacher, lawyer, and human– and this is an interview you do not want to miss

EEG-based Machine Learning Framework For Schizophrenia Diagnosis And Predictive Modeling

Schizophrenia is a chronic psychiatric disorder characterized by hallucinations, delusions, cognitive deficits, and disorganized behavior. Despite its prevalence, diagnosis remains subjective and treatment response is highly individualized. This thesis presents a machine learning (ML) framework leveraging electroencephalography (EEG) data to develop an objective, scalable, and non-invasive diagnostic and predictive tool for schizophrenia. The model utilizes clinical EEG recordings from patients and healthy controls during a social decisionmaking task. A multi-stage preprocessing pipeline—including temporal data augmentation, wavelet-based denoising, and independent component analysis—was applied to enhance signal clarity and structure the data for deep learning. EEG segments were then tokenized and input into a Transformer-based neural network capable of both binary classification (schizophrenia vs. control) and pseudo-severity estimation. Trained using stratified group 5-fold cross-validation and optimized with early stopping, the model achieved an average accuracy of 90%, an AUC of 0.93, and an F1 score of 0.90. Continuous output scores from the model provide a gradient of symptom severity, offering insights into subclinical neural patterns. This dual-output approach enables both diagnostic support and real-time symptom tracking, representing a novel contribution to AI-assisted precision psychiatry. The results demonstrate that EEG-derived neural signatures, like altered gamma oscillations, impaired phase synchrony, and reduced eventrelated potentials, can be effectively leveraged by deep learning models to enhance clinical decision-making. This work establishes a foundation for scalable EEG-based diagnostics and creates the framework for further development of integrated neuroinformatics tools for psychiatric care

Nature\u27s Microplastic Solution: Engineering Marine Bacteria for Sustainable Plastic Decomposition

This research looked at the metabolic response of Alcanivorax borkumensis in pyruvate-supplemented medium as well as its polystyrene microplastic degradation potential. The study used CO₂ analysis to confirm previous results on plastic digestion and discover new understanding into degradation kinetics and inhibitory pathways. A. borkumensis was cultivated in ONR7a medium with varied pyruvate concentrations (0.5%, 1%, and 2%) at 32 degrees C and 220 RPM. Polystyrene microplastics (≤5 mm) were introduced over five 24-hour experiments. Every four hours, the OD600 was used to track development, and the Zobell Marine agar plates streaked with bacteria at assigned time points verified viability. The controls were pyruvate only (positive), no carbon source (negative), and pyruvate with polystyrene (test). Goal one of confirming previous scientific results on plastic digestion was partially achieved. Although polystyrene-containing media saw development (especially in experiments 2, 4, and 5), the erratic OD600 and plating results made interpretation difficult, probably due to overlapping pyruvate metabolism. Goal 2 to identify new scientific insights on plastic digestion kinetics, inhibitory or competitive biopathways for synthetic polymer bond cleavage based on the analysis of CO2 was partially achieved. The optimal degradation kinetics developed between 8 and 12 hours of incubation with 1% pyruvate. The results suggest a hierarchical substrate use in which pyruvate enhances metabolism while suppressing the energy-intensive enzymatic pathways required for polymer bond cleavage. These findings highlight the need of pyruvate-free setups to definitively determine plastic mineralization and CO2 pathways. The most significant biomolecular new discovery in current experiment where unexpected bacterial growth in negative control (no pyruvate, no polystyrene), suggests A. borkumensis may possess an active CO₂ fixation pathway, which was also confirmed by the photo of the agar plates streaked with bacteria at assigned time points

‘Immigrant Invaders,’ ‘Arab Terrorists,’ and ‘Leftist Subversives’:Surveillance State from Palestine to Mexico

Immigrants in the US exist under a very strict regime of surveillance. This regime is characterized by authentication systems, check-in points, registration, forms of mobility tracking, interoperable databases, and, at the militarized US-Mexico border, the scrutiny of facial-recognition technology, automatic watchtowers, and drones. However, this didn’t come about spontaneously. This paper attempts to outline the development of the immigration surveillance state as a generations-long bipartisan project. From the evolution of the military-industrial complex and technologies like the internet innovated to spy on national independence movements, to the proliferation of data collection made for global finance, the immigration surveillance state reveals a dense web of private beneficiaries who dictate its operation and the narrative surrounding it. Its long-term trajectory also illustrates another pattern, wherein the practices, technologies, and economic policies shaping surveillance at home are overwhelmingly developed in the context of imperialism and colonial domination. Few struggles illuminate this as transparently as that of the Palestinians, who are subjected to technologies and policies of colonization that are imported by American police and ICE. Indeed, there is a line that travels from repression and apartheid abroad, to the surveillance of immigrants, and eventually the surveillance and repression of political dissidents at home, as is now being witnessed with the case of Palestinian organizer Mahmoud Khalil. The incoming Trump Administration, and its connections to Elon Musk and the private tech sector, project vicious escalations not only to the surveillance of immigrants, but also to the repression of mass movements threatening the capitalist hegemony. However, this paper urges readers to look beyond individuals, and put on trial the entire system which brought these actors to power, which tends toward surveillance to preserve control over its subjects, and which time and again prioritizes profit over people’s needs

LEETQUEST

The technical interview process for software engineering position has drastically changed in recent times to focus on Data Structures and Algorithms (DSA). Although, data structures and algorithms are a foundational aspect of several programming languages, the questions asked in the technical interview process do not accurately reflect, in terms of style and difficulty, the experience students and professionals have with DSA from school and work respectively. As a result, many have looked to online resources in order sharpen their DSA problem solving skills. However, the existing solutions are riddled with flaws. In particular, the learning resources they provide are locked behind paywalls and are not integrated into the problems themselves. The issue with the problems themselves is that they lack any direction, leaving users lost as to where to even begin. This thesis centers around the development of LeetQuest: an online web application that provides a user friendly way to learn and practice DSA problem solving skills. LeetQuest is designed specifically to address the problems with the existing solutions. It divides the problems by topic, and within those topics plots the associated problems on a directed graph which gives users multiple clear paths to follow providing a balance of freedom and direction. Additionally, along these directed graphs users will encounter nodes focused around learning; by integrating learning into the process it divides the learning into more manageable segments. The learning resources it provides are entirely free and come with several features designed for di↵erent styles of learners such as colorized descriptions and visualizations of data structures and algorithms to accommodate visual learners. Users can take notes and easily reference any previous learning or problem node they encountered. Moreover, users have access to a dashboard which visualizes their progress in numerous ways to encourage them continue learning. This project attempts to create a more user friendly and accessible means of preparing for technical interviews