Recipients for Fall 2024 Mary Gates Award
Congrats to Jack, Liliana, and Sabrina for receiving the 2024 Mary Gates Endowment for Students in autumn quarter. This scholarship provides $5,000 disbursed over two quarters and is intended to enhance the educational experiences of undergraduates while they are engaged in research guided by faculty.
Liliana Elizabeth Flores
Award: Mary Gates Research Scholarship
Major: Physics (Bothell)
Mentor: Dr. Paola Rodriguez Hidalgo, Physical Sciences Division, STEM
Studying and Developing Programming Tools to measure Extremely High Velocity Outflows
Quasars are some of the most luminous objects in the universe. Through analysis of quasar spectra, outflows of gas and dust can be identified by absorption troughs. Outflows that travel at speeds greater than 10% of the speed of light are known as Extremely High Velocity Outflows (EHVOs), and while there have been fewer studies compared to those at lower speeds, they might carry out large amounts of energy due to their higher speeds. The amount of gas in these outflows can be measured and studied through their CIV absorption troughs. However, in some cases, this absorption is contaminated by absorption of other ions at lower speeds. I have developed programming tools to analyze some of these complex EHVO absorption features. I will present the results of applying these techniques to two interesting cases: (1) one of the most luminous quasars in the universe and (2) the fastest known EHVO to date. My work improves the quality of EHVO analysis, resulting in more accurate measurements of absorption of these extreme outflows. This is crucial to obtain better estimates of mass outflow rates and kinetic energies in quasars, of which EHVOs might be some of the largest contributors.
Jack McFarland
Award: Mary Gates Research Scholarship
Major: Computer Science & Software Engineering
Mentor: Dr. Afra Mashhadi, Computing Software and Systems
FairRL-FL: Reinforcement Learning for Fairer Federated Models
Bias in Machine Learning (ML) can lead to unfair treatment of certain groups, particularly in areas like healthcare and finance, where disparate outcomes can have life-altering consequences. New training techniques aim to improve fairness while preserving privacy. Federated Learning (FL) is one such approach, allowing models to be trained on data from many devices without centralizing it. Instead of sharing raw data, each device trains a local model and sends model updates (adjustments based on its local data)to a central server, which aggregates them into a global model. This protects privacy while enabling large-scale training, but differences in data quality, representation, or access across devices can reinforce bias, leading to models that work well for some groups but poorly for others. This project tests whether a debiasing system can effectively mitigate bias in FL without sacrificing model performance. To tackle this, I’m adapting a Reinforcement Learning (RL) system, where an agent learns by interacting with an environment and receiving rewards for beneficial actions. The agent evaluates fairness using feedback from client devices and adjusts the central model’s weights before redistributing it for further training. Using fairness metrics and accuracy as its reward signal, the agent continuously refines its strategy, learning how to mitigate bias while preserving performance. I’m solely responsible for designing, building, testing, and analyzing this system, though I’ve benefited greatly from the guidance of my mentor, Dr. Afra Mashhadi, insights from her graduate students, and tools developed in prior research. Results from prior work suggest this method can reduce bias while maintaining strong model accuracy, highlighting its potential for improving fairness in FL systems. If successful, this approach could be applied in areas like medical diagnostics, risk assessment in insurance, and hiring algorithms, where biased models can lead to significant real-world harm.
Sabrina Prestes Oliveira
LETI participants toured their new community/event center in Everett, where they plan on launching a variety of new initiatives and expanding their capacity to serve the Latino community.
Award: Mary Gates Leadership Scholarship
Major: Data Visualization
Mentor: Sarina Barrett
Visualizing Latino Demographics, Entrepreneurship, and Developing Outreach Strategies for Community Empowerment in WA
Latinos in the United States, while a fundamental part of its history, often face barriers in education, employment, and healthcare. Nonetheless, research has shown that despite limited access to financing, Latino immigrants over-index among entrepreneurs and business owners nationwide, playing a key role in driving economic growth. Latino Educational Training Institute (LETI), founded in Lynnwood, Washington, in 1998 to provide language classes, career training, and cultural events for Latino residents, is a key organization in the region. As LETI expands its reach with a new location in Everett, it seeks to expand avenues for prospective small business owners and vendors, especially women, to develop as leaders and entrepreneurs. Building on the data analysis started during UW-Bothell’s Data for Public Good program in Summer 2024, this project will further assess and visualize the needs of the Everett Latino community, working with local stakeholders to access diverse data sets and evaluate the state of Latino entrepreneurship in Washington and the Puget Sound region. This will provide LETI with not only insights to guide future outreach and program development initiatives but motivational data stories for the community at large. We face a climate of unprecedented fear, misinformation, and discrimination against Latino immigrants. Through community-based geography and strengths-focused demographic analysis, this project will serve as a counterpoint to popular narratives of marginalization and validate LETI’s mission for collective, local empowerment.