As the first AI Librarian at the University of Chicago, I help shape the university's approach to AI literacy and adoption, teach AI at an introductory level, and support faculty and students in critically and ethically integrating AI into research and learning.
In my work, I aim to make AI accessible to people from all backgrounds. My own interdisciplinary background, including a BA in Linguistics from UC Berkeley and an MSE in Computer Science from Johns Hopkins University, along with professional experiences in big tech, startups, nonprofit organizations, and academia, has given me a strong understanding of how novel technologies like AI are built, integrated, and received in a variety of contexts and priorities. I use these insights to create educational resources and opportunities that help people from both technical and non-technical fields apply their perspectives in critiquing and building the future of AI ethics, policy, and practice.
I also built Gaita, an AI education platform that generates personalized Computer Science learning pathways from open-access courseware. Inspired by my own transition into tech, I created this project to make CS education accessible to learners from nontraditional and underserved backgrounds.
My Work
For a complete list of my research work, please visit my ResearchGate and GitHub profiles.
GAITA
GAITA (Generative AI Teaching Assistant) is a RAG system that generates personalized Computer Science learning pathways from open courseware, reducing choice overload and making AI education accessible to learners with nontraditional/nontechnical backgrounds.
Detecting Suicide Ideation with Text Classification
My first machine learning project that evaluates and compares different models for detecting suicide ideation in Reddit posts. The project aims to identify at-risk individuals through text analysis and provide insights for mental health intervention.
On the Use of Metaphor Translation in Psychiatry
A literature review exploring the importance of metaphors in psychiatric discourse, the shortcomings of mental healthcare for people with Limited English proficiency (LEP), and emerging solutions in Machine Translation.
Essays
I enjoy writing about theoretical Linguistics, Literary Theory, Music Theory, and NLP. If you look closely, you'll see that my biggest literary influences are Longinus, Burke, Kant, and the Russian Formalists. Visit my Medium page for the complete collection of my work.
Optimality Theory, A Room of One's Own, and Education as a Panacea for Pain
An exploration of how Optimality Theory in linguistics can be applied to understand Virginia Woolf's feminist classic, and how education serves as a transformative force for personal growth and societal change.
My Statement of Purpose
A personal reflection on my nontraditional career journey from Linguistics to Computer Science.
Translator's Notes, The Dream of a Ridiculous Man, and What it Means to Love Someone
An analysis of Dostoevsky's short story through the lens of translation studies (if that exists).
What it Means to be an Artist — and Why I Study Computer Science
Rumination on the relationship of art and technology and how Computer Science can be approached with an artistic mindset.
AI & ML Tutorials
What are Embeddings?
A comprehensive explanation of embeddings in machine learning, how they work, and their applications in NLP.
A Comprehensive Guide to AI Agents
An in-depth exploration of AI agents, their architecture, capabilities, and how they're transforming various industries through autonomous decision-making.
What is a Vector Database and How is it Used for RAG?
A detailed explanation of vector databases, their underlying technology, and their role in Retrieval Augmented Generation (RAG) systems.
What is Dimensionality Reduction?
An accessible guide to dimensionality reduction techniques in machine learning, and how they improve model performance and data visualization.
What is Knowledge Distillation?
An examination of knowledge distillation in deep learning, showing how complex models can transfer their knowledge to smaller, more efficient models.
Different Design Methods in A/B Testing
A guide to various design methodologies used in A/B testing.
Pros and Cons of K-Means Clustering
A balanced analysis of K-means clustering algorithm and appropriate use cases in unsupervised learning.
Pros and Cons of Gaussian Mixture Models (GMMs)
An evaluation of Gaussian Mixture Models and applications compared to other clustering techniques.