I build personalized learning systems that make AI more accessible through education and education more effective through AI. I have a master's degree in Computer Science from Johns Hopkins University, where my research focused on combining Information Retrieval and Generative AI to develop adaptive, learner-centered learning pathways. Prior to this, I studied Linguistics and English at UC Berkeley.
My experience includes supporting AI Education programs for both technical and non-technical audiences. While at Johns Hopkins, I spent a year on Apple's internal AI Education team, where I collaborated with educators and domain experts to create NLP and ML learning materials for engineering, research, and annotation teams.
My goal is to empower people using AI and Education. AI has the power to enhance human capabilities across every field and personalize learning to fit individual goals and backgrounds. Meanwhile, education is a longstanding tool for empowerment, leading to knowledge and opportunity. I envision a future where AI and Education equip individuals with the critical thinking and technical skills to bring their visions to life and make a meaningful impact.
Reach out if you’re interested in collaborating on AI or education-related work :)
My Work
I design and build AI-powered personalized education systems. My passion project GAITA is a conversational RAG system that generates personalized CS learning pathways from open-access courseware. Inspired by my own transition into CS from a nontechnical background, I started GAITA to make open-access education more effective, relevant, and applicable by tailoring it to individuals' diverse backgrounds and goals. I also served as the Technical Lead for a RAG chatbot for NIH’s AIM-AHEAD Data Science Training Core (in collaboration with Johns Hopkins' Center for Digital Health and AI) that generates personalized course recommendations for biomedical researchers/students seeking to learn AI/Data Science.
Looking ahead, I plan to expand into English and AI/Digital Literacy and reshape education into something that meets people where they are in their learning journeys and personal circumstances. My goal is to democratize quality education, especially for nontraditional and underserved communities, and empower everyone to do meaningful 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.