I am a PhD Student in Computer Science at the University of Vermont, currently serving as a MassMutual Research Fellow. My research lies at the intersection of privacy, ethics, and artificial intelligence, with a focus on GDPR compliance, differential privacy, and the privacy-preserving applications of Large Language Models.
Previously, I completed my M.S. in Computer Science with a thesis on the hierarchical analysis of conspiracy theories. Beyond my academic work, I am an advocate for safe streets and active transportation, serving as a Board Member for Local Motion in Burlington, VT.
Advisee of Juniper Lovato
Member of Computational Ethics Lab
- Privacy Policy
- Differential Privacy
- Large Language Models
- Conspiracy Theories
- Honeypots
- Addresses a critical privacy challenge by using Differential Privacy to create private, synthetic health data that protects individual identities.
- Successfully applied this method to a real-world health study (LEMURS) of college students, protecting sensitive data collected from Oura rings and personal health surveys.
- Demonstrates that a practical balance is achievable, identifying a "sweet spot" where the generated data remains highly useful for researchers while significantly reducing privacy risks for participants.
- Developed a scientifically structured 'family tree' of conspiracy theories, categorizing and illustrating the connections among various conspiracies to enhance community understanding.
- Created the dataset by scraping articles from fact-checking websites and efficiently labeling them using , simplifying the process of identifying the main themes in each article.
- Developed a binary classifier using various machine learning methods, and our RoBERTa model achieved the highest performance with an F1 score of 87%, effectively distinguishing between conspiracy-related and non-conspiracy articles.
- Utilized the HDBSCAN + UMAP algorithm to facilitate effective data clustering and exploration, generating labels to be added to the main family tree.
Automated evaluation of privacy policies against evolving legal standards.
- Developing a novel scoring system to assess tech companies' privacy policies for compliance with major legislation, ranging from the 1980 OECD guidelines to modern GDPR and CCPA frameworks.
- Tracking the evolution of policy language over time to identify shifts in compliance and transparency.
- Utilizing a fine-tuned LLM model to automate the interpretation and ranking of complex legal text.
Leveraging Large Language Models to increase transparency in state government spending.
- Developed a contract classifier for over 20,000 Iowa state government contracts.
- Utilized Google Gemini embeddings with average embedding pooling, achieving an 85% F1 score.
- Implemented Few-Shot Chain-of-Thought prompting with GPT to handle complex classification tasks (71% accuracy).
- Fine-tuned LLaMA models for large-scale assisted labeling and multi-class classification.
- Simulated a secure, distributed cryptocurrency system, ensuring transparency and trust by enabling each participant to control and validate the ledger, effectively preventing fraudulent activities such as double spending.
- Leveraging a robust peer-to-peer network framework, our simulation demonstrates the essential processes of a cryptocurrency operation including transaction signing, block mining, broadcasting, and validation, culminating in a dynamic and decentralized digital currency ecosystem.
