About Me
I'm a computer science and linguistics graduate of the University of Chicago and an MSAII student at Carnegie Mellon's Language Technologies Institute. My work spans LLM interpretability, agentic systems, NLP, and search and recommendation. I'm most interested in where language, meaning, and models meet. I also think a lot about how we can build long-term goals into AI and recommendation systems.
I'm open to research and AI/ML engineering intern roles starting summer 2027.
On a personal level, I enjoy learning languages, reading, playing music, wrestling, and cooking. Check out my Goodreads or give my band (from years ago) a listen!
About Projects
Most of my projects work with LLMs in interpretability and LLM applications.
Most recently I was an AI Fellow at Opteryx. Before that I was a research assistant with Dr. Zhewei Sun at the Speech and Language group at TTIC. Descriptions of these and other projects are below.
Opteryx
As an AI Fellow at Opteryx, I shipped a humanization feature into a production LLM pipeline that generates regulated pharmaceutical stability reports for paying enterprise customers.
I built a feature extraction system across five heterogeneous document corpora to model the stylistic and structural patterns of human-authored reports, which lifted reviewer acceptance by 40%. I also designed an evaluation pipeline that scores generated reports on factual correctness, logical consistency, register, and how well they reflect customer goals. This replaced a slow manual review process.
Isolated Knowledge Updates in LLMs
Course project, University of Chicago
Quantified the limits of localized model editing using counterfactual arithmetic edits on Qwen2.5-1.5B. A sweep of 13 standard editing configurations showed that every method which successfully injects a false belief collapses neighbor arithmetic accuracy from 90% to under 20%.
We compared three preservation mechanisms (KL regularization over a 50-item multi-domain locality set, explicit neighbor replay, and a frozen-backbone correction head) across arithmetic, geography, and CounterFact edits. An inference-time "alpha sweep" over the LoRA task vector gives continuous post-hoc control of the edit-preservation tradeoff.
*Note: The associated repository is currently private.
Prepost AI / Epistemic Hygienist
Engineered a multi-agent system using an AI architecture that integrates NLI nodes and RAG retrieval to mitigate "bullshit" assertions and promote nuanced social media speech.
The system gathers external context from sources like Wikipedia and BBC to perform structured reasoning. I validated performance across diverse agentic designs using models like Gemma 3, Llama 3.1, and GPT-OSS.
Research with Dr. Zhewei Sun (TTIC)
This research developed NLP techniques to better process and model informal language across languages.
We collected novel datasets for fine-tuning models using BeautifulSoup to more accurately model slang cross-linguistically. We also expanded and modified LLM architectures with PyTorch on distributed GPUs to build on previous research on embedding slang terms.
Nabu - Personal AI Language Tutor
Developed an AI native desktop application for real-time conversations with a personalized AI system to expand vocabulary using spaced repetition.
Crafted the workflows, prompts, and API endpoints, implementing a host of AI tools and APIs such as LangGraph, Whisper, and ElevenLabs.
Send an email to nolanpozzobon[at]uchicago.edu or connect on LinkedIn.