Curriculum Vitae
Summary
Master's student in the Machine Learning Department at Carnegie Mellon University focused on reliable and interpretable AI, multi-agent training paradigms, and activation-based model analysis.
Education
Experience
Research fellowship advised by Shi Feng.
Develop multi-agent reinforcement learning environments to improve legibility of reasoning traces. Design user studies and adversarial setups to measure influence of legibility on human and multi-agent interactions.
Engineered activation profiles of language model evaluators to suppress self-preferential bias, applied to LLM routers (ICML 2026, MechInterp @ NeurIPS 2025). Winner of Mechanistic Router Interpretability Hackathon (40 submissions).
Designed and trained large-scale graph-text models; evaluated adversarial robustness of LLMs; built historical correspondence networks and dashboards.
Automated ETL pipelines and implemented synthetic-data imputation for compromised client data.
Deployed decision-management systems and structured-prediction models on meeting transcripts.
Built Amazon sales data visualizations and automated analytics with GCP BigQuery and BERT-based embedding search.
Extended NER to long-tail ethnics cuisines using Bon Appetit data.
Publications
Conference Papers
Dani Roytburg, Matthew Bozoukov, Matthew Nguyen, Jou Barzdukas, Mackenzie Puig-Hall, and Narmeen Oozeer. Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations. Forty-Third International Conference on Machine Learning, 2026.
Dani Roytburg, Shreya Sridhar, and Daphne Ippolito. Measuring Weak-to-Strong Legibility of Reasoning Models. ICML 2026 Workshop on AI for Good (AI4GOOD), 2026.
Dani Roytburg* and Beck Miller*. Mind the Gap: Pathways Towards Unifying AI Safety and Ethics Research. Proceedings of the International Association for Safe and Ethical AI, 2026.
Dani Roytburg*, Matthew Bozoukov*, Hongyu Fu, Matthew Nguyen*, Jou Barzdukas*, and Narmeen Fatimah Oozeer. Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators. Mechanistic Interpretability Workshop at NeurIPS 2025, 2025.
Dani Roytburg*, Deborah Olorunisola*, Sandeep Soni, and Lauren Klein. Words and Action: Modeling Linguistic Leadership in # BlackLivesMatter Communities. Proceedings of the International AAAI Conference on Web and Social Media, 2025.
Theses
Daniel Roytburg. Generative Argument Mining: Pretrained Language Models are Argumentative Text Parsers. Undergraduate Thesis, Emory University, 2025.
Awards & Recognition
- Best Poster, LTI Student Research Symposium, 2026
- Emory University Dean's List, 2022, 2024, 2025
- Winner, Martian Research Mechanistic Interpretability Hackathon, 2025
Skills
Python · Java · R · JavaScript · Typescript · SQL · PyTorch · JAX · HuggingFace Transformers · scikit-learn · spaCy · networkx · D3.js · React.js · GCP · Docker · MySQL