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Curriculum Vitae

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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

M.S., Machine Learning 2026
Carnegie Mellon University, School of Computer Science
B.S., Computer Science; Quantitative and Social Sciences 2025
Emory University, College of Arts and Science

Experience

Research Assistant, Language Technologies Institute September 2025 – Present
Carnegie Mellon University, Pittsburgh, PA

Develop multi-agent adversarial reinforcement learning environments to improve legibility of reasoning traces.

Research Fellow May 2025 – Present
Martian Research, Remote

Engineer activation profiles of language model evaluators to suppress self-preferential bias; winner of a mechanistic interpretability hackathon.

Research Assistant, Digital Humanities Lab November 2021 – May 2025
Emory University, Atlanta, GA

Designed and trained large-scale graph-text models; evaluated adversarial robustness of LLMs; built historical correspondence networks and dashboards.

Internship - Business Technology Solutions June 2024 – August 2024
ZS Associates, Evanston, IL

Automated ETL pipelines and implemented synthetic-data imputation for compromised client data.

Intern — Machine Learning January 2024 – May 2024
Cloverpop, Chicago, IL

Deployed decision-management systems and structured-prediction models on meeting transcripts.

Intern — Global Marketing and Sales Data Science June 2023 – August 2023
McCormick and Company, Hunt Valley, MD

Built Amazon sales data visualizations and automated analytics with GCP BigQuery and BERT-based embedding search.

Research Assistant, Mediacloud Lab June 2022 – October 2022
Northeastern University, Boston, MA

Extended NER to long-tail ethnics cuisines using Bon Appetit data.

Publications

Conference Papers

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: Examining Self-Preference in LLM Evaluators through Activation-Based Representations. 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

  • 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