Eric Onyame
Eric Onyame
View CV

About Me

I am a PhD candidate in Data Science at the University of Virginia School of Data Science, where I am fortunate to work with Dr. Chirag Agarwal in the AIKYAM Lab. My research focuses on Trustworthy AI, with particular emphasis on Multilingual AI, AI Safety, and Multimodal AI.

Before starting my PhD, I earned an MS in Mathematics from the University of Tennessee at Chattanooga, where I worked with Dr. Lakmali Weerasena on facility location problems in mathematical optimization. I received my bachelor's degree in Mathematics with Economics (First Class Honors) from the University of Cape Coast in Ghana.

Current Research

I am currently studying chain-of-thought (CoT) monitorability under linguistic distribution shift, where I investigate how we can oversee and interpret the reasoning traces of large language models across different linguistic and cultural settings.

One of my recent works includes:

  • Curriculum-informed reinforcement learning for multilingual medical reasoning (ACL 2026, Oral).

News

Recent updates and milestones.

Apr 2026
One paper on multilingual medical reasoning in LLMs accepted to ACL 2026 (Main Conference, Oral). See you in San Diego! 🎉
Jun 2025
Passed the qualifying exam. 🎉
Mar 2025
Gave a talk on applications of large language models at the UVA School of Data Science.
Aug 2023
Started my PhD at the University of Virginia School of Data Science. 🎉
Mar 2023
Received a fully funded fellowship and the Provost Scholarship to pursue my PhD at the University of Virginia, and was also selected as a Quantitative Foundation Fellow.
Mar 2023
Received the 2023 Outstanding Master's Student in Mathematics award at UTC. 🎉
Feb 2023
Defended master's thesis: Covering Problem with Minimum-Radius Enclosing Circle.
Apr 2022
Inducted into Pi Mu Epsilon Mathematics Honor Society.

Research

I spend most of my time thinking about how to make AI models more reliable and aligned with human values. I am broadly interested in Trustworthy AI, with a focus on building systems that are safe, robust, and worthy of user trust. I am motivated by a central question: how can we design AI models whose outputs are honest, harmless, helpful, and faithful to evidence across diverse users, languages, and contexts?

Recently, I have been especially interested in three directions:

Multilingual AI

How can we build models, representations, and evaluation pipelines that generalize across languages and cultures, while producing responses that are consistent, accurate, and high-quality in the user's language?

AI Safety

How can we detect and mitigate unsafe or misleading behaviors in advanced AI systems, including deception, manipulation, sycophancy, and scheming? I study these through interpretability-based methods, safety evaluations, and work on alignment and control.

Multimodal AI

How do these challenges extend beyond text to models that reason across multiple modalities? I am interested in making multimodal systems robust to distribution shifts, out-of-domain inputs, and safety risks in complex settings.

Publications

Selected publications and ongoing work.

The Robustness of Existing AI Monitors
Eric Onyame, Chirag Agarwal.
In progress
Counterfactual LLM Verifiers for Math and Logic Reasoning Tasks
Elita Lobo, Eric Onyame, Yair Zick, Chirag Agarwal.
In progress

Education

Academic journey.

University of Virginia
PhD in Data Science · Aug 2023 – Present
Advisor: Dr. Chirag Agarwal
The University of Tennessee at Chattanooga
MS in Mathematics · Aug 2021 – May 2023
Advisor: Dr. Lakmali Weerasena
University of Cape Coast, Ghana
BSc in Mathematics with Economics · Aug 2016 – Jul 2020
First Class Honors

Teaching

Teaching Assistant for core Data Science and AI courses.

Spring 2026
DS 6050: Deep Learning
Led office hours, assisted with assignments, and guided students on optimization, regularization, and neural network architectures.
Fall 2025
DS 7800: Research Methods in Data Science
Supported student research development, methodology design, and critical evaluation of data-driven studies.
Spring 2025
DS 6051: Decoding Large Language Models
Assisted instruction on modern language models, including projects, readings, and practical evaluation and prompting workflows.
Fall 2024
DS 6600: Data Engineering I
Facilitated hands-on sessions on large-scale data systems and data visualization pipelines.
Fall 2024
DS 1001: Foundations of Data Science
Supported student understanding of core data science concepts through discussion, problem-solving, and feedback.
2021 – 2022
Elementary Statistical Analysis; College Algebra (UTC)
Delivered lectures, graded assessments, and provided structured academic support to undergraduate students.

Skills

Technical proficiencies.

Languages
Python R Julia SQL
Tools & Libraries
PyTorch PySpark Docker Git NumPy Pandas Matplotlib scikit-learn LaTeX Cloud Computing