Eric Onyame
Education News Research Publications Teaching CV
Portrait of Eric Onyame
Email: reh6ed@virginia.edu
Trustworthy AI · AI Safety · Multilingual AI · Multimodal AI

Eric Onyame

PhD Candidate · University of Virginia · School of Data Science

I am a third-year 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 Applications. 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 science 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.

View CV Selected Publications

Education

  • 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 Weeraseena
  • University of Cape Coast, Ghana BSc in Mathematics with Economics · Aug 2016 – Jul 2020 First Class Honors

News

  • Jan 2026 Detailed update on my paper submission will be posted soon.
  • Mar 2025 Gave a talk on the applications of large language models at the UVA School of Data Science.
  • August 2023 Started PhD at the University of Virginia, School of Data Science.
  • Mar 2023 Received the 2023 Outstanding Master’s Student in Mathematics award at the University of Tennessee at Chattanooga.
  • Feb 2023 Defended my master’s thesis in mathematical optimization. Title: Covering Problem with Minimum-Radius Enclosing Circle.
  • April 2022 Inductee, Pi-Mu Epsilon Mathematics Honor Society, USA.

Research

I am broadly interested in Trustworthy AI. I am curious and motivated by a simple question: how can we design AI systems whose outputs are reliable, faithful to evidence, and worthy of user trust?

Recently, I have been thinking about three directions:

  1. Multilingual AI: How can we build representations and evaluation pipelines that generalize across languages and produce consistent, high-quality responses in the user’s language?
  2. AI Safety: How can we monitor and mitigate unsafe or misleading behaviors in advanced AI systems? I study this through interpretability-based approaches and safety-focused evaluation, with an emphasis on AI alignment and control techniques.
  3. Multimodal AI: How can we extend these questions beyond text to models that reason over multiple modalities while remaining robust to distribution shifts and out-of-domain inputs?

Publications

More updates will be added soon.

  • CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning
    Eric Onyame, Akash Ghosh, Subhadip Baidya, Sriparna Saha, Xiuying Chen.
    (Under review) · Read the arXiv version
  • 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)

    • Teaching

    Teaching Assistant: Supported instruction and grading for core Data Science and AI courses, including:

    • 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: Data Management & Visualization 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.
    • Aug 2021 – Jun 2022 Elementary Statistical Analysis; College Algebra (UTC) Delivered lectures, graded assessments, and provided structured academic support to undergraduate students.

    Skills

    • Languages: Python, R, Julia, SQL.
    • Tools and Libraries: PyTorch, PySpark, Docker, Git, NumPy, Pandas, Matplotlib, scikit-learn, LaTeX, Cloud computing.

    CV

    Download / View my CV (PDF)