Brandone Fonya
MEng. Artificial Intelligence • Carnegie Mellon University
I am a graduate research assistant, teaching assistant and final year Master's student in Engineering Artificial Intelligence at Carnegie Mellon University's College of Engineering, specializing in machine learning and computer vision. My research focuses on deep learning for medical imaging and healthcare.
Prior to CMU, I earned a Bachelor's degree (Hons) in Software Engineering from The ICT University in Cameroon, graduating first in my department. I am currently working with Upanzi Network AI research team on advancing precision oncology through generative models for breast cancer in Africa.
Beyond work, I enjoy traveling, reading, and watching documentaries.
Email: bfonya [at] andrew [dot] cmu [dot] edu
Recent News
- 01/2026 – One paper accepted for Oral presentation at IEEE/CVF WACV 2026 workshop, Tucson, Arizona.
- 01/2026 – Joined CMU's (18-662) Principles and Engineering Applications of AI as Graduate TA for Spring 2026.
- 01/2026 – Joined CMU's (18-661) Introduction to Machine Learning for Engineers as Graduate TA for Spring 2026.
- 12/2025 – Featured by Carnegie Mellon University for research journey to China. Read here
- 09/2025 – Started as Graduate TA for (18-751) Applied Stochastic Processes, Fall 2025.
- 07/2025 – Joined Autonomous Intelligence Lab as research intern at Westlake University, China.
- 06/2025 – Paper accepted at European Public Health Conference 2025, Helsinki, Finland.
- 05/2025 – Visited University of Cape Town to explore AI research opportunities.
Research
I am committed to research that ensures AI benefits society, specifically in healthcare. I focus on developing efficient deep learning models for computer vision applications with particular interest in medical imaging and analysis.
Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
Baimam Boukar, *Brandone Fonya, Nchofon Tagha, Pauline Nyaboe
A zero-shot EEG decoding framework that learns subject and task-invariant neural priors from large-scale HBN data, enabling robust cross-subject generalization without calibration.
Optimizing healthcare facility distribution in Rwanda: a data-driven approach
*Brandone Fonya, Irene Busah, Michaella Rugumbira, Nchofon Tagha, Emily Aiken
Analyzed health facility distribution in Rwanda using geospatial mapping and ML to propose equitable resource allocation based on disease prevalence.
Robust Non-Invasive Tuberculosis Triage Using Audio Embeddings from Solicitated Cough Sounds
Timothy Belekollie1, *Brandone Fonya, Edwin Mugume, Conrad Tucker, [...].
Audio-based TB screening using pretrained foundation models achieving AUC of 1.000, optimized for mobile edge inference.
CAM-FD: Improving Adversarial Robustness without Sacrificing Generalization
*Brandone Fonya, Denis Musinguzi, Prasenjit Mitra
Curriculum Adversarial Mixup with Feature Denoising framework for increasing robustness while maintaining generalization.
Uncertainty-Aware Autonomous Driving in African Cities
Victor Miene, *Brandone Fonya, Joshua Momo, Ozan Tonguz
Modeling pedestrian behaviors in unstructured urban environments using ConvLSTM, enabling behavioral planning for autonomous vehicles.
Teaching & Service
Carnegie Mellon University
(18-661) Introduction to Machine Learning for Engineers - Graduate TA (Spring 2026)
(18-662) Principles and Engineering Applications of AI - Graduate TA (Spring 2026)
(18-751) Applied Stochastic Processes - Graduate TA (Fall 2025)
Service
Conference Reviewer, Applied Machine Learning Days (AMLD) Africa 2026
IEEE Student Member (2024 - Present)