CV

Link to my resume.

Education

• PhD in Computer Science (Fall 2021 - Ongoing)
  • Mila and Intelligence and Data Institute (IID), Laval University, Quebec
  • Visiting Student Researcher at Standford Universty
  • Focus: Adversarial Robustness, Distribution Shifts and Uncertainty of Deep Learning Models
  • Research: Jonas Ngnawé - Google Scholar
  • DEEL project’s page: DEpendable and Explainable Learning - DEEL-QUEBEC - DEEL-FRANCE
• Master’s in Machine Learning (2018-2019)
  • AIMS African Master’s of Machine Intelligence, Kigali, Rwanda
  • Funded by Facebook and Google
  • Courses: Link complete list of courses (Fundamentals, Vision, NLP etc.)
  • Project: Scalable Inference with Distributed Gaussian Processes using the Wasserstein Barycenter.
• Master’s in Mathematical Sciences (2016-2017)
  • African Institute for Mathematical Sciences, Limbe, Cameroon
  • Project: k-Clustering Problem-Application to a Field Technician Allocation Optimization Problem.
• Master’s Degree in Computer Engineering (2011-2016)
  • Ecole Nationale Supérieure Polytechnique de Yaoundé, Cameroon
• Other: Machine Learning Summer School (MLSS), London 2019

Work experience

• Google AI resident (Oct 2019 - Sep 2021)
  • Google Brain Team Accra, Ghana
  • Development of robust methods for deep neural networks
  • Compression of deep neural networks by quantization and pruning
  • Tools: Python/Tensorflow 2.0, Google Cloud Platform (GCP)
  • Publication: Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Networks (Accepted at NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications)
• Computer Engineer (Jan - Aug 2018)
  • GroupOne Holding Company, Limbe, Cameroun
  • Website development
  • Fuel consumption prediction projects (Fraud Detection, Supply prediction)
  • Business plan for startup incubator creation
  • Industrial Maintenance (Refinery) of the Honeywell Experion System
  • IT Support
  • Tools: HTML5/CSS3/JavaScript/JQuery/JQueryUI/Ajax/PHP, MySQL, Méthodologie PMI, Business Model Canvas
• Freelance Software Developer (2017-2018)
  • Digitization of administrative procedures for various ministries in Cameroon
  • Development and maintenance of WordPress plugins (WooCommerce)
  • Tools: Electronic Document Management (EDM)/Electronic Archiving(MARCH RM), PHP/Woocommerce, MySQL Workbench, Microsoft Access, Git/Gitlab/Github, Java, Netbeans, SCRUM
• Software Engineer Intern (Feb-August 2016)
  • Bolloré Africa Logistics, Port of Douala, Cameroon
  • Automation and Analysis of the Review of Access Rights to the Information System
  • Detection of abnormal access rights
  • Tools: Talend Open Studio, Qlikview, C#, ASP.Net, Microsoft SQL Server, Visual Studio, UML/Merise, BPMN 2.0, PMI Methodology

Additional Skills

• Data Science/AI: Pytorch, Scikit-Learn, Pandas, Weight&Biases, Matlab, Octave, Docker etc.

Publications

Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling

Ngnawé. J., Heuillet, M., Sahoo, S., Pequignot, Ahmad, O., Durand, A. Y., Precioso, F., Gagné, C. (2025). Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling. arXiv preprint arXiv:2509.23325.

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Heuillet, M., Bhagwatkar, R., Ngnawé. J., Pequignot, Y., Larouche, A., Gagné, C., Rish, I., Ahmad, O., Durand, A. (2025). A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy. arXiv preprint arXiv:2508.14079.

A Layer Selection Approach to Test Time Adaptation

Sahoo, S., ElAraby, M., Ngnawe, J., Pequignot, Y. B., Precioso, F., & Gagné, C. (2025, April). A layer selection approach to test time adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 19, pp. 20237-20245).

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers.

Ngnawé, J., Sahoo, S., Pequignot, Y., Precioso, F., & Gagné, C. (2024). Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers. The Thirty-eighth Annual Conference on Neural Information Processing Systems.

Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets

Ngnawe, J., NJIFON, M. A., Heek, J., and Dauphin, Y. Robustmix: Improving robustness by regularizing the frequency bias of deep nets. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications, 2022. URL https://openreview.net/forum?id=Na64z0YpOx.

Talks

Galsen AI Reading Group

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

January 11, 2025

Talk at Galsen AI Youtube Channel, Online

Neptune.ai Challenge

AI Research Paper Overview: Detecting Brittle Decisions For Free

November 14, 2024

Talk at Neptune.ai Youtube Channel, Online

KmerAI Talks

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

July 20, 2024

Talk at KmerAI Youtube Channel, Online(https://kmer-ai.org/kmeraitalks.html)

IndabaX Cameroon 2022

Robustness in AI

December 22, 2022

Talk at IndabaX-Cameroon 2022, Polytechnique Yaounde, Online

Teaching

Teaching Assistant/Introduction to Machine Learning by Christian Gagné

Crash Course Introduction to Statistical Machine Learning

Teaching Assistant (Tutor) at AIMS-Cameroon

Activities

• Host (2024)
  • KmerAI-talks
• Keynote speaker (Dec. 27, 2022)
  • Deep Learning IndabaX-Cameroon: Robustness in AI
• Organizing committee member
  • Interpolate workshop @Neurips2022: First Workshop on Interpolation Regularizers and Beyond.
• Founding member (Since 2020)
  • Kmer-AI, an association for the promotion and vulgarization of AI and Machine Learning in Cameroon.
• Co-organizer (Since 2020)
  • Machine Learning Projects Competition (MLPC), Polytechnique Yaoundé
• Co-organizer (September 2019)
  • Two (02) week introductory course in Machine Learning, Polytechnique Yaoundé
• Co-organizer (April 2019)
  • First Deep Learning IndabaX Rwanda, a conference on Machine Learning and AI
• Reviewer (2022, 2023, 2024)
  • Reviews: Algorithmic Fairness through the Lens of Causality and Privacy (2022), Montreal AI Symposium, Black in AI Workshop @NeurIPS, Deep Learning Indaba.