Hi, I'm Baimam Boukar 👋
I am a research and software engineer working on mechanistic interpretability and the science of evaluations for advanced language models. At Jinesis AI Lab, University of Toronto, I study when probes, sparse autoencoders, and causal interventions generalize across model families, layers, tasks, and data distributions. I recently completed my master's in Applied Machine Learning at Carnegie Mellon University.

Jul 9, 2026 Intellibra won Cameroon's Social Entrepreneurship Prize (1st Place)
Research Focus
I study how language models represent, conceal, and act on information.
Mechanistic Interpretability
How models encode and use internal representations.
Deception & Situational Awareness
When models detect oversight, hide intent, or behave strategically.
White-box Control
Whether probes, steering, and patching provide reliable causal control.
Evaluation Science
How to measure emerging capabilities with trustworthy controls.
Selected Work
View AllFeatured Projects
Selected Publications
Phoenix: Safe End to End Codebase Refactoring via Multi-Agent LLMs
Baimam Boukar Jean Jacques, Kipngeno Koech, Muhammad Adam, Joao Barros
International Conference on Responsible AI (ICRAI) 2026•Published
Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
Baimam Boukar Jean Jacques, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe
Medical Imaging with Deep Learning (MIDL) 2026•Submitted
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection
Allassan Tchangmena A Nken, Baimam Boukar Jean Jacques, Miriam Rateike, Celia Cintas, Skyler Speakman
AAAI 2026 Workshop on New Frontiers in Information Retrieval•Preprint