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 earned my MSc in Information Technology, specializing in Applied Machine Learning, from Carnegie Mellon University Africa.

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 GitHub Issue Resolution via Multi-Agent LLMs
Kipngeno Koech, Muhammad Adam, Baimam Boukar Jean Jacques, Joao Barros
•Preprint
Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
Baimam Boukar Jean Jacques, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe, Kipngeno Koech
•Preprint
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