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Research

My research focuses on Mechanistic Interpretability, AI Safety, and Machine Learning applications for Space Systems. I build tools to reverse-engineer foundation models and verify their reliability in high-stakes scenarios

An Adaptive Latent Semantic Analysis Framework for Binary Text Classification

An Adaptive Latent Semantic Analysis Framework for Binary Text Classification

Baimam Boukar JJ, Isaac Touza, Kaladzavi Guidedi

International Journal of Information Management

NLPLatent Semantic AnalysisInterpretabilityText ClassificationSubmitted
Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding

Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding

Baimam Boukar JJ, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe

Medical Imaging with Deep Learning (MIDL) 2026

EEGBrain-Computer InterfacesZero-Shot LearningNeural DecodingPublished
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

Baimam Boukar JJ, Allassan Nken, Miriam Rateike, Celia Cintas

AAAI Conference on Artificial Intelligence 2026

Information RetrievalDeepScanLLMs EmbeddingsAnomalous PatternPublished
Mapping Socioeconomic Air Quality Disparities In Rwanda Using Sentinel-5P TROPOMI Data In Google Earth Engine

Mapping Socioeconomic Air Quality Disparities In Rwanda Using Sentinel-5P TROPOMI Data In Google Earth Engine

Baimam Boukar JJ, Kamikazi Raissa, Bertin Ndahayo, Evelyne Umubyeyi

IEEE MIGARS 2025

Air QualityRemote SensingGoogle EEPublished
Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks

Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks

Baimam Boukar JJ, Clarence Worrel

Harvard AstroAI Workshop 2025.

SpaceAIAstrophysicsPresented
Computer Vision-based Calibration of Visual Landing Aids Using Autonomous Drones (PAPI Case Study)

Computer Vision-based Calibration of Visual Landing Aids Using Autonomous Drones (PAPI Case Study)

Baimam Boukar JJ, Alice Mugengano, Jonathan Kayizzi, Richard Muhirwa

IEEE Aerospace Conference 2026.

Computer VisionAIAerospaceReview