Research
My research focuses on understanding and improving Foundation Models and their applications in various high-impact domains, including Space, Healthcare, and Earth Observation.

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 DecodingSubmitted

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection
Baimam Boukar JJ, Allassan Nken, Miriam Rateike, Celia Cintas
AAAI 2026 Workshop on New Frontiers In Information Retrieval
Information RetrievalDeepScanLLMs EmbeddingsAnomalous PatternReview

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
Baimam Boukar JJ, Clarence Worrel
Harvard AstroAI Workshop 2025.
SpaceAIAstrophysicsPresented

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

Humidity Inference with Geographic Features and Machine Learning for Enhanced Contrail Prediction for African Airspace
Baimam Boukar JJ, Alice Mugengano, Jonathan Kayizzi
IEEE MIGARS 2025
AIAviationContrailsPublished