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Baimam Boukar Jean Jacques

Hi, I'm Baimam Boukar JJ 🥷🏾

I am a graduate student pursuing a Master of Science in Information Technology, Applied Machine Learning at Carnegie Mellon University Africa. My research and projects interests center on Earth Observation, and Artificial Intelligence applications in Astronomy, Space Missions Design and Space Operations. My expertise lies in Machine Learning and Software Engineering, and I am continuously building skills around space missions design. I am passionate about the idea of applying these skills and expertise to impactful space projects in a dynamic environment to gain more experience, and be ready for a competitive PhD program.


Currently analyzing spacecraft telemetry for anomaly detection using causal inference methods

Recent Updates

Jun 1, 2025Joined IBM as a Research Scientist Intern

Jul 4, 2025Attending the African Aviation Summit 2025

May 4, 2025Presented a talk about Geospatial AI

Jun 30, 2025Presented a Talk on Open Source


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Featured Research

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Highlights from my research portfolio spanning remote sensing, AI for healthcare, and space systems.

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

NeurIPS 2026

Ongoing

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

EEGBrain-Computer InterfacesZero-Shot Learning
Co-authored with Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe, Kipngeno Koech
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

AAAI 2026 Workshop on New Frontiers In Information Retrieval

Review

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

Information RetrievalDeepScanLLMs Embeddings
Co-authored with Allassan Nken, Miriam Rateike, Celia Cintas
Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks

Harvard AstroAI Workshop 2025.

Presented

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

SpaceAIAstrophysics
Co-authored with Prof. Clarence Worrel
Computer Vision-based Calibration of Visual Landing Aids Using Autonomous Drones (PAPI Case Study)

IEEE Aerospace Conference 2026.

Review

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

Computer VisionAIAerospace
Co-authored with Alice Mugengano, Jonathan Kayizzi, Richard Muhirwa
Humidity Inference with Geographic Features and Machine Learning for Enhanced Contrail Prediction for African Airspace

IEEE MIGARS 2025

Published

Humidity Inference with Geographic Features and Machine Learning for Enhanced Contrail Prediction for African Airspace

AIAviationContrails
Co-authored with Alice Mugengano, Jonathan Kayizzi

Featured Projects

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A showcase of my favorite projects spanning mobile apps, web applications, and open-source contributions.


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