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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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Research Focus

Exploring the intersection of machine learning theory and applied AI across multiple domains.

Machine Learning

Advancing core ML techniques for scalable and efficient AI systems

AI for Neuroscience

Applying AI to understand and interface with neural systems

AI for Space Systems & EO

Machine learning for spacecraft monitoring and Earth observation applications


Featured Research

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

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

Harvard AstroAI Workshop 2025.

Published

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

SpaceAIAstrophysics
Co-authored with team members
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)

SpaceAIAstrophysics
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

Remote SensingAviationContrails
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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