Hi, I'm Baimam Boukar 👋
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.
Research Domains
Foundation Models
Understanding LLM limitations and developing improvements.
AI for Space Systems
Autonomous operations, mission planning, and anomaly detection for spacecraft.
Information Retrieval
Advanced retrieval architectures and long-context execution.
Geospatial AI
Deep learning for Earth observation, remote sensing, and environmental monitoring.
Selected Work
View AllFeatured Projects
Recent Research
Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health. Conventional approaches have been hindered by poor cross-subject and cross-task generalization, owing to high inter-subject variability and non-stationary neural signals. We address this challenge with a zero-shot cross-subject decoding framework on the large-scale Healthy Brain Network dataset,benchmarking a convolutional neural network baseline, a hybrid LSTM, and a Transformer-based foundation model. To adapt the Transformer for regression while averting catastrophic forgetting, we propose a novel progressive unfreezing strategy. The baseline yielded an nRMSE of 0.9991, whereas our fine-tuned Transformer achieved 0.9799 on unseen subjects.This work establishes scalable, calibration-free EEG decoding for computational psychiatry and behavioral prediction.
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection
A classical vector retrieval problem typically considers a single query embedding vector as input and retrieves the most similar embedding vectors from a vector database. However, complex reasoning and retrieval tasks frequently require multiple query vectors, rather than a single one. In this work, we propose a retrieval method that considers multiple query vectors simultaneously and retrieves the most relevant vectors from the database using concepts from anomalous pattern detection. Specifically, our approach leverages a set of query vectors Q (with |Q| ≥ 1), and identifies the subset of vector dimensions within Q that standout (anomalous) from the rest of dimensions. Next, we scan the vector database to retrieve the set of vectors that are also anomalous across the previously identified vector dimensions and return them as our retrieved set of vectors. We validate our approach on two image datasets, a text dataset, and a tabular dataset.
Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks
Classifying Potentially Hazardous Asteroids (PHAs) is crucial for planetary defense. While the official designation relies on simple metrics (Earth MOID and Absolute Magnitude H), we explore if complex dynamical relationships offer a robust, interpretable hazard assessment framework. We introduce a Graph Neural Network (GNN) approach that models asteroids as nodes defined by 11 orbital and physical features. Edges are defined using the Tisserand parameter relative to Jupiter (TJ) as a proxy for dynamical similarity, allowing the GNN to capture latent relationships among asteroid groups. Using the NASA/JPL Small-Body Database dataset, the model achieves an overall accuracy of 99% and an AUC of 0.99.