I'm Jalil, a Machine Learning Reseacher focused on building robust, generalizable machine learning systems for real-world and regulated environments, primarily in imaging.
I work across the full ML lifecycle: problem formulation, experimental design, model development, and deployment under practical constraints. My interests are in understanding why models work (or fail)—with particular interest in generalization, robustness, uncertainty, and physics-informed learning.
Tools: Python, PyTorch, TensorFlow, computer vision pipelines, ML experimentation tooling, and production-oriented workflows. Frontend and systems experience when needed are used pragmatically, not for novelty.
Note: This GitHub contains research prototypes, experiments, and selected engineering work. Not everything here is polished; some repositories exist to document ideas, failures, and trade-offs rather than finished products.