MSc Artificial Intelligence graduate building practical AI, machine learning, and decision-support systems.
I’m interested in AI systems that move beyond notebooks into usable tools: medical imaging workflows, explainable ML, route-aware decision support, multi-agent simulation, and human-in-the-loop operational systems.
Research prototype for automated L3 skeletal muscle analysis from abdominal CT scans. Includes DICOM/NIfTI processing, L3 slice selection, TotalSegmentator baseline masks, PyTorch U-Net segmentation, CSA/SMRA/SMI metric calculation, aggregate evaluation plots, and a Streamlit review interface.
End-to-end tabular machine learning workflow for used-car price prediction, including preprocessing, ensemble models, model comparison, feature importance, and explainability.
Comparison of BFS, DFS, UCS, and A* search algorithms on real OpenStreetMap road-network data, with benchmark outputs and runtime analysis.
Computer vision project for vehicle detection and classification using YOLOv8, annotated image data, validation metrics, and sample predictions.
Drone-based multi-agent search simulation for oil-spill detection under uncertainty, modelling search coverage, sensing radius, environmental dynamics, and agent coordination.
C# data-structures project implementing a binary-search-tree text indexer with search and lookup functionality.
Machine Learning & AI: PyTorch, scikit-learn, YOLOv8, OpenCV, SHAP Data & Scientific Computing: pandas, NumPy, matplotlib, Jupyter Medical Imaging: DICOM, NIfTI, SimpleITK, segmentation workflows Software & Tools: Python, C#, SQL, Streamlit, Git, GitHub Current Direction: explainable AI, decision-support systems, route-aware optimisation, and human-in-the-loop tools
I’m currently building a staff/session allocation recommender that combines constraints, scoring, travel feasibility, explainability, and human review for operational decision support.
- GitHub: @Inioluwa-Ashamu
- LinkedIn: www.linkedin.com/in/inioluwa-ashamu