Anar Amirli — Portfolio

While concept bottleneck models (CBMs) offer promise, they suffer from key limitations: the difficulty of defining clinically meaningful concepts, the high cost of annotations, reliance on heatmaps for localization, and potentially spurious alignment between visual features and textual labels. To avoid these pitfalls and leverage the strengths of vision models, we focus exclusively on visually grounded concepts. However, prior visually grounded methods often produce only global, class-specific explanations, neglect concept interactions, and providing unstable interpretability due to post-hoc nature.

Our framework addresses these issues by (a) discovering visual concepts with NMF, and (b) constructing concept graphs that capture interactions through a shallow GAT, balancing expressiveness and interpretability. Although our models do not outperform heavily optimized task-specific CBMs, they demonstrate consistent generalization across medical and standard datasets and, in some cases, surpass baseline CBMs, showcasing a more faithful, visually grounded alternative for explainability.

Experiments showed that combining statistical pattern recognition with multivariate anomaly detection pipelines significantly improves both accuracy and interpretability. By categorizing anomalies into distinct classes and highlighting faulty sensors, our method provides actionable insights for engineers monitoring production. This pipeline achieved promising results and demonstrates the value of integrating explainability into anomaly detection systems.

Traditional topology optimization relies on iterative solvers and finite element methods, which are computationally expensive and time-consuming. By leveraging modern generative approaches, we accelerate the design process while preserving structural accuracy. Notably, performance improved at higher iteration levels, underscoring the potential of generative AI to reshape how engineers approach design optimization across multiple modalities.