Itilekha Podder is a data scientist at Robert Bosch Kft. and a Doctoral candidate in Computer Science at Eötvös Loránd University (ELTE). Her research focuses on interpretable machine learning, unsupervised deep learning, and multimodal representation learning, particularly applied to industrial quality control in semiconductor manufacturing. At Bosch, she designs AI systems for pattern recognition, sensor diagnostics, and process optimization—combining rigorous research with real-world deployment. Her recent work explores deep clustering frameworks and visual attribution methods that enable engineers to understand and trust AI-driven insights in label-scarce environments. She actively bridges academic innovation and industrial application within the context of Industry 4.0 and smart manufacturing.