I have actively participated in the following projects:
2022-Present: DayTiMe (Digital Lifecycle Twins for Predictive Maintenance)
DayTiMe addresses the gap of lacking functioning examples of digital twins for Predictive Maintenance (PdM) in actual industrial practice. It integrates findings and solutions from 14 industrial use cases and uses a generic value chain model. I contributed in improvement of MRI remote maintenance services at Philips and TU/e.
My main task is adapting a natural language processing model for analyzing engineers' maintenance notes in order to select the proper parts for a new maintenance order.
2020-Present: LARA (Lane Analysis and Route Advisor)
The project is related to the optimization of route planning for global air freight shipments, specifically for products with special handling needs, such as temperature-sensitive pharmaceuticals with the application of advanced data analytics and Artificial Intelligence (AI). As a postdoc researcher I mainly contributed in WP1 and WP2.
WP 1: Develop a concept for a search engine to determine the available routing options for a shipment with specific handling needs.
WP 2: Develop an AI-based decision support system that evaluates and compares different routing options for shipments and recommends the optimal routings.
2014-2018: Analyzing Persian Text
The project aims to develop different tools for annotation, extraction, analyzing, and predicting useful information from Persian texts. As a PhD student I mainly participated in WP2.
WP 2: Develop AI-based models for identifying and predicting relationships between entities from Persian texts.