Anahita Pakiman
I am a multidisciplinary Dr.-Ing. with 12+ years bridging mechanical engineering and data science while bringing applied research to industry. My career focuses on solving complex industrial challenges through innovative approaches combining domain expertise with cutting-edge technology.
After completing my Bachelor's in Mechanical Engineering, I pursued Applied Mechanics in Sweden, a premier program for industrial demands. This led to five years of industry experience, where I identified critical gaps in automotive R&D data management and optimization, developing solutions that shaped my research direction.
At Zeekr, I pioneered the first fully automated semantic-based reporting system for crash simulation data, making previously inaccessible results centralized and comparable. This breakthrough led to collaboration with Fraunhofer Research Institute for doctoral research on knowledge graphs for automotive crash simulation, establishing unique industry-academia partnership.
Following graduation, I expanded expertise in graph-based machine learning through consultancy across diverse domains: contract management at eccenca GmbH, job profiling at Interim, medical applications at Merck, and technical documentation at Zeiss SMT. This demonstrated versatility of graph-based approaches in solving complex data challenges.
Currently at Amazon, I've returned to mechanical engineering roots, leading initiatives leveraging graph technologies for reliability and maintenance challenges to enable Digital Twin. My work synthesizes traditional engineering principles with modern AI methodologies, positioning me at the forefront of next-generation industrial solutions.
Research interests include knowledge graphs, agentic reasoning, CAE simulation, graph analytics, LLM, and AI applications to technical domains. I'm passionate about translating academic research into practical industrial applications driving real-world impact.
