Ghezal Ahmad Jan ZIA

With over 15 years of experience in computer science and a PhD in the field, my role as a researcher at the Bundesanstalt für Materialforschung und -prüfung (BAM) has allowed me to specialize in developing AI solutions for material testing. Specifically, I focus on large language models (LLM) and data-driven approach. Throughout my career, I have successfully designed, implemented, and optimized AI-driven systems for diverse applications. My passion lies in innovation, problem-solving, and collaboration. I am committed to continuous learning and strive to leverage AI to enhance the quality, efficiency, and safety of material testing.

• 11/01/16, DAAD scholarship, Ph.D. in Informatics, TU Berlin, Berlin
• 01/01/11, DAAD, Scholarship, Master in Informatics, TU Berlin, Berlin
• 03/01/09, CCENT/CCNA, CISCO Academy

• 2020 - 2021, Berlin International University of Applied Sciences, Machine Learning, Introduction to Relational Database, Python, Sklearn, Pandas, Numpy, Matplotlib, Seaborn, MySQL, PostgreSQL
• 2014 - 2017, Faculty of Computer Science, KABUL UNIVERSITY, Software Engineering, Machine Learning, Introduction to Relational Database, Database Security, Data Mining, Python, Sklearn, Pandas, Numpy, Matplotlib, Seaborn, MySQL, PostgreSQL, Junit, UML

• 2020, Dari dataset for named entity recognition (DariNER1)
• 2020, Dari dataset for named entity recognition (DariNER2)
• 2020, Dari dataset for part-of-speech
• 2020, Dari dataset for coreference resolution
• 2020, Dari language stopword lists

• 2024, An Adaptive Upscaling Approach for Assessing Materials' Circularity Potential with Non-Destructive Testing (NDT)
• 2024, Leveraging Large Language Models for Automated Knowledge Graphs Generation in Non-Destructive Testing
• 2024, REINCARNATE: SHAPING A SUSTAINABLE FUTURE IN CONSTRUCTION THROUGH DIGITAL INNOVATION
• 2024, Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing
• 2023, The Impact of CyRIS in Ascertaining Accurate Implementation of Projects
• 2023, 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
• 2023, Data-driven design of alkali-activated concrete using sequential learning
• 2023, Presenting SLAMD - A Sequential Learning-based Software for the Inverse Design of Sustainable Cementitious Materials
• 2023, The Intersection Between Semantic Web and Materials Science
• 2023, Natural MSE Queries - A natural way to query Materials Science Engineering data experiments
• 2022, Applying IoT on Contemporary Traffic System
• 2022, ACCELERATING THE SEARCH FOR ALKALI-ACTIVATED CEMENTS WITH SEQUENTIAL LEARNING
• 2020, A comparative mention-pair model for coreference resolution in the Dari language for information extraction
• 2020, HMM-based Dari named entity recognition for information extraction
• 2019, Applying IoT on contemporary traffic system