WordPress on Google Compute Engine

Open Intelligence

Written by

Rojin Kianian Svenning

in

Authors

  • Rojin Kianian Svenning

    Rojin is Director of Data Science and Lead of Open Intelligence Performance AI at WPP, where she leads the teams building performance-driven cross-channel media intelligence and optimization. She brings 8 years of experience in adtech, spanning identity graphs, programmatic activation, and applied machine learning.

  • Chris Aivazidis

    Senior Data Scientist, Team Lead, and ML researcher dedicated to building high-impact, trustworthy AI systems. Currently leading multimodal fusion and recommender projects at Satalia and Choreograph (WPP), Chris has a proven track record of scaling AI platforms and developing advanced ML solutions for big clients. With a strong foundational background in mathematics and data science from TUM and AuTh, his expertise spans Explainable AI, risk modeling, and advanced AI workflows. Chris is also a published author and a frequent speaker at major AI conferences including PyData and MLCONF.

  • Andreas is a Data Scientist at Open Intelligence, with a background in Mechanical Engineering and a M.Sc. in Data Science and Machine Learning. His work focuses on developing and deploying advanced machine learning solutions, with expertise spanning deep learning, generative AI, probabilistic modeling, and variational inference. He has experience applying state-of-the-art AI techniques to real-world industry challenges, including multimodal learning, foundation models, and large-scale predictive systems.

  • I am a Senior Knowledge Graph Engineer and Semantic Technologies expert with over 20 years of experience designing and deploying enterprise-scale RDF Knowledge Graphs. I specialize in ontologies, taxonomies, semantic interoperability, and LLM-augmented knowledge engineering, with hands-on experience building and operating billion-statement graphs in production. My focus is on building scalable, explainable, and standards-aligned Knowledge Graph solutions that bridge human expertise and AI systems.

  • Francisco is a Senior Machine Learning Engineer with a background in computer science and engineering and a PhD in Informatics from the Technical University of Munich. His work has focused on applying machine learning to problems spanning scientific time-series analysis and image reconstruction, industrial control and forecasting, and graph-structured data in marketing. Across these domains, he has worked with supervised deep learning, probabilistic and generative methods for uncertainty estimation, and, more recently, graph neural networks and representation learning.

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