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If sensitive data can't be shared, how do we train machine learning? How can you do modern data architectures while placing Ethics and Privacy at the core of your data pipelines? Innovation is impossible without data - and Synthesized has found a new way to share data in a compliant manner. The Synthesized DataOps platform enables data-driven regulated organizations to automate data provisioning for research and development staying compliant with data privacy using AI-curated simulated data streams.
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Nicolai's led our growth from a simple idea to a service used by tech companies in the UK, Europe and the US. Nicolai’s responsible for the direction and product strategy of Synthesized. He holds a PhD in Machine Learning from the University of Cambridge.
Rob has recently completed his PhD in High Energy Physics at Imperial College London, where he worked on developing AI algorithms to search for dark matter in the universe. As part of his PhD, he developed an object-oriented Python framework to process noisy sensor data and implemented signal processing algorithms for downstream analysis. He also created a generative adversarial network architecture in PyTorch to speed up complex simulations and reduce computing requirements.
