Statice's synthetic data technology is now part of Anonos Data Embassy, the award-winning data security and privacy solution.
Synthetic data creates unprecedented data access and privacy protection opportunities for data science, engineering, compliance, and data protection stakeholders across industries. The increased interest is bringing new providers and solutions on the market, along with a new stream of novel research ideas and approaches. As a result, it's more challenging to navigate the field and choose the right technology for your needs.
In this webinar, the Statice team presents the technological landscape of synthetic data, along with its own solution. Our experts cut through the noise around synthetic data and provide an overview of the current technical approaches and differentiate them. Join the discussion with Benjamin Nolan, Head of Business Development, Daniel Perez, Product & Engineering Lead, and Dr. Christoph Wehmeyer, Senior Data Scientist, to learn about:
Key takeaways
speakers
Ben heads up commercial efforts at Statice. He works with Statice's partners to help them overcome their data access and privacy challenges with privacy-preserving synthetic data. He gets excited about data-driven innovation and has spent the last 8 years working in data focussed startups and scaleups.
Dani leads the product and engineering efforts at Statice. He holds a computer science degree and a master's degree in operations research. He has spent most of his professional career optimizing airline and airport operations using mathematical optimization, software engineering and the proper use of data. He founded a company which offers software to trade energy in the Spanish and Portuguese energy markets in an automated and optimized way.
Within the product team, Christoph plays a key role in the machine learning developments at Statice. He holds a Ph.D. in theoretical physics from the Freie Universität of Berlin. Prior to joining Statice, Christoph spent five years as a postdoctoral researcher investigating machine learning methods for molecular biology.
