Co-Founders

Our co-founders bring a broad range of expertise from the fields of numerical modelling, data science and insurance

Thomas Loridan
Co-Founder

Thomas leads the development of our global risk models, with a slight obsession for all things machine learning. Having built several tropical cyclone risk models for the cat modelling industry, Thomas has a strong understanding of the strength and limitations of current model solutions. His job at reask is to make sure we get rid of these limitations.

Contact Thomas for guidance and advice on our range of catastrophe risk products and services
Risk Modeling Solutions
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Nick Hassam
Co-Founder

With over 20 years’ experience in the application of technology to the commercial sector, Nick’s work has focussed on the use of spatial science and technology to help the global re/insurance industry better understand the impact of natural catastrophes. In close collaboration with our partners and clients, Nick is helping define the future of catastrophe modelling.

Contact Nick to discuss commercial arrangements, partnerships and implementing our solutions in your business
Insurance Risk and Partnerships
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Eugene Dubossarsky
Co-Founder

When it comes to leveraging the latest advances in machine learning, we understand there is no faking it. That’s why we turned to the best for advice and mentoring. Eugene has over 20 years of experience playing with neural nets and the likes, and we are proud to have him as reask’s in house AI guru.

Contact Eugene to understand how data science can better service your organisation
AI and Innovation
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Nico Bruneau
Co-Founder

Our models are only as good as the data they are built on. Nico’s job is to ensure we make best use of the constant increase in both quality and availability of global earth system data. Beyond management of the raw data Nico’s key contribution is the development of our automated knowledge discovery engine, using state-of-the-art methods from the field of image processing and pattern recognition.

Contact Nico for deep understanding of the underlying components of our models
Earth System Data
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