The team behind Laplace
Laplace brings together physicists with doctoral and master’s degrees, leaders of complex scientific R&D, and experienced founders and business operators.
Our collective qualifications
Our collective academic qualifications include PhDs in physics, an MSc in physics, and a Master in Public Administration.
Our education and research span the Weizmann Institute of Science, Harvard Kennedy School, Ben-Gurion University and Columbia University, including postdoctoral research at Columbia.
We are bringing that combination to one mission: creating the realities Physical AI learns from.
Our team’s research has been published in Nature, Science and Nature Communications. Our backgrounds span numerical simulation, experimental physics and scientific instrumentation, including developing computational models and new ways to measure physical phenomena.
That experience matters to Laplace: useful simulated experience depends on understanding the physics, choosing sound approximations, and knowing how to test a model against reality.
Our team includes decades of senior scientific leadership in national laboratories and government R&D, alongside experience developing physical technologies across modelling, materials, experiments and engineering.
We understand how to connect disciplines, turn scientific requirements into testable systems, and lead ambitious research through technical uncertainty. Those are central challenges in making simulation accessible across different physical domains.
Our commercial experience includes founding and leading companies, P&L responsibility, and bringing technical products to customers across software, cybersecurity and regulated industries.
That experience connects Laplace’s scientific ambition to practical needs: choosing valuable problems, building products around real workflows, and creating a business that can sustain the mission.
Built around the mission
Laplace sits where these backgrounds meet: physical understanding, computational experimentation and product execution. We are building toward a future in which established physics and large amounts of compute make far more experiments possible in parallel, giving Physical AI richer conditions, interactions and outcomes to learn from.
We are building in stealth. Individual team profiles will be shared when the time is right.
Early access to Laplace