bayesian-optimization
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Filip Auksztol
Filip Auksztol is the CEO and founder of Quantum Boost, a London-based deep-tech startup that uses machine learning to help chemists formulate better products faster. A trained quantum physicist who earned a PhD at Singapore's Centre for Quantum Technologies and studied materials science at Oxford, he turned the math of atomtronic devices toward a more stubborn problem: the slow, trial-and-error grind of laboratory formulation. Quantum Boost's platform applies Bayesian optimization to experimental data, promising to cut formulation development time by 50 to 80 percent across inks, paints, pharmaceuticals and specialty chemicals.
Scott Clark
Scott Clark is the Co-founder and CEO of Distributional, an enterprise AI testing platform that helps companies identify behavioral drift and unknown failures in AI systems. A triple-degree graduate of Oregon State University with a PhD in Applied Mathematics from Cornell, he co-founded and sold SigOpt (a Bayesian optimization platform backed by Y Combinator and a16z) to Intel in 2020, then led 200 engineers as VP & GM of AI/HPC Supercomputing at Intel before founding Distributional in September 2023. The company raised $30M in under a year, including a $19M Series A led by Two Sigma Ventures in October 2024.
Hermann Tribukait
Hermann Tribukait is the co-founder and CEO of Atinary Technologies, a Lausanne- and Silicon Valley-based deeptech startup that built SDLabs — a no-code AI/ML platform compressing years of R&D into days. A Harvard-trained economist who helped coin the term 'Self-Driving Labs®' in 2017, Tribukait has channeled a career spent brokering $200M+ in global R&D partnerships into software that lets machines design experiments, learn from results, and iterate without human bias getting in the way. Atinary's tools are now used in pharma, biotech, chemicals, and climate tech, with a physical self-driving lab open in Boston since early 2026.