Max Robotics — Global Service Robot Directory + US Certification

About

Stephen Jesse is a leading figure at the intersection of scanning probe microscopy, electron microscopy, and artificial intelligence, pioneering the transformation of materials characterization from a manual craft into an autonomous, data-driven science. His core research focuses on developing machine learning and active learning frameworks to create “self-driving” laboratories, where experiments are dynamically guided by algorithms rather than static scripts. Jesse’s major contributions include the design of Bayesian-optimized recommender systems that enable curiosity-driven, partially human-in-the-loop automated experiments—a paradigm shift that accelerates the discovery of novel materials and physical phenomena. His seminal work, “Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy” (2021), has garnered over 134 citations, establishing a foundational roadmap for the field. By integrating real-time decision-making into microscopy, Jesse has dramatically increased experimental throughput and reproducibility, allowing researchers to explore vast parameter spaces with unprecedented efficiency. His ongoing development of dynamic, curiosity-driven active recommender systems (2023–2024) further refines this approach, balancing exploration and exploitation to uncover unexpected insights. Through these innovations, Jesse is not only advancing fundamental physics but also equipping the next generation of scientists with the tools to conduct smarter, faster, and more insightful experiments.