Papers
6
Total Citations
631
H-Index
4
About
Daylond Hooper is a researcher at the intersection of autonomous experimentation, materials science, and robotics, with particular expertise in applying machine learning and artificial intelligence to accelerate scientific discovery. His most influential contribution, "Autonomy in materials research: a case study in carbon nanotube growth" (2016, 377 citations), helped establish the conceptual and practical framework for self-directing research systems that reduce reliance on human-centered experimental cycles. Building on this foundation, his earlier work on wall-selective carbon nanotube growth (2014, 101 citations) demonstrated the power of automated experimentation in achieving fine-grained control over nanotube production — a longstanding challenge in the field. More recently, Hooper extended these principles to additive manufacturing, showing how Bayesian optimization can autonomously tune 3D printing parameters to minimize defects and accelerate material development (2021, 143 citations). His earlier career reflected a grounding in multi-robot systems and coalition formation under uncertainty, providing him with a systems-level perspective that informs his approach to autonomous laboratory design. Collectively, Hooper's work represents a compelling vision for the future of accelerated materials discovery through intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1Autonomy in materials research: a case study in carbon nanotube growth377 citations · 2016
- 2Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer143 citations · 2021
- 3
- 4HAMR: A Hybrid Multi-Robot Control Architecture4 citations · 2009
- 5Coalition Formation under Uncertainty3 citations · 2012
- 6Dynamic Behavior Sequencing for Hybrid Robot Architectures3 citations · 2011