Gabriella Pizzuto

Dr. Gabriella Pizzuto is a Lecturer in Robotics and Chemistry Automation with a joint appointment in the Department of Computer Science and Department of Chemistry at the University of Liverpool. She serves as Research Area Lead for the Royce Digital Chemical Materials Design Research Area, holds a Royal Academy of Engineering Research Fellowship, and acts as ECR Co-Chair for the EPSRC AI Hub in Chemistry (AIChemy).

Her educational background includes a Ph.D. in Computer Science (2020) from the University of Manchester, where she was a Marie-Sklodowska Curie Early Stage Researcher. During her doctoral studies, she held visiting scholar positions at the University of Edinburgh and the Italian Institute of Technology, followed by a postdoctoral research associate position at the Edinburgh Centre for Robotics.

Dr. Pizzuto's research sits at the critical intersection of robotics and applied machine learning for scientific discovery. Her work specifically targets:

• Multimodal robot skill learning in unstructured environments
• Failure recovery systems for chemistry laboratory automation
• Safe human-robot collaboration frameworks
• Accelerated materials discovery through autonomous experimentation

Her research group develops learning-based methods to enable robotic scientists to operate with increasing autonomy in complex experimental settings.

Dr. Pizzuto's scientific recognition includes:

• Royal Academy of Engineering Research Fellowship
• Marie Skłodowska-Curie Early Stage Researcher Fellowship

Her research trajectory demonstrates strong grant leadership, having served as Lead Research Associate on the ERC Synergy Grant 'Autonomous Discovery of Advanced Materials' (ADAM) before establishing her independent research program. Current funding is supported through her Royal Academy Fellowship and leadership roles in national research hubs.

Dr. Pizzuto leads a dynamic research group focused on developing intelligent robotic systems for chemistry laboratories, with particular emphasis on creating adaptive platforms that can learn from experimental failures and collaborate effectively with human researchers to accelerate scientific discovery.