This symposium invites contributions on sustainable nanomaterial innovation pipelines through automation, focusing on semiconductor nanomaterials, given their broad use in applications such as electronics, magnetics, photocatalysis, environment, and cancer therapy.
It will create a space where experimentalists in nanomaterial science, roboticists, engineers, along with experts in AI, and computational modelling, will discuss current challenges in nanomaterials technologies and emerging tools, from automated workflows, visual learning, to FAIR data-driven nanomaterial technologies, to the development and use of digital twins, for the next-generation of intelligent nanomaterials engineering.
- Discovery of unconventional nanomaterials for energy via automated workflows
- Computational screening vs high-throughput screening of challenging nanomaterials
- Digital Twins from experiments: challenges and innovation
- FAIR data-driven strategies for efficient semiconductor nanomaterials
- Safer by Design: Automating Toxic Workflows
I am an energetic, creative, female scientist with a solid expertise in Material Science and Technology. I have successfully implemented an engineering approach to guide the development of functional nanohybrids through general and simple routes. Throughout my work, I have introduced important mechanisms on the cooperative coupling of dissimilar materials in single structures, which represents a fundamental knowledge for the creation of a new-generation of nano and macro hybrid materials.
Andy Sode AnkerAngelo CangelosiKourosh Darvish is a staff scientist and principal investigator at the Acceleration Consortium at the University of Toronto. Previously, he served as a postdoctoral researcher at the Computer Science and Robotics Institute of the University of Toronto (UofT) and was a member of the Vector Institute. Before joining UofT in 2022, he worked as a postdoctoral researcher at the Italian Institute of Technology (IIT). In 2019, he completed his PhD in Bioengineering and Robotics from the University of Genoa, Italy. He earned his B.Sc. and M.Sc. degrees in Aerospace Engineering from K.N. Toosi University of Technology and Sharif University of Technology (Tehran, Iran) in 2012 and 2014, respectively. His research focuses on robotics, reinforcement learning & control theory, computer vision, reasoning & planning, and shared autonomy.
Shijing Sun is an associate professor at the University of Cambridge, and an affiliate faculty at the University of Washington. Her research primarily focuses on autonomous materials design and collaborative intelligence specifically aimed at advancing energy technologies. Sun previously held the position of a senior research scientist at the Toyota Research Institute. During her time there, she dedicated her efforts to the development of AI-powered solutions that aimed to accelerate R&D in the fields of electric vehicle batteries and fuel cells. Prior to joining the industry, Sun worked as a research scientist in the Department of Mechanical Engineering at the Massachusetts Institute of Technology, leading the development of high-throughput synthesis and characterization methods for thin-film solar cells. She completed her academic studies at Trinity College, University of Cambridge, where she obtained her BA degree in natural sciences, as well as MSci and PhD degrees in materials science.