Rastegar
Postdoctoral Researcher @Acceleration Consortium
Signup · Get unlimited contacts
WORK HISTORY
Postdoctoral Researcher @Acceleration Consortium
Toronto, ON, CA
Developed a self-driving lab for high-throughput liquid/powder dispenser, integrating machine learning for optimization and uncertainty reduction in experimental parameter space.• Designed and fabricated custom hardware and prototypes for precise mechanical/chemical/electrical/pharmaceutical synthesis and characterization.• Applied advanced imaging techniques to assess homogeneity, defects, and thermal properties (Inverse Heat Transfer).• Collaborated with multidisciplinary teams to develop methodologies for self-driving lab with autonomous synthesis and characterization and enhance functionality.• Optimized polymer synthesis conditions, including molecular weight, concentration, and crosslinking.• Implemented machine learning algorithms to refine experimental designs and improve data reproducibility.
EDUCATION
University of Tehran
Master's degree, Mechanical Engineering
University of Toronto
Doctor of Philosophy - PhD, Mechanical Engineering
University of Tehran
Bachelor's degree, Mechanical Engineering
ABOUT RASTEGAR
Postdoctoral Fellow at the Acceleration Consortium, University of Toronto, specializing in the intersection of mechanical engineering, data science, and materials discovery through self-driving laboratories. EXPERTISE:• Mechanical Engineering: Solid & fluid mechanics, FEA/CFD simulations, materials characterization• Data Science & AI: Machine learning, physics-informed neural networks (PINNs), Bayesian optimization• Materials Discovery: Self-driving lab automation, high-throughput synthesis & characterization• Advanced Manufacturing: 3D printing, microfabrication, composite materials processing CURRENT FOCUS:Pioneering the future of materials science through autonomous laboratory systems that combine physics-informed AI with high-throughput experimentation. My work bridges the gap between traditional mechanical engineering and cutting-edge data science to accelerate materials discovery for sustainable technologies.Open to collaborations in autonomous laboratory design, physics-informed machine learning, and advanced materials characterization.
This profile is compiled from publicly available professional sources. Unifers is not affiliated with or endorsed by LinkedIn. Request removal of this profile.