Faculty Profile

featured image

Julian A. Norato


Associate Professor
Ph.D., University of Illinois at Urbana-Champaign, 2005
julian.norato@uconn.edu

Campus Location: United Technologies Engineering Building Rm. 368

Tel: (860) 486-2345
Fax: (860) 486-5088

Personal Website: http://sol.engr.uconn.edu/

Short Bio:

Dr. Norato joined the Mechanical Engineering Department in 2014. He holds M.Sc. and Ph.D. degrees in Mechanical Engineering with Specialization in Computational Science and Engineering from the University of Illinois at Urbana-Champaign, and a Bachelor’s degree in Mechanical Engineering from the Universidad Nacional de Colombia. Prior to joining our department, he was responsible for the Product Optimization group at Caterpillar, where he and his team researched numerical methods and developed computational tools for structural and multidisciplinary optimization. He started and led the development of Caterpillar’s in-house topology optimization code, as well as the development of a method and tool for optimization of welding sequences to reduce weld-induced distortion, for which he and his collaborators received Caterpillar’s Move the Mountain Award. His current research interests lie in incorporating failure, geometric, manufacturing and cost requirements in computational topology and shape optimization techniques for the design exploration of novel and highly efficient structures and architected materials. Dr. Norato is a 2020 Air Force Research Lab Summer Fellow, the recipient of the 2019 ASME Design Automation Young Investigator Award, a 2018 NSF CAREER awardee and a recipient of the 2017 Office of Naval Research Young Investigator Program award. He currently serves as Review Editor for the Journal of Structural and Multidisciplinary Optimization and as Associate Editor of the ASME Journal of Mechanical Design. 

Keywords: Biomedical Related Applications, computational design, Computational Shape Modeling,Design and Optimization, Current, design optimization, multi-disciplinary optimization, topology optimization