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Catherine French, Member National Academy of Engineering
Awards and Honors, Department News, Structural engineering

Catherine French elected to the National Academy of Engineering

Posted

University of Minnesota-Twin Cities College of Science and Engineering Professor Catherine French elected to the National Academy of Engineering (NAE). This is among the highest professional distinctions awarded to an engineer. She joins Professor Emmanuel Detournay and Professors Emeriti Steve Crouch, Charles Fairhurst, and Theodore Galambos.

Nahum Yelizarov
Department News, Structural engineering, Student Activities

Nahum Yelizarov, BCE + Research Led to a Master's Degree

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"Getting involved in research with various professors let me explore my interests and opened a door to a Master’s degree. Taking higher level design and theory classes in structural engineering, as well as doing a thesis, allowed me to get a better technical understanding and taught me to how educate myself on topics I don’t know.”

Ketson R.M. dos Santos
Department News, Research, Structural engineering

Ketson R. M. dos Santos joins Structural Faculty

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CEGE's strong commitment to educating structural engineers continues with a new additionof Ketson dos Santos to the structural faculty. He comes to UMN from the École Polytechnique Féderale de Lausanne (EPFL), Switzerland, where he was a Post-Doctoral Researcher. His expertise lies in uncertainty quantification and computational engineering dynamics/mechanics.

Caleb Widstrand
Awards and Honors, Department News, Structural engineering

Caleb Widstrand Earns Best Master’s Thesis

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​​​​​​​Caleb Widstrand completed and defended his Master’s of Science thesis, “Wave Propagation in Periodic, Configurable Kerfed Metamaterials,” in 2021 under the direction of his adviser Professor Stefano Gonella. And now, Widstrand’s thesis has been chosen as the best Master’s Thesis within the Department of Civil, Environmental, and Geo- Engineering (CEGE).

Qizhi He in his office
Awards and Honors, Department News, Structural engineering

Reducing Defects in 3D Metal Printing by Scientific Machine Learning

Posted

Qizhi He and Ju Sun are developing a physics aware machine learning framework to help predict defects in metal Additive Manufacturing (3D printing). Their novel, knowledge-augmented, machine learning tool will quickly and reliably predict thermal mechanical behavior and the induced defects, by utilizing both thermomechanical models and process monitoring data.