ARCH 702B Computational Design Studio 2: Fabrication and Robotics Prof. Pablo Lorenzo-Eiroa Midterm Review Students Presentation Order in Midterm Review Video: Farah Jalil, Yousef Ismail, Salma Kattass, Kavya Sista Midterm Review Invited Guest Critics: Fred Levrat, Tom Verebes, David Diamond, Marcella del Signore, Nishan Kazazian, Nelson Montas Lacurente The ARCH 702B Studio Syllabus aims at researching informational flows between algorithms, Machine Learning, Structural simulation and optimization and Robotically informed fabrication, understanding continuity and non linear feedback between computational design and informed fabrication. Students started by developing analog models based on the work of engineers and architects that developed methodologies for form finding architectural models in relation to structural performance, including but not limited to Gaudi, Brunel, Fuller, Ricolais, Otto, Dieste, and others. The studio is working with several computational and robotic technologies understanding form as structure, including the range of emergent conditions that arise from fabrication. Although students are working with simulation, optimization and ML, the studio aims at unmotivating drawing-modeling and activate design through fabrication, avoiding any translation between representational modes and understanding performance in the object of study. Students are at the moment working on scaled prototypes understanding structural forces in architectural typologies and optimizing 3d printing models, will then proceed to activate feedback loops through and personalized technology workflow activating AI ML, to transition into full scale prototypes in the Summer of 2022 during their last term in the program.
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Data, Matter, Design Edited by Marcella Del Signore (NYIT), Nancy Diniz (UAL) and Frank Melendez (CCNY) presents a comprehensive overview of current design processes that rely on the input of data and use of computational design strategies, and their relationship to an array of outputs. The discussion included Randy Deutsch (ICC), Jeremy Edminston (CCNY), Shwan Rickenbacker CCNY), Allen Sayergh (HGSD), Anthony Vanky (UM), and New York Institute of Technology faculty Mathew Ford (NYIT), Pablo Lorenzo-Eiroa (NYIT), and Jason Van Nest (NYIT)
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