2024 Speakers
Benjamin Jones
Representations and Learning in Computer Aided Design
Abstract: Computer Aided Design (CAD) systems are built around two complementary representation schemes, parametric geometry and procedural modeling, in order to simultaneously achieve precision, accuracy, and editability. While these constructs are very powerful, their discrete and heterogeneous nature has made applying machine learning to CAD modeling difficult, leading to a surprising dearth of AI in the world of mechanical design. In this talk, he will discuss how to overcome the challenges of data complexity and scarcity in CAD representations to enable learned analysis of CAD models. He will also present new and ongoing work in generating procedural representations of CAD models by drawing on semantic information in foundational text and image models.
Bio: Benjamin is a PhD student at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Adriana Schulz. His research bridges geometry, machine learning, and computational fabrication. He is interested in how to represent and explore complex design spaces. Lately he's been working on neural representations for CAD geometry, and AI exploration of CAD design spaces. Prior to joining the University of Washington he earned bachelor's degrees in physics and joint mathematics and computer science from Harvey Mudd College, where he helped develop a wireless power transmission array for space-based solar power. He has also built distributed systems for web analytics at Quantcast, and worked on computational imaging (Fourier ptychography) in the Biophotonics Laboratory at Caltech.
Prof. Dr. Daniela Mitterberger
Strange Comfort: Intuitive Machines and Robotic Gardeners
Abstract: This talk will explore technology as a primary mediator and familiar medium for structuring our relationship with the built and natural environment. Starting from a reflection on how human activities such as agriculture, pollution and urbanisation have significantly altered ecological dynamics, the talk will present how we can move the interaction between humans, machines and the environment from a traditional, static model to a dynamic, integrative approach. Such an approach promotes the transition from the optimisation and control of nature and machines to new forms of intelligence, autonomy and a deeper understanding of ecology as a dynamic continuum consisting of different media, signals and temporalities.
To illustrate the technological advances required for such a shift, this talk will present three case studies from Mitterberger's academic work as well as recent works by MAEID (Büro für Architektur & transmediale Kunst) that explore robotic gardens and intuitive machines. These case studies combine architectural design, sensor systems, robotic fabrication, machine learning and microbiology and show how we can create fertile ground for new relationships between humans, nature, and technology.
Bio: Daniela Mitterberger is an architect and researcher with a strong interest in new media and the relationship between humans, digital fabrication and emerging technologies. Mitterberger is an Assistant Professor at Princeton University, where she develops innovative computational methods that enable human-machine collaborative processes through adaptive digital fabrication and extended reality. She was a postdoctoral researcher at Gramazio Kohler Research (ETH Zurich) and works within the Design++ initiative (Centre for Augmented Computational Design in AEC). Mitterberger was the Co-lead of the Immersive Design Lab, a lab for collaborative research and teaching in the field of extended reality and machine learning in architecture and construction. Mitterberger is co-founder and director of «MAEID [Büro für Architektur und transmediale Kunst]», a multidisciplinary architecture practice based in Innsbruck. She was also a researcher at the University of Applied Arts and co-leader of the FWF PEEK project titled “Co-corporeality”. Previously she was a lecturer at several international graduate and postgraduate programs, among others at the MSD Melbourne (Australia), UniSA University of Adelaide (Australia), University of Applied Arts Vienna (Austria), Academy of Fine Arts Vienna (Austria), University of Innsbruck (Austria), ETH Zurich (Switzerland), Tongji University (China), and IACC in Barcelona (Spain). Her work has been recognized with several awards and has been widely exhibited at various international galleries, institutions, and events, including Venice Biennale 2021, Princess of Asturia Awards 2021, Seoul Biennale 2020, Ars Electronica Linz, MAK Vienna, Melbourne Triennial, Academy of Fine Arts Vienna, and HdA Graz.
Professor Sarah Kenderdine
Computational Museology: Art & Science in the Age of Experience
Abstract: Computational museology is a scaffold that unites machine intelligence with data curation, ontology with visualization, and communities of publics and practitioners with embodied participation through kinaesthetic interfaces. Computational museology empowers cultural organisations to link all forms of culture and materiality: objects, knowledge systems, representation and participation. Research at the Laboratory for Experimental Museology (eM+) reaches beyond object-oriented curation to blend experimental curatorship with contemporary aesthetics, digital humanism and emerging technologies. This lecture explores key themes including interactive archives and emergent narrative, deep fakes and blockchain sovereignties, embodied knowledge systems and performative interfaces and scientific visualization for museums in the age of experience. She will also give an overview of EPFL Pavilions exhibitions and focus the discussion on Deep Fakes: Art and Its Double.
Bio: Professor Sarah Kenderdine researches at the forefront of interactive and immersive experiences for galleries, libraries, archives and museums. In widely exhibited installation works, she has amalgamated tangible and intangible cultural heritage with new media art practice, especially in the realms of interactive cinema, augmented reality and embodied narrative. Sarah has produced over 100 exhibitions and installations for museums worldwide. In 2017, Sarah was appointed professor at the École Polytechnique fédérale de Lausanne (EPFL), Switzerland where she has built the Laboratory for Experimental Museology (eM+). Sarah is also director and lead curator of EPFL Pavilions, a new art/science initiative. In 2021, Sarah was appointed corresponding fellow of The British Academy. Her upcoming book is Deep Fakes: A Critical Lexicon of Digital Museology (2024).
Professor Bernd Bickel
Rethinking Design and Fabrication with Computational and Data-Driven Method
Bio: Bernd Bickel is a Professor for Computational Design at ETH Zurich. He is a computer scientist interested in computer graphics and its overlap into robotics, computer vision, machine learning, material science, and digital fabrication. His main objective is to push the boundaries of how digital content can be efficiently created, simulated, and reproduced.
Bernd obtained his Master's degree in Computer Science from ETH Zurich in 2006. For his PhD studies, Bernd joined the group of Markus Gross who is a professor of Computer Science at ETH Zurich and the director of Disney Research Zurich. From 2011-2012, Bernd was a visiting professor at TU Berlin, and in 2012 he became a research scientist and research group leader at Disney Research. From 2015-2023, Bernd was a professor at IST Austria.
Bernd's work focuses on three closely related challenges: developing (1) novel modeling and simulation methodologies, (2) efficient representation and design algorithms for materials, 3D geometry, and functional objects, and (3) custom measurement systems and data-driven techniques for bridging the gap between the real and virtual world. Recent work includes: theoretical foundations and practical algorithms for measuring and modeling the deformation behavior of soft tissue; simulating and reproducing fundamental properties, such as elasticity, surface reflectance, and subsurface scattering; and computational design systems for efficiently creating functional artifacts such as soft robots, programmable materials, and mechanical systems.