Atoms. Geometry. Algorithms.

Catalyst Conversations and List Visual Arts Center

Tess Smidt and Erik Demaine share a relationship to both science and art. They will address the many interests that they each pursue.

MIT List Visual Arts Center

In the early 1920’s Danish Physicist Niels Bohr was struggling to reimagine the structure of matter. …he realized that science needed new metaphor. ...As Bohr said, “When it comes to atoms, language can be used only as in poetry.” Ordinary words couldn’t capture the data.”Tess Smidt and Erik Demaine share a relationship to both science and art. They will address the many interests that they each pursue in their respective practices, among them the parallels and importance of iteration and ways they each endeavor to capture data. Their passion for what they do is guided by their openness to working across the (sometime) borders between science and art. The research group Smidt heads, The Atomic Architects, works at the intersection of physics, geometry, and machine learning to design algorithms that aid in the understanding and design of physical systems. As an undergraduate she designed the Cosmic Ray Chandeliers, a science-art installation located on M.I.T.’s campus on the 5th floor between buildings 16 and 26. The Chandeliers illuminate upon detecting particles, called muons, created in cosmic ray showers. As in other projects she demonstrates her deep knowledge and adventurness.Demaine’s interest in and work with Origami along with Martin Demaine is well known. He says “My favorite type of results are universality results which prove that, in a certain model of folding, everything is possible. Typically, they come with an efficient algorithm for finding the folding; pure existential proofs are rare. If we cannot hope to fold everything, the next best thing is to have an efficient (polynomial) algorithm, both for detecting whether an object is foldable, and if so, for finding an efficient folding.”_﻿__<a href="https://blondegeek.github.io/?mc_cid=81eb290bcd&mc_eid=UNIQID" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Tess Smidt</a> is an Assistant Professor of Electrical Engineering and Computer Science at MIT. Tess earned her SB in Physics from MIT in 2012 and her PhD in Physics from the University of California, Berkeley in 2018. Her research focuses on machine learning that incorporates physical and geometric constraints, with applications to materials design. Prior to joining the MIT EECS faculty, she was the 2018 Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory and a Software Engineering Intern on the Google Accelerated Sciences team where she developed Euclidean symmetry equivariant neural networks which naturally handle 3D geometry and geometric tensor data.<a href="http://erikdemaine.org/?mc_cid=81eb290bcd&mc_eid=UNIQID" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Erik Demaine</a> is a Professor in Computer Science at MIT. His research interests range throughout algorithms, from data structures for improving web searches to the geometry of understanding how proteins fold to the computational difficulty of playing games. He received a MacArthur Fellowship as a “computational geometer tackling and solving difficult problems related to folding and bending—moving readily between the theoretical and the playful, with a keen eye to revealing the former in the latter”. He appears in the origami documentaries Between the Folds and NOVA's The Origami Revolution. He cowrote a book about the theory of folding (Geometric Folding Algorithms), and a book about the computational complexity of games (Games, Puzzles, and Computation). His interests span the connections between mathematics and art, including curved-crease sculptures in the permanent collections of the Museum of Modern Art in New York, and the Renwick Gallery in the Smithsonian.___Top Image: The output of three randomly initialized symmetry-preserving neural networks when given a tetrahedron geometry (top) and octahedron geometry (bottom) as input. The outputs have equal or higher symmetry than the inputs."Tess Smidt, Euclidean Symmetry and Equivariance in Machine Learning, Trends in Chemistry Volume 3, Issue 2, February 2021, Pages 82-85.Bottom Image: “Eddy” by Erik Demaine &amp; Martin Demaine, 2012. Mi-Teintes watercolor paper,10" × 9" × 12"Quote: excerpted from Jonah Lehrer article The Future of Science is Art? SeedMagazine.com, 2008

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Atoms. Geometry. Algorithms.

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