Positions
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Mary Amanda Wood Professor of Physics, University of Pennsylvania (2021-)
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Professor of Mech. Eng. & Appl. Mech. (secondary), University of Pennsylvania (2022-)
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Associate Director, Center for Soft and Living Matter, University of Pennsylvania (2021-)
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Professor of Physics, University of Pennsylvania (2004-2021)
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Professor of Physics (assistant to full), UCLA (1991-2004)
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Postdoc, Exxon Research & Engineering (1989-1991)
Honors
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Chair line, APS Topical Group on Statistical and Nonlinear Physics (2023-2026)
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Fellow, American Association for the Advancement of Science (2022)
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Chair line, APS Division of Soft Matter (2017-2021)
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Sigma Xi Distinguished Lecturer (2003-2005)
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Fellow, American Physical Society (2005)
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Member at Large, APS Topical Group on Statistical and Nonlinear Physics (2005-2008)
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UCLA Outstanding Teaching Award (1995)
Ph.D. Cornell (1989)
A.B. The University of Chicago (1984)
I’m an experimentalist working on the statistical and nonlinear physics of widely-varied forms of soft matter. Until recently my focus was on particulate systems composed of densely packed colloids, grains, or bubbles. But my primary focus is shifting to a new class of systems we call “Contrastive Local Learning Networks (CLLNs).” Like dense particulate systems, CLLNs feature unusual dynamics in a complex high-dimensional landscape. Our CLLNs are metamaterials composed of many copies of a specially-designed repeat unit, arrayed in highly connected network, which are capable of learning complex AI-like functionalities autonomously using local learning rules – without the need for external memory or digital processor—similar to the brain. In effect, these are neural networks in which both training and inference are done automatically and in-memory by analog physical processes. As such, they potentially offer a tremendous advantage in terms of speed and energy efficiency over artificial neural networks at large scales. They also pose a host of fascinating new fundamental research questions at the intersection of physics, data science, and neuroscience. We are looking for motivated graduate students and postdocs to join our highly-collaborative interdisciplinary research team – please see the following papers/press and contact me for further information.
Selected Publications
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S. Dillavou, M. Stern, A. J. Liu, and D. J. Durian, Demonstration of Decentralized, Physics-Driven Learning, Physical Review Applied 18, 014040 (2022). Press; press; press.
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S. Dillavou, B. D. Beyer, M. Stern, A. J. Liu, M. Z. Miskin, D. J. Durian, Machine Learning Without a Processor: Emergent Learning in a Nonlinear Analog Network, Proc. Nat. Acad. Sci. 121, e2319718121 (2024). Press; press; commentary.
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S. Dillavou, B. Beyer, M. Stern, M. Miskin, A. Liu, D. Durian, Nonlinear classification without a processor, NeurIPS Workshop on Machine Learning with New Compute Paradigms (2023)
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J. F. Wycoff, S. Dillavou, M. Stern, A. J. Liu, and D. J. Durian, Desynchronous Learning in a Physics-Driven Learning Network, J. Chem. Phys. 156, 144903 (2022); special issue on Memory Formation.
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M. Stern, S. Dillavou, M. Z. Miskin, D. J. Durian, A. J. Liu, Physical Learning Beyond the Quasistatic Limit, Physical Review Research 4, L022037 (2022).
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M. Stern, S. Dillavou, D. Jayaraman, D. J. Durian, A. J. Liu, Training physical learning systems for power-efficient solutions, APL Machine Learning 2, 016114 (2024).
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L. E. Altman, M. Stern, A. J. Liu, D. J. Durian, Experimental Demonstration of Coupled Learning in Elastic Networks, Physical Review Applied 22, 024053 (2024).
See Google Scholar for a full list of publications
Phys 016: Energy, Oil, and Global Warming
Phys 101: General Physics: Mechanics, Heat, Sound
Phys 102: General Physics: EM, Optics, Modern Physics
Phys 140: Principles of Physics I: Mechanics and Wave Motion
Phys 141: Principles of Phys II: Electromagnetism and Radiation
Phys 351: Analytical Mechanics
Phys 401: Thermodynamics and the Intro to Statistical Mechanics
Phys 421: Modern Optics