Douglas Durian

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

• 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. 

• 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. 

• 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)

• 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. 

• 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). 

• 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). 

• 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