153 citations · 188 across the 19 of their papers we have counts for
19 papers
Theoretical Limits of Energy Extraction in Active Fluids
Shahriar Shadkhoo, Matt Thomson
Active materials form a class of far-from-equilibrium systems that are driven internally and exhibit self-organization which can be harnessed to perform mechanical work. Inspired b…
Spatiotemporal patterning of extensile active stresses in microtubule-based active fluids
Linnea M. Lemma, Minu Varghese, Tyler D. Ross +3
Active stresses, which are collectively generated by the motion of energy-consuming rod-like constituents, generate chaotic autonomous flows. Controlling active stresses in space a…
Dynamic Flow Control Through Active Matter Programming Language
Fan Yang, Shichen Liu, Heun Jin Lee +2
Cells control fluid flows with a spatial and temporal precision that far exceeds the capabilities of current microfluidic technologies. Cells achieve this superior spatio-temporal…
Engineering flexible machine learning systems by traversing functionally-invariant paths
Guruprasad Raghavan, Bahey Tharwat, Surya Narayanan Hari +2
Transformers have emerged as the state of the art neural network architecture for natural language processing and computer vision. In the foundation model paradigm, large transform…
Cell density controls signal propagation waves in a multicellular synthetic gene circuit
Marco Santorelli, Pranav Bhamidipati, Andriu Kavanagh +4
During organismal development, biochemical reaction networks sense and respond to mechanical forces to coordinate embryonic patterning with embryo morphogenesis. Factors such as co…
Signaling receptor localization maximizes cellular information acquisition in spatially-structured, natural environments
Zitong Jerry Wang, Matt Thomson
Cells in natural environments like tissue or soil sense and respond to extracellular ligands with intricately structured and non-monotonic spatial distributions that are sculpted b…
Solving hybrid machine learning tasks by traversing weight space geodesics
Guruprasad Raghavan, Matt Thomson
Machine learning problems have an intrinsic geometric structure as central objects including a neural network's weight space and the loss function associated with a particular task…
Reinforcement Learning reveals fundamental limits on the mixing of active particles
Dominik Schildknecht, Anastasia N. Popova, Jack Stellwagen +1
The control of far-from-equilibrium physical systems, including active materials, has emerged as an important area for the application of reinforcement learning (RL) strategies to…
Programming Boundary Deformation Patterns in Active Networks
Zijie Qu, Jialong Jiang, Heun Jin Lee +3
Active materials take advantage of their internal sources of energy to self-organize in an automated manner. This feature provides a novel opportunity to design micron-scale machin…
Sparsifying networks by traversing Geodesics
Guruprasad Raghavan, Matt Thomson
The geometry of weight spaces and functional manifolds of neural networks play an important role towards 'understanding' the intricacies of ML. In this paper, we attempt to solve c…
Persistent fluid flows defined by active matter boundaries
Zijie Qu, Dominik Schildknecht, Shahriar Shadkhoo +5
Biological systems achieve precise control over ambient fluids through the self-organization of active protein structures including flagella, cilia, and cytoskeletal networks. In a…
Phenomenological model of motility by spatiotemporal modulation of active interactions
Dominik Schildknecht, Matt Thomson
Transport at microscopic length scales is essential in biological systems and various technologies, including microfluidics. Recent experiments achieved self-organized transport ph…
Self-organization of multi-layer spiking neural networks
Guruprasad Raghavan, Cong Lin, Matt Thomson
Living neural networks in our brains autonomously self-organize into large, complex architectures during early development to result in an organized and functional organic computat…
Geometric algorithms for predicting resilience and recovering damage in neural networks
Guruprasad Raghavan, Jiayi Li, Matt Thomson
Biological neural networks have evolved to maintain performance despite significant circuit damage. To survive damage, biological network architectures have both intrinsic resilien…
Neural networks grown and self-organized by noise
Guruprasad Raghavan, Matt Thomson
Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational dev…
Active Learning of Spin Network Models
Jialong Jiang, David A. Sivak, Matt Thomson
The inverse statistical problem of finding direct interactions in complex networks is difficult. In the natural sciences, well-controlled perturbation experiments are widely used t…
Controlling Organization and Forces in Active Matter Through Optically-Defined Boundaries
Tyler D. Ross, Heun Jin Lee, Zijie Qu +3
Living systems are capable of locomotion, reconfiguration, and replication. To perform these tasks, cells spatiotemporally coordinate the interactions of force-generating, "active"…
Physically optimizing inference
Audrey Huang, Benjamin Sheldan, David A. Sivak +1
Data is scaling exponentially in fields ranging from genomics to neuroscience to economics. A central question is: can modern machine learning methods be applied to construct predi…
Environmental statistics and optimal regulation
David A. Sivak, Matt Thomson
Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its en…