About

Jon Estrada is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan. His research focuses on experimental mechanics of soft and bio-materials, including material characterization through high-speed rheometry, magnetic resonance, and inverse techniques. His work also encompasses inertial cavitation dynamics, photoacoustic and laser microscopy, cell mechanics, and the development of image processing techniques such as digital image and volume correlation. He holds a Ph.D. and an S.M. in Solid Mechanics from Brown University, as well as a B.S. in Materials Science and Engineering from MIT. Estrada's research interests are centered on biomechanics and biosystems engineering, mechanics, and materials. He has been recognized with an NSF CAREER Award for developing customizable procedures to test mechanically graded soft materials, aiming to bring these materials closer to industry applications.

Research topics

• Materials science
• Engineering
• Computer Science
• Structural engineering
• Artificial Intelligence
• Data Mining
• Composite material
• Nanotechnology
• Mathematics
• Anatomy
• Optics
• Biological system
• Biology
• Medicine
• Physics

Selected publications

• Microbubble surface instabilities in a strain stiffening viscoelastic material

  ArXiv.org · 2026-01-07

  articleOpen access

  Understanding the dynamics of instabilities along fluid-solid interfaces is critical for the efficacy of focused ultrasound therapy tools (e.g., histotripsy) and microcavitation rheometry techniques. Non-uniform pressure fields generated by either ultrasound or a focused laser can cause non-spherical microcavitation bubbles. Previous perturbation amplitude evolution models in viscoelastic materials either assume pure radial deformation or have inconsistent kinematic fields between the fluid and solid contributions. We derive a kinematically-consistent theoretical model for the evolution of surface perturbations. The model captures the non-linear kinematics of a strain-stiffening viscoelastic material surrounding a non-spherical bubble. The model is validated for (i) small, approximately linear radial oscillations and (ii) large inertial oscillations using laser-induced microcavitation experiments in a soft hydrogel. For the former, the bubble is allowed to reach mechanical equilibrium, and then surface perturbations are excited using ultrasound forcing. For the latter, the microbubble forms small bubble surface perturbations at its maximum radius that grow during collapse. The model's dominant surface perturbation mode scales linearly with equilibrium radius and matches experiments. Similarly, the model's perturbation amplitude evolution sufficiently constrains the rheometry problem and is experimentally validated.

  PublisherOA PDF

• Hierarchical Bayesian constitutive model selection for high-strain-rate soft material characterization

  Soft Matter · 2026-01-01

  articleOpen access

  A hierarchical Bayesian framework is developed for the inertial microcavitation rheometry technique to probabilistically select constitutive models and estimate parameters of soft materials from bubble dynamics under ultra-high strain rates.

  PublisherOA PDFDOI

• Resilient Reconfiguration and Fault-Tolerant Cooperative Control of MAS under Permanent Link Failures within a Hybrid-Systems Framework

  2026-02-09

  article1st authorCorresponding

  This work addresses the problem of fault-tolerant cooperative control (FTCC) in multi-agent systems (MAS) subject to abrupt and randomly occurring link failures. The agent dynamics and consensus algorithm are reformulated as a closed-loop stability problem. A hybrid system framework is adopted to model both the occurrence of link failures and the subsequent network reconfiguration. A topology reconfiguration protocol is proposed to ensure resilient consensus despite these disruptions, eliminating the need for the link recoverability assumption common in intermittent communication scenarios. The effectiveness of the proposed strategy is validated through a 3D formation control problem and a trajectory-oriented structural inspection scenario, where stability during topology disconnection is guaranteed and global consensus is immediately re-established after network reconnection, demonstrating the protocol’s resilience.

  PublisherDOI

• Real-time drug release monitoring from acoustically responsive scaffolds

  Ultrasonics Sonochemistry · 2026-03-04 · 1 citations

  articleOpen access

  Acoustically responsive scaffolds (ARSs), composite hydrogels containing phase-shift droplets that are activated by ultrasound, enable on-demand drug delivery with spatiotemporal precision. Yet, real-time monitoring of drug release from ARSs remains limited. Here, we studied the dynamics of the ultrasound-based activation mechanism, acoustic droplet vaporization (ADV), in fibrin-based ARSs containing perfluorohexane droplets. We investigated how initial droplet concentration, acoustic pressure, and burst number affect droplet dynamics, bubble cloud evolution, acoustic emissions, and drug release efficiency. Optical imaging, at 5 million frames per second (Mfps) and 50 fps, revealed that ADV-induced bubble cloud morphologies were concentration dependent. At high concentrations and pressures, bubble clouds expanded significantly beyond the ultrasound focal region by up to 300%. ADV generated distinct low-frequency (LF) emissions that progressively decreased over repeated bursts by ≈ 30 dB, indicating a reduction in the number of vaporized droplets within the focal region. The burst number at which LF emissions plateaued (e.g., 51 bursts at 0.1% (v/v), 6.2 MPa) correlated with the burst number at which payload release reached its maximum value (57 bursts), demonstrating that LF emissions provide real-time, non-invasive feedback on ADV-mediated drug delivery. These results establish a direct correlation between LF emissions and ADV, and underscore their potential for real-time monitoring of drug release in ARSs. • Acoustic droplet vaporization (ADV) dynamics were concentration-dependent • ADV generated distinct low frequency (LF) acoustic emission signatures • A plateau in LF acoustic emissions indicated no further ADV events occurring • LF emission plateau correlated with payload release for real-time monitoring.

  PublisherDOI

• Variation‐Matching Sensitivity‐Based Virtual Fields for Hyperelastic Material Model Calibration

  Strain · 2026-02-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  ABSTRACT Accurate identification of nonlinear material parameters from three‐dimensional full‐field deformation data remains a challenge in experimental mechanics. The virtual fields method (VFM) provides a powerful, computationally efficient approach for material model calibration; however, its success depends critically on the choice of virtual fields and the informativeness of available kinematic data. In this work, we advance the state‐of‐the‐art discrete formulation of the sensitivity‐based virtual fields (SBVF) method by systematically developing and comparing alternative variational and analytical SBVFs within a strain‐invariant‐based modeling framework. A central contribution of this work is the implementation and assessment of variation‐based SBVFs (vSBVFs), formulated using directional Gâteaux derivatives, as well as virtual fields derived from analytical differentiation (aSBVFs) which provide explicit, model‐tailored virtual displacement fields for parameter identification. Using simulated noisy volumetric datasets, we demonstrate that vSBVFs and aSBVFs enable procedural, automated construction of optimal virtual fields for each material parameter, substantially enhancing the robustness and efficiency of calibration without the need for manual field selection or high temporal resolution in the data acquisition. We quantify data richness—the effective diversity of sampled kinematic states—showing that increased data richness via sample geometry and loading protocols leads to improved parameter identifiability. These findings establish a pathway for automated, noise‐robust material model calibration suitable for future deployment with experimental full‐field imaging of soft, complex materials.

  PublisherOA PDFDOI

• Microbubble surface instabilities in a strain stiffening viscoelastic material

  arXiv (Cornell University) · 2026-01-07

  preprintOpen access

  Understanding the dynamics of instabilities along fluid-solid interfaces is critical for the efficacy of focused ultrasound therapy tools (e.g., histotripsy) and microcavitation rheometry techniques. Non-uniform pressure fields generated by either ultrasound or a focused laser can cause non-spherical microcavitation bubbles. Previous perturbation amplitude evolution models in viscoelastic materials either assume pure radial deformation or have inconsistent kinematic fields between the fluid and solid contributions. We derive a kinematically-consistent theoretical model for the evolution of surface perturbations. The model captures the non-linear kinematics of a strain-stiffening viscoelastic material surrounding a non-spherical bubble. The model is validated for (i) small, approximately linear radial oscillations and (ii) large inertial oscillations using laser-induced microcavitation experiments in a soft hydrogel. For the former, the bubble is allowed to reach mechanical equilibrium, and then surface perturbations are excited using ultrasound forcing. For the latter, the microbubble forms small bubble surface perturbations at its maximum radius that grow during collapse. The model's dominant surface perturbation mode scales linearly with equilibrium radius and matches experiments. Similarly, the model's perturbation amplitude evolution sufficiently constrains the rheometry problem and is experimentally validated.

  PublisherDOI

• Microbubble surface instabilities in a strain stiffening viscoelastic material

  Extreme Mechanics Letters · 2026-04-22

  articleOpen access
  PublisherOA PDFDOI

• Microbubble surface instabilities in a strain stiffening viscoelastic material

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Experimental characterization of high-strain-rate viscoelastic and damage behavior in anisotropic soft materials using laser-induced inertial cavitation

  Research Square · 2026-02-11

  preprintOpen access
  PublisherOA PDFDOI

• Accelerating Bayesian Optimal Experimental Design via Local Radial Basis Functions: Application to Soft Material Characterization

  ArXiv.org · 2025-05-19

  preprintOpen access

  We develop a computational approach that significantly improves the efficiency of Bayesian optimal experimental design (BOED) using local radial basis functions (RBFs). The presented RBF--BOED method uses the intrinsic ability of RBFs to handle scattered parameter points, a property that aligns naturally with the probabilistic sampling inherent in Bayesian methods. By constructing accurate deterministic surrogates from local neighborhood information, the method enables high-order approximations with reduced computational overhead. As a result, computing the expected information gain (EIG) requires evaluating only a small uniformly sampled subset of prior parameter values, greatly reducing the number of expensive forward-model simulations needed. For demonstration, we apply RBF--BOED to optimize a laser-induced cavitation (LIC) experimental setup, where forward simulations follow from inertial microcavitation rheometry (IMR) and characterize the viscoelastic properties of hydrogels. Two experimental design scenarios, single- and multi-constitutive-model problems, are explored. Results show that EIG estimates can be obtained at just 8% of the full computational cost in a five-model problem within a two-dimensional design space. This advance offers a scalable path toward optimal experimental design in soft and biological materials.

  PublisherOA PDFDOI

Frequent coauthors

• Christian Franck

  University of Wisconsin–Madison

  59 shared

• Mark T. Scimone

  University of New Hampshire at Manchester

  34 shared

• Harry C. Cramer

  Dana-Farber Cancer Institute

  28 shared

• Eyal Bar-Kochba

  Johns Hopkins University Applied Physics Laboratory

  16 shared

• Selda Buyukozturk

  Brown University

  13 shared

• David L. Henann

  John Brown University

  11 shared

• Ellen M. Arruda

  University of Michigan–Ann Arbor

  11 shared

• Bachir A. Abeid

  University of Michigan–Ann Arbor

  11 shared

Education

• Ph.D., Engineering: Mechanics of Solids

  Brown University

  2017

• Sc.M., Engineering: Mechanics of Solids

  Brown University

  2013

• B.S., Materials Science and Engienering

  Massachusetts Institute of Technology

  2011

Awards & honors

• NSF CAREER Award (2024)