About

Yoel Fink is the Danae and Vasilis (1961) Salapatas Professor of Materials Science and Engineering at MIT. His research interests are in the theory, design, fabrication, and characterization of multimaterial, multifunctional fibers and fiber assemblies. His group focuses on extending fiber materials from optical transmission to include electronic, optoelectronic, and acoustic properties, utilizing the combination of disparate materials arranged in elaborate geometries with features down to 10 nanometers. Fink employs two approaches: integrating multiple functional components into a single fiber and assembling large-scale fiber arrays and fabrics. His multimaterial fibers offer unprecedented control over material properties and functions across length scales from nanometers to kilometers. Fink earned a BS in chemical engineering and a BA in physics from the Technion-Israel Institute of Technology, followed by a PhD in materials science at MIT. He joined the MIT faculty in 2000 and became a joint professor of Electrical Engineering and Computer Science in 2011. He is a co-founder of OmniGuide, served as its CEO from 2007 to 2010, and was a member of its board until 2014. Fink has authored over 80 scientific journal articles and holds 60 US patents on photonic fibers and devices. His notable contributions include developing a rechargeable lithium-ion battery fiber that is self-contained, ultra-long, and washable, and creating multifunctional fibers capable of delivering light and drugs for in vivo photopharmacology, enabling targeted therapy with minimal invasiveness.

Research topics

• Computer Science
• Nanotechnology
• Materials science
• Acoustics
• Physics
• Artificial Intelligence
• Composite material
• Chemistry
• Computer hardware
• Engineering
• Electrical engineering
• Biology
• Neuroscience

Selected publications

• Single-Photon Sensitive Optoelectronic Fibres for Distributed Nuclear Radiation Detection in Textile Fabrics

  ArXiv.org · 2026-04-06

  articleOpen accessSenior author

  Nuclear radiation detectors play a key role in applications spanning nuclear and particle physics, nuclear engineering, security, and medicine. With the expanded global interest in nuclear power, discreet, inconspicuous, and readily deployable nuclear detection capabilities are increasingly important. However, conventional dosimeters are often rigid, bulky, or lack spatial resolution, limiting their use for mobile, conformal, or large-area distributed mapping of dynamic fields. Here, we present flexible, radiation-sensitive optoelectronic fibres with up to 50% elasticity for real-time gamma dosimetry. Silicon photomultipliers are thermally drawn into the core of fibres composed of a scintillator waveguide, enabling electronic-photonic integration and detection of scintillation light with single-photon resolution. We show that these fibres are sensitive to localized nuclear radiation exposure from collimated 0.5 μCi Sr-90 β-sources and 10 μCi Cs-137 and Co-60 γ-sources, with extended responsivity measured over 30 cm, and estimated lower detection limits approaching near- background radiation levels (~14-41 nSv/hr). Co-locating the scintillator and detectors in the fibre eliminates past length limitations driven by optical losses and enabling a greater collection cone through capture of transient non- guided modes. We further enhance radiation sensitivity and mechanical robustness by covering the fibres with a tungsten-merino wool composite braid, enabling us to machine-weave them into fabrics alongside common textile yarns. The tungsten wires function as a gamma-electron converter, increasing the detection efficiency of the assembly by ~20%. Distributed woven arrays of fibres formed in this way present an opportunity to create large-area, conformal fabrics capable of real- time dosimetry of gamma radiation fields with high spatial resolution.

  PublisherOA PDF

• Single-Photon Sensitive Optoelectronic Fibres for Distributed Nuclear Radiation Detection in Textile Fabrics

  arXiv (Cornell University) · 2026-04-06

  preprintOpen accessSenior author

  Nuclear radiation detectors play a key role in applications spanning nuclear and particle physics, nuclear engineering, security, and medicine. With the expanded global interest in nuclear power, discreet, inconspicuous, and readily deployable nuclear detection capabilities are increasingly important. However, conventional dosimeters are often rigid, bulky, or lack spatial resolution, limiting their use for mobile, conformal, or large-area distributed mapping of dynamic fields. Here, we present flexible, radiation-sensitive optoelectronic fibres with up to 50% elasticity for real-time gamma dosimetry. Silicon photomultipliers are thermally drawn into the core of fibres composed of a scintillator waveguide, enabling electronic-photonic integration and detection of scintillation light with single-photon resolution. We show that these fibres are sensitive to localized nuclear radiation exposure from collimated 0.5 μCi Sr-90 β-sources and 10 μCi Cs-137 and Co-60 γ-sources, with extended responsivity measured over 30 cm, and estimated lower detection limits approaching near- background radiation levels (~14-41 nSv/hr). Co-locating the scintillator and detectors in the fibre eliminates past length limitations driven by optical losses and enabling a greater collection cone through capture of transient non- guided modes. We further enhance radiation sensitivity and mechanical robustness by covering the fibres with a tungsten-merino wool composite braid, enabling us to machine-weave them into fabrics alongside common textile yarns. The tungsten wires function as a gamma-electron converter, increasing the detection efficiency of the assembly by ~20%. Distributed woven arrays of fibres formed in this way present an opportunity to create large-area, conformal fabrics capable of real- time dosimetry of gamma radiation fields with high spatial resolution.

  PublisherDOI

• A single-fibre computer enables textile networks and distributed inference

  Nature · 2025-02-26 · 41 citations

  articleSenior author
  PublisherDOI

• Accelerating Digit Classification on FPGA with Pruned Binarized Neural Networks

  Optimizations in Applied Machine Learning · 2024-04-10

  articleOpen access

  As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and <9ns latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.

  PublisherOA PDFDOI

• Single Layer Silk and Cotton Woven Fabrics for Acoustic Emission and Active Sound Suppression (Adv. Mater. 28/2024)

  Advanced Materials · 2024-07-01

  articleOpen accessSenior author

  Blocking Sound with Silk In article number 2313328 by Yoel Fink and co-workers, sound control using traditional fabrics is explored. A silk fabric with a piezoelectric fiber emits 70 dB of sound. Surprisingly, by dynamically suppressing vibrations in a 130-micron silk monolayer, transmitted sound decreases by 75% and acoustic reflectivity increases by 68%. These findings present opportunities in apparel, transportation, and architecture.

  PublisherOA PDFDOI

• Acoustic and Vibrational Properties of the American Horseshoe Crab Shell

  Research Square · 2024-11-26

  preprintOpen access
  PublisherOA PDFDOI

• Single Layer Silk and Cotton Woven Fabrics for Acoustic Emission and Active Sound Suppression

  Advanced Materials · 2024-04-01 · 12 citations

  articleOpen accessSenior authorCorresponding

  Whether intentionally generating acoustic waves or attempting to mitigate unwanted noise, sound control is an area of challenge and opportunity. This study investigates traditional fabrics as emitters and suppressors of sound. When attached to a single strand of a piezoelectric fiber actuator, a silk fabric emits up to 70 dB of sound. Despite the complex fabric structure, vibrometer measurements reveal behavior reminiscent of a classical thin plate. Fabric pore size relative to the viscous boundary layer thickness is found-through comparative fabric analysis-to influence acoustic-emission efficiency. Sound suppression is demonstrated using two distinct mechanisms. In the first, direct acoustic interference is shown to reduce sound by up to 37 dB. The second relies on pacifying the fabric vibrations by the piezoelectric fiber, reducing the amplitude of vibration waves by 95% and attenuating the transmitted sound by up to 75%. Interestingly, this vibration-mediated suppression in principle reduces sound in an unlimited volume. It also allows the acoustic reflectivity of the fabric to be dynamically controlled, increasing by up to 68%. The sound emission and suppression efficiency of a 130 µm silk fabric presents opportunities for sound control in a variety of applications ranging from apparel to transportation to architecture.

  PublisherOA PDFDOI

• Multifunctional microelectronic fibers enable wireless modulation of gut and brain neural circuits

  Nature Biotechnology · 2023 · 134 citations

  • Computer Science
  • Neuroscience
  • Computer Science

  Progress in understanding brain-viscera interoceptive signaling is hindered by a dearth of implantable devices suitable for probing both brain and peripheral organ neurophysiology during behavior. Here we describe multifunctional neural interfaces that combine the scalability and mechanical versatility of thermally drawn polymer-based fibers with the sophistication of microelectronic chips for organs as diverse as the brain and the gut. Our approach uses meters-long continuous fibers that can integrate light sources, electrodes, thermal sensors and microfluidic channels in a miniature footprint. Paired with custom-fabricated control modules, the fibers wirelessly deliver light for optogenetics and transfer data for physiological recording. We validate this technology by modulating the mesolimbic reward pathway in the mouse brain. We then apply the fibers in the anatomically challenging intestinal lumen and demonstrate wireless control of sensory epithelial cells that guide feeding behaviors. Finally, we show that optogenetic stimulation of vagal afferents from the intestinal lumen is sufficient to evoke a reward phenotype in untethered mice.

  PublisherOA PDFDOI

• Pruning Binarized Neural Networks Enables Low-Latency, Low-Power FPGA-Based Handwritten Digit Classification

  2023-09-25

  article

  As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt; 9\text{ns}$</tex> latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.

  PublisherDOI

• Magnetically Actuated Fiber‐Based Soft Robots

  Advanced Materials · 2023-06-03 · 109 citations

  articleOpen access

  Broad adoption of magnetic soft robotics is hampered by the sophisticated field paradigms for their manipulation and the complexities in controlling multiple devices. Furthermore, high-throughput fabrication of such devices across spatial scales remains challenging. Here, advances in fiber-based actuators and magnetic elastomer composites are leveraged to create 3D magnetic soft robots controlled by unidirectional fields. Thermally drawn elastomeric fibers are instrumented with a magnetic composite synthesized to withstand strains exceeding 600%. A combination of strain and magnetization engineering in these fibers enables programming of 3D robots capable of crawling or walking in magnetic fields orthogonal to the plane of motion. Magnetic robots act as cargo carriers, and multiple robots can be controlled simultaneously and in opposing directions using a single stationary electromagnet. The scalable approach to fabrication and control of magnetic soft robots invites their future applications in constrained environments where complex fields cannot be readily deployed.

  PublisherOA PDFDOI

Recent grants

• Multifunctional fibers for high-throughput microfluidics

  NIH · $376k · 2017–2020

Frequent coauthors

• John D. Joannopoulos

  Institute for Soldier Nanotechnologies

  170 shared

• Alexander M. Stolyarov

  Materials Processing (United States)

  75 shared

• Fabien Sorin

  67 shared

• Polina Anikeeva

  Massachusetts Institute of Technology

  63 shared

• Ayman F. Abouraddy

  58 shared

• Ofer Shapira

  48 shared

• Chong Hou

  Huazhong University of Science and Technology

  44 shared

• Gabriel Loke

  Massachusetts Institute of Technology

  39 shared

Education

• Ph.D., Materials Science and Engineering

  Massachusetts Institute of Technology

  1992

• M.S., Materials Science and Engineering

  Massachusetts Institute of Technology

  1988

• B.S., Materials Science and Engineering

  Technion - Israel Institute of Technology

  1985

Awards & honors

• 2004 National Academy of Sciences Initiatives in Research Aw…
• 2006 Joseph Lane Award for Excellence in Teaching
• 2007 Margaret MacVicar Faculty Fellow, MIT