About

Professor George Barbastathis is the Ralph E. and Eloise F. Cross Professor in Manufacturing and a Professor of Mechanical Engineering at MIT. His research interests include optical imaging, holography, statistical optics, compressive imaging, artificial dielectrics, nonlinear Hamiltonian optics, and micro and nanoengineering. He is known for his work in quantitative phase imaging in the visible and x-ray bands, correlation functions and sparse representations, and GRadient-INdex (GRIN) optics. Professor Barbastathis holds a B.Eng. from the National Technical University of Athens (1993), an M.Sc. from Caltech (1994), and a Ph.D. from Caltech (1998). His professional experience includes postdoctoral research at the University of Illinois Urbana-Champaign, visiting scholar positions at Harvard University, and multiple research scientist roles at the Singapore-MIT Alliance for Research and Technology (SMART) Centre. He has also served as a visiting professor at the University of Michigan - Shanghai Jiao Tong University Joint Institute. His notable contributions include developing machine learning algorithms to model the impact of quarantine measures on Covid-19’s spread, and creating deep-learning techniques to recognize transparent objects in low-light conditions, which have applications in biological tissue imaging. He has been recognized as a Fellow of the Optical Society of America and SPIE, and has received awards such as the China One Thousand Scholar Award and the Ruth and Joel Spira Award. Professor Barbastathis is actively involved in professional service, conference organization, and teaching at MIT.

Research topics

• Artificial Intelligence
• Computer Science
• Optics
• Physics
• Political Science
• Machine Learning
• Computer vision
• Biology
• Law
• Environmental health
• Business
• Medicine
• Algorithm
• Virology

Selected publications

• Interpretable Deep Learning for Single-Molecule Nanopore Fingerprinting Using Physics-Guided Preprocessing

  ACS Sensors · 2026-02-20

  articleCorresponding

  Rapid and robust molecular fingerprinting is critical in biomanufacturing, diagnostics, and environmental monitoring. Nanopore sensing provides single-molecule readouts as transient ionic current pulses; however, conventional analyses depend on handcrafted features that miss informative structural information. We present an interpretable machine learning framework that operates directly on raw pulses, pairing a physics-guided time-frequency transform with a compact neural classifier and feature-attribution maps. We also include conventional feature-based SVMs and a 1D classifier trained on raw pulses as baselines. On two self-assembled DNA nanostructures of similar size but distinct geometry, for which standard pulse features overlap, the method achieves high accuracy and yields physically consistent attributions that highlight discriminative signal motifs. A matched control without the time-frequency transform clarifies when learned filters suffice versus when physics-guided preprocessing improves reliability, leading to a practical "custom-filter" design principle. The workflow is modular, lightweight, and applicable to pulse-based sensing platforms, including virus and exosome analysis, electrochemical monitoring, and industrial fault detection. By combining accuracy with transparency, it lays the groundwork for deployable sensing platforms in regulated, mission-critical settings.

  PublisherDOI

• AI to Identify Strain-Sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma

  Investigative Ophthalmology & Visual Science · 2026-02-11

  articleOpen access

  Purpose: The purposes of this study were to assess whether optic nerve head (ONH) biomechanics, quantified by tissue strain, improves classification of progressive visual field (VF) loss patterns in glaucoma beyond morphology, and to use saliency maps to identify ONH regions associated with the predictions. Methods: We recruited 249 patients with glaucoma (mean age 69 ± 5 years, 54% female patients). One eye per subject was imaged under (1) primary gaze and (2) primary gaze with IOP elevated to approximately 35 millimeters of mercury (mm Hg) via ophthalmo-dynamometry. Twelve subjects were excluded due to poor scan quality/limited lamina cribrosa (LC) visibility. Experts classified subjects into four categories based on the presence of specific visual field defects (VFDs): (1) superior nasal step (N = 26), (2) superior partial arcuate (N = 62), (3) full superior hemifield defect (N = 25), and (4) other/non-specific defects (N = 124). Automatic segmentation and digital volume correlation computed neural tissue and LC strains. Biomechanical and structural features were input to a PointNet model. Three classification tasks were performed to detect: (1) superior nasal step, (2) superior partial arcuate, and (3) full superior hemifield defect. Data were split 80/20 (train/test). Area under the curve (AUC) assessed performance. Saliency maps (an explainable artificial intelligence [XAI] technique) highlighted ONH regions most critical to classification. Results: Models achieved AUCs of 0.77 to 0.88 across VFD classifications. The structure-only model reached an AUC of 0.83 ± 0.02 for superior arcuate defects, which significantly improved to 0.87 ± 0.02 (P < 0.05) with the addition of strain information, demonstrating that ONH biomechanics enhance prediction beyond morphology. Strain-sensitive regions were localized to the inferior and inferotemporal rim, expanding with increasing severity of VF loss. Conclusions: ONH strain enhances classification of glaucomatous VF loss patterns. The neuroretinal rim, rather than the LC, was most critical, suggesting rim strain may play a dominant role in axonal injury and functional loss.

  PublisherOA PDFDOI

• Neural-PDE modeling of reaction-diffusion using time-series imaging for sub-diffraction-limit 3D lithography

  2025-08-08

  articleSenior author

  Two-color projection micro-stereolithography (PμSL) is an advanced additive manufacturing technique that enables rapid and continuous 3D printing. This method uses two distinct wavelengths of light to independently control photoinitiation and photoinhibition of polymerization in a photocurable resin, achieved by selecting a photoinitiator and photoinhibitor with complementary absorption spectra, enabling high-precision, continuous printing compatible with viscous resins. PμSL has significant potential in applications such as the fabrication of optical diffractive neural networks (D2NNs), which require sophisticated three-dimensional photonic structures capable of optical computation. However, achieving high-precision 3D printing with PμSL remains challenging due to limitations in current inspection and modeling techniques. The photochemical processes involved in photopolymerization are highly complex, with unknown local interactions among different species and non-local diffusion effects arising from spatial concentration differences in the reaction region. Accurate modeling of this lithography process is necessary for inverse design to fabricate precise sub-diffraction-limit polymer features. In this study, we develop a dynamic model for the polymerization process during 3D printing. We first establish a local generalized Lotka-Volterra system with parameters estimated from Fourier Transform Infrared Spectroscopy (FT-IR) measurements. We then extend this system to a reaction-diffusion model with unknown diffusion coefficients. To address residuals not captured by the symbolic models, we incorporate a neural network as a universal function approximator. Simulation results align closely with imaging measurements, and further analysis demonstrates improved generalization when additional physical priors are applied to the diffusion coefficients.

  PublisherDOI

• Simulation-based approach for fast optimal control of a Stefan problem with application to cell therapy

  Automatica · 2025-06-07

  article
  PublisherDOI

• Extracting the proportion of particles in a mixture through speckle polarization information

  2025-06-18

  articleSenior author
  PublisherDOI

• Speckle-based particle size distribution estimation for pharmaceutical powders

  2025-06-23

  articleSenior author
  PublisherDOI

• Revealing the Dynamics and Kinetics of Copper Pulse Reversal Electrodeposition with Multimodal Synchrotron X-Ray Nanoimaging

  ECS Meeting Abstracts · 2025-11-24

  article

  Understanding nanoscale kinetic processes during electrochemical deposition is critical for advancing a range of technologies such as heterogeneous catalysis, microelectronics manufacturing, energy storage systems and precision fabrication of functional materials. Copper pulse-reversal electrodeposition (Cu PR-ED) serves as an ideal model system to probe these dynamics, where control over deposition morphology through parameters like current density and pulse timing remains empirically established but mechanistically unresolved. This study employs operando synchrotron X-ray nanoimaging combined with multiscale characterization to resolve interfacial processes at previously inaccessible sub-10nm and millisecond spatiotemporal resolutions. A key innovation lies in overcoming the inherent trade-off between imaging fidelity and beam-sample interactions for radiation sensitive materials: our methodology, which uses computational and machine learning-based denoising and background correction, enables the analysis of images acquired under reduced X-ray doses. This approach allows for observation of intrinsic deposition phenomena while minimizing radiation artifacts under operando conditions. Comparative growth analysis under different attenuated radiation levels reveals distinct kinetic regimes governed by interfacial transport conditions, with implications for nucleation and growth mechanisms across electrochemical systems. Beyond advancing Cu PR-ED control, this work establishes a general framework for studying dynamic processes in beam-sensitive materials, directly addressing fundamental limitations identified in radiation-based nanoscale imaging.

  PublisherDOI

• Dynamical system regularized object positioning from diffraction movie

  2025-08-08

  articleSenior author

  We present a coupled nonlinear optimization framework that combines a physics-based dynamical model K with an optical propagation model H to perform dynamic low-dose characterization directly from time-resolved diffraction data. By embedding the equations of motion within the optical forward operator, we allow for mutual regularization between imaging and dynamics. The method converts the inverse imaging problem into a parameter-estimation task, thereby avoiding frame-by-frame phase retrieval and suppressing ill-conditionedness arising from noisy data. The approach is validated on synchrotron X-ray movies of a thermally actuated micro-electro-mechanical (MEMS) oscillator. At a photon dose of ≈ 4 photons per pixel per frame, it simultaneously reconstructs the shuttle edge profile, partially coherent probe modes, and their temporal occupancies. With an incident flux of ≈ 0.015 photons per pixel per frame, the framework still recovers the MEMS shuttle displacement trajectory. Compared with conventional two-step pipelines, the joint treatment yields improved noise robustness by exploiting temporal correlations and enforcing physically admissible motion.

  PublisherDOI

• 3460060.pdf

  Open MIND · 2025-01-01

  articleSenior author

  Supplemental material

• Probabilistically Robust Uncertainty Analysis and Optimal Control of Continuous Lyophilization via Polynomial Chaos Theory

  arXiv (Cornell University) · 2025-02-19

  preprintOpen access

  Lyophilization, aka freeze drying, is a process commonly used to increase the stability of various drug products in biotherapeutics manufacturing, e.g., mRNA vaccines, allowing for higher storage temperature. While the current trends in the industry are moving towards continuous manufacturing, the majority of industrial lyophilization processes are still being operated in a batch mode. This article presents a framework that accounts for the probabilistic uncertainty during the primary and secondary drying steps in continuous lyophilization. The probabilistic uncertainty is incorporated into the mechanistic model via polynomial chaos theory (PCT). The resulting PCT-based model is able to accurately and efficiently quantify the effects of uncertainty on several critical process variables, including the temperature, sublimation front, and concentration of bound water. The integration of the PCT-based model into stochastic optimization and control is demonstrated. The proposed framework and case studies can be used to guide the design and control of continuous lyophilization while accounting for probabilistic uncertainty.

  PublisherOA PDFDOI

Recent grants

• CAREER: 3D Optical Engineering

  NSF · $225k · 2000–2004

Frequent coauthors

• Lei Tian

  Boston University

  78 shared

• Yuan Luo

  National Taiwan University

  67 shared

• Se Baek Oh

  KLA (United States)

  46 shared

• Zhengyun Zhang

  Kunming University of Science and Technology

  45 shared

• Laura Waller

  University of California, Berkeley

  38 shared

• Iksung Kang

  36 shared

• Justin Lee

  California Institute of Technology

  35 shared

• Jennifer K. Barton

  35 shared

Labs

• MIT Department of Mechanical EngineeringPI

Awards & honors

• Fellow of the Optical Society of America (OSA) (2011)
• China One Thousand Scholar Award (2015)
• Ruth and Joel Spira Award (2022)
• SPIE Fellow (2022)