George Barbastathis

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…

Research topics

• Artificial Intelligence
• Computer Science
• Optics
• Physics
• Political Science
• Machine Learning
• Computer vision
• Biology
• Law
• Environmental health

Selected publications

• Phase imaging with an untrained neural network

  Light Science & Applications · 2020 · 468 citations

  Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy, and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition, in many practical applications, it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here, we propose to overcome this limitation by incorporating into…

  DOI

• Single-shot lensless imaging with fresnel zone aperture and incoherent illumination

  Light Science & Applications · 2020 · 177 citations

  Senior authorCorresponding

  Lensless imaging eliminates the need for geometric isomorphism between a scene and an image while allowing the construction of compact, lightweight imaging systems. However, a challenging inverse problem remains due to the low reconstructed signal-to-noise ratio. Current implementations require multiple masks or multiple shots to denoise the reconstruction. We propose single-shot lensless imaging with a Fresnel zone aperture and incoherent illumination. By using the Fresnel zone aperture to enco…

  DOI

• Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning

  medRxiv (Cold Spring Harbor Laboratory) · 2020 · 114 citations

  Senior authorCorresponding

  Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019 (CHP 2020), the disease has spread worldwide and met with a wide variety of social distancing and quarantine policies. The effectiveness of these responses is notoriously difficult to quantify as individuals travel, violate policies deliberately or inadvertently, and infect others without themselves being detected (Li et al . 2020 a ; Wu & Leung 2020; Wang et al . 2020; Chinazzi e…

  DOI

• Impinging jet mixers: A review of their mixing characteristics, performance considerations, and applications

  AIChE Journal · 2024-09-04 · 35 citations

  reviewOpen access

  Abstract Optimal control over fast chemical processes hinges on the achievement of rapid and effective mixing. Impinging jet mixers are a unique class of passive mixing devices renowned for their exceptional ability to achieve rapid mixing at micro‐length scales, whilst offering the possibility of a high throughput. Comprising of two co‐linear jets flowing in opposite directions and colliding with each other within a small (usually confined) volume, these devices effectively intensify various mi…

  PublisherOA PDFDOI

• Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion

  Nature Communications · 2025-09-01 · 25 citations

  articleOpen access

  The surge in artificial intelligence applications calls for scalable, high-speed, and low-energy computation methods. Computing with photons is promising due to the intrinsic parallelism, high bandwidth, and low latency of photons. However, current photonic computing architectures are limited by the speed and energy consumption associated with electronic-to-optical data transfer, i.e., electro-optic conversion. Here, we demonstrate a thin-film lithium niobate (TFLN) computing circuit that addres…

  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)

Similar researchers at Massachusetts Institute of Technology

• Gang Chenh-160
• Tonio Buonassisih-81
• Lallit Anandh-66