About

Mark Foster is a professor of electrical and computer engineering at Johns Hopkins University. His research focuses on the development of practical optical systems for ultrahigh-speed signal processing, operating at the intersection of photonics and electronics. Foster’s lab combines state-of-the-art photonic devices with modern information theory to produce advanced technologies that enhance imaging and sensing systems. His work includes applications in future optical communications systems, ultrawide-bandwidth microwave photonics, and high-throughput optical imaging. Foster develops practical photonic techniques for manipulating signals on extremely fast time scales, from hundreds of picoseconds to a few femtoseconds. His lab pioneered one of the fastest imaging systems in the world, utilizing encoded laser pulses to reconstruct objects at line rates in the tens of megahertz. His research has been funded by the National Science Foundation, IARPA, the Defense Threat Reduction Agency, and NIH National Eye Institute. Foster has published over 200 papers and holds at least eight patents. His awards include a Johns Hopkins Catalyst Award, NSF CAREER award, DARPA Young Faculty Award, and a Young Investigator Award from the Office of Naval Research. In addition to his primary appointment, he serves as a fellow for the Hopkins Extreme Materials Institute.

Research topics

• Computer Science
• Artificial Intelligence
• Physics
• Computer Security
• Optics
• Remote sensing
• Meteorology
• Environmental science
• Optoelectronics
• Geography
• Materials science
• Metallurgy
• Computer vision
• Mathematics

Selected publications

• Hyperspectral imaging pyrometer for reactive material combustion studies

  2026-03-04

  articleSenior author

  The combustion of reactive materials presents a rich in-operando environment of optical information that is primed for in-situ observation and analysis. Typically, optical instruments for combustion studies aim to either record spectral information for pyrometry or other spectral analysis, or spatial information to capture structural evolution at small time scales. In doing so, vital information about the underlying dynamics of the combustion process is neglected. To capture a wholistic view of reactive scenes, we demonstrate a hyperspectral imaging pyrometer to capture the evolution of combusting powders or compacts both spatially and spectrally, while retaining high enough temporal resolution to capture the evolution of the reaction. This spectral information allows for local information about the reaction to be extracted, such as temperature, spectral emissions, and relative emissivity.

  PublisherDOI

• LiDAR Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection

  2025-03-12 · 26 citations

  articleSenior author

  LiDAR-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. However, they are known to be sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR). As a result, LiDAR-based object detectors trained on data captured in normal weather tend to perform poorly in such scenarios. However, collecting and labelling sufficient training data in a diverse range of adverse weather conditions is laborious and prohibitively expensive. To address this issue, we propose a physics-based approach to simulate LiDAR point clouds of scenes in adverse weather conditions. These augmented datasets can then be used to train LiDAR-based detectors to improve their all-weather reliability. Specifically, we introduce a hybrid Monte-Carlo based approach that treats (i) the effects of large particles by placing them randomly and comparing their back reflected power against the target, and (ii) attenuation effects on average through calculation of scattering efficiencies from the Mie theory and particle size distributions. Retraining networks with this augmented data improves mean average precision evaluated on real world rainy scenes and we observe greater improvement in performance with our model relative to existing models from the literature. Furthermore, we evaluate recent state-of-the-art detectors on the simulated weather conditions and present an in-depth analysis of their performance.

  PublisherDOI

• Simultaneous hyperspectral imaging and pyrometry for multi-phase temperature profiling of energetic composite reactions

  Journal of Quantitative Spectroscopy and Radiative Transfer · 2025-07-24 · 3 citations

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• A novel combinatorial approach to evaluate the ignition and combustion performance of aluminum powders alloyed with other elements

  Combustion and Flame · 2025-12-01 · 1 citations

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  PublisherDOI

• Quantifying 3D ejecta velocities during high-velocity impact experiments into concrete

  International Journal of Impact Engineering · 2025-09-24 · 3 citations

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• Automated multi-object tracking: Applications to metal combustion under XPCI

  Computational Materials Science · 2025-11-01

  articleOpen access

  This study introduces the XPCI Multi-object Tracker (XMOT), a tool designed for the automated analysis of X-ray Phase Contrast Imaging (XPCI) videos that capture the combustion of metal composite powders. While tailored for XPCI data, the design and methods behind XMOT are general and can be applied to many kinds of scientific imaging of dynamic processes. XMOT automates the detection of particles and the construction of their trajectories, greatly improving the efficiency of data analysis. This methodology allows for the quantification of dynamic and static particle properties and has been used to demonstrate that micro-explosions occur in both spherical and non-spherical particles. Such data is crucial for evaluating combustion mechanisms and performance. Validation demonstrates that XMOT achieves about 90 % accuracy and 74 % detection coverage in the particle detection step, and shape classification accuracy of about 70 % for spherical particles and about 85 % for non-spherical particles. By automating complex, labor-intensive processes, XMOT facilitates deeper insights into the relationships between material properties and combustion performance, paving the way for advanced material design and optimization. • Automated characterization of metal particle combustion using XPCI. • Multi-object tracking with GMMs, Kalman filters and Hungarian algorithms. • Identification of secondary events (eg. microexplosions) and particle features (eg. sphericity) during combustion. • Generalizable framework for dynamic imaging.

  PublisherDOI

• Quantifying 3d Ejecta Velocities During High-Velocity Impact Experiments into Concrete

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Machine Learning-Assisted Analysis of Combustion and Ignition in As-milled and Annealed Al/Zr Composite Powders

  ArXiv.org · 2025-06-03

  preprintOpen access

  Micron-scale metal-based composite powders are promising for energetic applications due to their tailored ignition and combustion properties. In particular, ball-milled Al/Zr composites exhibit lower ignition thresholds than pure aluminum, driven by exothermic intermetallic formation reactions and have demonstrated enhanced combustion properties. However, the extent to which this heat release governs ignition and combustion remains unclear, especially when progressively removed through annealing. To systematically investigate this effect, we synthesized Al/Zr powders (3Al:Zr, Al:Zr, and Al:3Zr at%) via ball milling, annealed them in argon up to 1000 C to partially complete the formation reactions, and characterized their ignition and combustion behavior. Ignition thresholds were measured using a hot wire method across different environments, while high-speed hyperspectral imaging tracked single-particle burn durations and temperatures. A convolutional neural network (CNN)-based method was developed to quantify the frequency of microexplosions. Results show that annealing - and thus reducing available reaction heat - increases ignition thresholds, most significantly for Al-rich compositions. In contrast, Zr-rich powders exhibit little change in ignition thresholds due to oxidation aiding ignition. Despite removing the available heat that drives ignition, average combustion temperatures range from 2400-3000 K and increased with annealing for Al- and Zr-rich powders. Average maximum temperatures are 100 to 400 K higher. The frequency of microexplosions remains high (>46%) and increases with annealing for all but the Al-rich powders. These findings suggest that while homogeneous Al/Zr powders (e.g., atomized) may exhibit higher ignition thresholds, they can achieve comparable combustion performance once ignited.

  PublisherOA PDFDOI

• Machine learning-assisted analysis of ignition and combustion properties in as-milled and annealed Al-Zr composite powders

  Combustion and Flame · 2025-11-25 · 2 citations

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• The thermal and mechanical response of refractory alloys at ultrahigh temperatures

  Acta Materialia · 2025-11-29

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Recent grants

• Wide-field scan-less multi-photon endoscopy using spatio-temporal pulse delivery and temporal focusing

  NIH · $377k · 2017–2020

• EARS: Photonically-Enabled Extremely Wideband Compressive Wireless Spectrum Sensing

  NSF · $500k · 2014–2017

• CAREER: Ultrahigh-Frame-Rate Compressively-Sensed Imaging using Ultrafast Lasers

  NSF · $400k · 2013–2018

• Compressive Imaging Beyond One Trillion Frames Per Second

  NSF · $280k · 2016–2019

Frequent coauthors

• Alexander L. Gaeta

  Columbia University

  115 shared

• Michal Lipson

  90 shared

• Amy C. Foster

  51 shared

• Reza Salem

  Thorlabs (United States)

  49 shared

• Amy C. Turner-Foster

  Cornell University

  45 shared

• Yoshitomo Okawachi

  37 shared

• Bryan T. Bosworth

  National Institute of Standards and Technology

  37 shared

• Jasper R. Stroud

  Johns Hopkins University

  29 shared

Awards & honors

• Johns Hopkins Catalyst Award
• NSF CAREER award
• DARPA Young Faculty Award
• Young Investigator Award from the Office of Naval Research