From early concepts and laboratory validation to proven field deployments on live infrastructure, we develop technologies that sense the physical world, transform observations into intelligence, and enable informed action. Building on our expertise in optics and photonics, we are expanding toward multimodal sensing, AI-powered understanding, and intelligent systems that interact with the physical world.
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Deployed telecom cables, urban fiber networks, and emerging optical sensors provide a powerful foundation for observing the physical world at scale. Our research spans forward-looking theoretical studies, cutting-edge experiments, artificial intelligence, and world- and industry-first field trials. We transform existing infrastructure into dense, real-time sensing platforms capable of monitoring complex environments over large areas.
Our research follows a broad sense–understand–act vision. We develop optical and distributed fiber sensing technologies that capture physical phenomena; AI methods that interpret complex signals, combine information across sensors, diagnose events, and predict changing conditions; and intelligent solutions that translate these insights into decisions and responses. Looking ahead, we envision extending this foundation beyond optical sensing to multimodal sensing and autonomous systems, including robotics, that can perceive, reason about, and respond to the physical world.
Our current work spans optical sensing, infrared imaging and spectroscopy, radio-frequency spectrum sensing and AI-enabled sensing analytics. These technologies can detect, localize, and characterize vibrations, intrusions, equipment conditions, and environmental changes along an infrastructure network’s physical route. By integrating sensing with edge computing and AI, we seek to move beyond data collection toward real-time understanding, prediction, and automated response.
By uniting advanced sensing, physical-world AI, and intelligent action, Optical Sensing & Solutions aims to create safer, more resilient, and more autonomous systems for infrastructure, utilities, transportation, environmental monitoring, public safety, smart cities, and intelligent manufacturing.
Are you interested in joining us? We seek the next generation of thought leaders in optical sensing for researcher positions. Outstanding applicants pursuing a career in optical sensing are encouraged to visit our careers page to learn more and apply for available positions.
Exciting internship opportunities for Summer 2027 will be available on our internship page this fall. We are looking for students pursuing advanced degrees in Computer Science and Electrical Engineering. Internships are typically 3 months long during the summer. The benefits of working for us include the opportunity to quickly join a project team applying cutting-edge technology to industry-leading concepts.
To apply for a Summer Internship, visit our Internship page.
Want to learn more about our optical sensing & solutions department? Meet our team of experts, check out our blog posts, or read our latest publications.
In this research area, we first focus on developing new optoelectronic designs and FPGA-based signal-processing technologies to improve the sensitivity, dynamic range, and stability of both backscattering- and forward-transmission-based DFOS systems.
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Our research and development focus on innovative applied machine learning and signal processing algorithms that convert low-level sensory inputs into high-level information, providing solutions for real-world sensing applications in safety, security, smart city, infrastructure health monitoring, power & energy industry, and more.
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We derive the exact probability density function of the signal to noise ratio (SNR) impairment caused by polarization dependent loss (PDL) in coherent optical systems. Using thehinge model for PDL statistics, we construct a sequence of variable transformations that yields the expression of the SNR probability
Machine learning is shifting from learning from data alone to learning from both data and teacher models. Beta-KD uses uncertainty-aware Bayesian weighting to train compact multimodal AI without blindly trusting every teacher signal.
Distributed multichannel acoustic sensing (DMAS) enables large-scale sound event classification (SEC), but performance drops when many channels are degraded and when sensor layouts at test time differ from training layouts. We propose a learning-free, physics-informed inpainting frontend based on reverse
Accurately modeling optical signal transmission is critical foroptimizing network performance, particularly in large-scalefiber optic networks operated by Internet Service Providers.In this work, we develop a Gaussian Noise model for a NewYork state ISPs optical backbone. Our model accounts for allmajor
Real-world deployment requires sound event and acoustic scene classification systems to remain reliable in noisy, diverse environments on resource-constrained devices. Although contrastive language-audio pretraining (CLAP) models with Transformer-based audio encoders achieve strong zero-shot performance,
Distributed fiber optic sensing (DFOS) has emerged as a promising technology for wide-area monitoring by utilizing existing telecom cables as large-scale sensing media. This paper explores three sensing modalities, backscattering-based sensing, forward-transmission-based sensing, and hybrid sensing,