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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A variety of efforts have been put into sensing and modeling of pavements. Such capability is commonly validated with experimental data and used as reference for damage detection and other structural changes. Finite element models (FEM) often provides a high fidelity physics-base benchmark to evaluate
Among the power transmission infrastructures, the low-voltage overhead power lines are specifically critical, due to the complicated roadside environments and the significant number of connections to the end utility users. Maintaining of such a large size grid with mostly wood poles is a challenging
This paper presents a deep learning-based framework to detect and localize the pedestrians’ anomaly behaviors in videos captured at the grade crossing. A skeleton detection and tracking algorithm are employed to capture the key point trajectories of body movements of the pedestrians. A deep recurrent
For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.
We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.
We report distributed-fiber-optic-sensing results on impulsive acoustic events localization/classification over telecom networks. A deep-learning-based model was trained to classify starter-gun and fireworks signatures with high accuracy of > 99% using fiber-based-signal-enhancer and >97% using aerial