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 proposed the use of BOTDA as a monitoring tool to identify fiber types present in deployed hybrid-span fiber cables, to assist in network planning, setting optimal launch powers, and selecting correct modulation formats.
We propose a method to estimate the input power dependency of the transceiver BER-OSNR characteristic. Experiments using commercial transceivers show that estimation error in Q-factor is less than 0.2 dB.
We implement a cascaded learning framework using component-level EDFA models for optical power spectrum prediction in multi-span networks, achieving a mean absolute error of 0.17 dB across 6 spans and 12 EDFAs with only one-shot measurement.
A method is proposed in order to improve QoT-E by calibrating the physical model parameters of an optical link post-installation, using only total power monitors integrated into the EDFAs and an OSA at the receiver.
We introduce a novel scheme to detect and localize optical network anomaly using forward transmission sensing, and develop a heuristic algorithm to optimize the route selection. The performance is verified via simulations and network experiments.
Falling trees or their limbs can cause power lines to break or sag, sometimes resulting in devastating wildfires. Conventional protections such as circuit breakers, overcurrent relays and automatic circuit reclosers may clear short circuits caused by tree contact, but they may not detect cases where