OPTICAL SENSING & SOLUTIONS
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Optical Sensing & Solutions
Tingfeng Li is a Researcher in the Optical Sensing & Solutions Department at NEC Laboratories America. She received her PhD in Computer Science from Rutgers University, her MS in Automation Engineering from Shanghai Jiao Tong University, and her BE in Automation Engineering from the University of Electronic Science and Technology of China. Her academic research has centered on representation learning, generative models, zero- and few-shot learning, and domain adaptation—areas that equip her with the tools to address complex sensing and signal interpretation challenges.
At NEC Labs, Tingfeng applies advanced machine learning and signal processing techniques to the development of next-generation distributed fiber-optic sensing (DFOS) systems. Her recent contributions include AI-driven road deformation detection using ambient noise–based distributed acoustic sensing (DAS), presented at OFC 2025, which demonstrated new possibilities for large-scale, non-intrusive infrastructure monitoring. She has also developed deep learning methods for intrusion and impulsive event detection, published in the IEEE Journal of Lightwave Technology, expanding the accuracy and reliability of DFOS in security applications. Her earlier work includes reinforcement learning–based localization methods for DFOS systems, presented at ICLR 2022, which explored adaptive approaches to improve event positioning in complex environments.
In addition to her publications, Tingfeng is a co-inventor on a patent for long-distance DFOS and WDM transmission using Raman amplification, a technology that enhances sensing range while maintaining high signal quality. By integrating expertise in AI, photonics, and large-scale sensing, Tingfeng’s work advances NEC Labs’ mission to create high-performance, scalable, and intelligent optical sensing systems with transformative potential for industries such as transportation, infrastructure management, and security.
Mix-CLAP from NEC Laboratories America delivers near-Transformer accuracy for sound event classification at a fraction of the compute cost, using dual lightweight encoders and adaptive, noise-aware inference for real-world edge deployment.
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,
NEC Laboratories America celebrates Women’s History Month and International Women’s Day by recognizing the women researchers, engineers, and interns whose work in artificial intelligence, optical networking, cybersecurity, and data science is helping shape the future of technology.
We present methods and field trial results demonstrating an integrated distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) system for manhole localization, condition diagnostics, and anomaly detection in pre-deployed telecommunication fiber networks. The proposed system leverages
Transformer-based methods have demonstrated strong potential in hyperspectral pansharpening by modeling long-range dependencies. However, their effectiveness is often limited by redundant token representations and a lack of multiscale feature modeling. Hyperspectral images exhibit intrinsic spectral
Distributed fiber-optic sensing combined with machine learning enables continuous monitoring of telecom infrastructure. We employ generative modeling for event classification, supporting semi supervised learning, uncertainty calibration, and noise resilience. Our approach offers a scalable, data-efficient
We demonstrate real-time fiber risk assessment and dynamic network routing in live metro networks using deployed DASs, satellite imagery, and large-scale AI, achieving the first significantreduction in fiber failures in four years
This study demonstrates an AI-driven method for detecting road deformations using Distributed Acoustic Sensing (DAS) over existing telecom fiber networks. Utilizingambient traffic noise, it enables real-time, long-term, and scalable monitoring for road safety.
Field trials of ambient noise-based automated methods for manhole localization and condition diagnostics using a real-time DAS/DTS integrated system were conducted. Crossreferencingmultiple sensing data resulted in a 94.7% detection rate and enhanced anomaly identification.
The NEC Labs America Optical Networking and Sensing team is attending the 2025 Optical Fiber Communications Conference and Exhibition (OFC), the premier global event for optical networking and communications. Bringing together over 13,500 attendees from 83+ countries, more than 670 exhibitors, and hundreds
We introduce two pioneering applications leveraging Distributed Fiber Optic Sensing (DFOS) and Machine Learning (ML) technologies. These innovations offer substantial benefits forfortifying telecom infrastructures and public safety. By harnessing existing telecom cables, our solutions excel in perimeter
We propose a reinforcement learning-based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. Our proposed method enables
We demonstrate a new application of fiber-optic-sensing and machine learning techniques for vehicle run-off-road events detection to enhance roadway safety and efficiency. The proposed approach achieves high accuracy in a testbed under various experimental conditions.
