Tingfeng Li is a Researcher in the Optical Networking and Sensing 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.
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 of sessions featuring industry leaders, OFC 2025 serves as the central hub for innovation and collaboration in the field. At this year’s conference, NEC Labs America will showcase its cutting-edge research and advancements through multiple presentations, demonstrations, and workshops.
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 intrusion detection via buried cables and impulsive event classification through aerial cables. To achieve comprehensive intrusion detection, we introduce a label encoding strategy for multitask learning and evaluate the generalization performance of the proposed approach across various domain shifts. For accurate recognition of impulsive acoustic events, we compare several standard choices of representations for raw waveform data and neural network architectures, including convolutional neural networks (ConvNets) and vision transformers (ViT).We also study the effectiveness of the built-in inductive biases under both high- and low-fidelity sensing conditions and varying amounts of labeled training data. All computations are executed locally through edge computing, ensuring real-time detection capabilities. Furthermore, our proposed system seamlessly integrates with cameras for video analytics, significantly enhancing overall situation awareness of the surrounding environment.
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 test-time policy adaptation to new environments where the reward signals are not readily available and outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing the trained agent from one specific class to another class. Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate the effectiveness of our approach.
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.
