Sensing AI for Real-World Physical Systems

projects

Machine Learning for Optical Fiber Sensing over Deployed Telecommunication Infrastructure

Overview

Telecommunication fiber networks provide a large-scale sensing substrate that extends beneath cities and along long-haul routes. Distributed fiber sensing turns existing optical cables into sensors for the physical world, while modern machine learning makes it possible to extract useful information from the resulting large-scale spatiotemporal data.

Telecom
Networks
→
Distributed Fiber
Sensing
→
Physical Signals
at Scale
→
Machine
Learning
→
Physical-World
Intelligence

My research develops machine learning methods for distributed acoustic and temperature sensing, with an emphasis on turning raw sensing signals into reliable physical-world intelligence. Applications include infrastructure monitoring, cable protection, traffic and vehicle sensing, manhole localization and condition diagnostics, and other forms of situational awareness over deployed telecommunication networks.

The work spans learning from limited supervision, generalization and adaptation across sensing environments, probabilistic and generative modeling, foundation-model adaptation, and structured prediction. A central goal is to connect advances in machine learning with sensing systems that operate beyond controlled laboratory settings, using deployed infrastructure as both a source of real-world data and a testbed for generalization, adaptation, and validation.

Research Themes

Learning from Limited Supervision

Deployed fiber-optic sensing networks generate massive streams of spatiotemporal data, but obtaining precise task-specific annotations is often expensive and operationally difficult. My research explores how to turn this abundance of unlabeled and weakly labeled field data into an advantage, using weakly supervised learning, self-supervised learning, semi-supervised learning, and generative semi-supervised learning to learn useful sensing representations and build reliable models with limited manual labeling.

Generalization Across Sensing Environments

Models trained in one sensing environment or deployment configuration may not transfer reliably to another. My research studies inductive biases, representation learning, and adaptation mechanisms that improve generalization across sensing environments, routes, and configurations, with field deployments providing realistic tests of robustness.

Adaptation and Multimodal Foundation Models

Pretrained audio and multimodal models provide transferable knowledge for downstream sensing domains, but substantial domain shifts can arise when adapting them to fiber-optic sensing data. My recent work explores support-set adaptation, knowledge distillation, and noise-aware representation learning to efficiently transfer this knowledge to domain-specific sensing tasks.

Structured Prediction for Linear Infrastructure

Many critical infrastructure systems—including roads, highways, railways, pipelines, power lines, and telecommunication networks—extend over long linear corridors, making distributed fiber sensing a natural platform for large-scale monitoring. My research explores structured prediction along these corridors, using dense spatial information from distributed sensing together with geospatial context to continuously localize events and monitor infrastructure conditions in the physical world.

Research Progression

Representative work progresses from weakly supervised landmark localization using ambient noise (Optics Express 2023), to energy-based generative classification that leverages unlabeled DAS data through semi-supervised modeling while supporting uncertainty calibration (ECOC 2025 Top-Scored Paper), to self-supervised representation learning and adaptation that uses sensing location as a pseudo-label to learn transferable representations across deployed fiber routes (JOCN 2026).

The research also progresses from event-level recognition (JLT 2024) to structured prediction for status monitoring over linear infrastructure (JLT 2026; OFC 2025 Top-Scored Paper), leveraging spatial and temporal context for fine-grained localization and infrastructure condition monitoring. Across these efforts, methods are developed and validated through field experiments on existing telecommunication fiber networks.

Field Deployment & Real-World Validation

Field deployment has been an integral part of my sensing research, using existing telecommunication networks to develop and validate sensing-AI methods under real-world operational conditions.

Cable Protection → Infrastructure Diagnostics → City-Scale Monitoring
01

Cable Protection & Event Monitoring

Long-term abnormal-activity monitoring and threat assessment over deployed telecommunication fiber.

Long-term field monitoring over deployed telecommunication fiber

Representative field events include a fallen utility pole affecting aerial cable and construction activity near buried cable.

02

Infrastructure Localization & Diagnostics

Distributed acoustic and temperature sensing for infrastructure localization and condition assessment across deployed fiber routes.

Infrastructure localization and diagnostics using DAS and DTS

Field trials in Richardson, TX, and Long Beach Island, NJ, demonstrate complementary DAS and DTS information for manhole localization and condition diagnostics.

03

City-Scale Multimodal Sensing

Long-term fiber sensing integrated with satellite imagery and AI for live telecommunication infrastructure management.

City-scale monitoring over deployed fiber with satellite imagery

A 21-month field deployment over approximately 40 km of fiber, with construction activity cross-referenced using satellite imagery.

Datasets & Code

Public resources associated with this research include:

  • CLAP-S — code for support-set adaptation of pretrained audio-language models. Code
  • Gunshot–Firework Dataset — fiber-optic acoustic sensing dataset for downstream recognition. Dataset

Patents & Technology Transfer

Sensing-AI research has contributed to patented technologies spanning the full pipeline from machine-learning methodology to physical-world applications and operational systems:

  • ML Methodology for Fiber Sensing — methods spanning context encoders, self-supervised, contrastive, and weakly supervised learning; zero-shot domain adaptation and support-set adaptation of foundation models; generative and synthetic-data modeling; and memory-augmented learning for privacy-preserving system design.

  • Physical Infrastructure Sensing & Understanding — methods for localization, identification, and condition monitoring of buried and aerial cables, manholes and handholes, utility poles and fiber coils, roads and highways, vehicles and construction equipment, and other physical assets and landmarks along deployed fiber networks.

  • Real-World Sensing Applications — applications spanning cable protection and intrusion detection, traffic and vehicle sensing, infrastructure health monitoring, earthquake and acoustic-source localization, fiber-route mapping and geolocation, and public-safety use cases such as gunshot detection.

  • Systems, Prototypes & Technology Transfer — research prototypes integrate fiber sensing, AI, and real-time processing into end-to-end systems for field validation and operational use. Related NEC Labs America research has also progressed into commercial systems: the NEC Fiber Optic Smart Sensing (FOSS) solution provides a modular AI-analytics platform over deployed fiber for applications including Fiber Cable Monitoring, Fiber Cable Identification, Fiber Cable Position Locator, and Pigtail Finder.

View patent portfolio →

Tutorials & Talks

View all talks & tutorials →

Research Highlights & Media

Fiber Sensing Publications

2026

2025

2024

2023

2022

2021