Shaobo Han NEC Labs AmericaShaobo Han is a Senior Researcher in the Optical Networking and Sensing Department at NEC Laboratories America in Princeton, NJ. He received his Ph.D. in Electrical and Computer Engineering and his M.S. in Statistical Science from Duke University, where his research focused on probabilistic modeling, transfer learning, and structured variational inference. He also earned an M.Eng. degree in Signal and Information Processing from the University of Chinese Academy of Sciences.

At NEC, Dr. Han has been prototyping and delivering advanced algorithmic solutions for real-world applications of sensing AI. By leveraging massive waveform data from NEC’s distributed fiber-optic sensors and cutting-edge machine learning technologies, his work transforms telecom infrastructure into a dense, large-scale network of acoustic sensors capable of real-time situational awareness. His research has led to multiple world-first and industry-first technology field trials and commercial products. He is the recipient of the NECAM Extra Mile Award, and the Outstanding Performance Award from NEC’s Global Innovation Business Unit (GIBU).

He also conducts research on parameter-efficient fine-tuning of large language models and the flexible adaptation of audio-language models. He holds more than 10 U.S. patents and has authored over 50 peer-reviewed papers in top-tier venues, including NeurIPS, ICLR, ICML, AISTATS, ICASSP, OFC, IEEE Transactions on Signal Processing, and the Journal of Lightwave Technology. His innovations advance the learning of structured, interpretable representations of the physical world from raw sensory inputs and enable cost-effective generalization to new environments and deployment scenarios.

Posts

Eric Blow Presents at the IEEE Photonics Conference Singapore on November 10th & 13th

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Eric Blow of NEC Labs will address how machine-learning methods applied to distributed acoustic-sensing data can monitor facility perimeters and detect intrusion via walk, dig, or drive events over buried optical fibre—for example achieving ~90% classification accuracy. Later in the week he will explore neuromorphic photonic RF sensing combining silicon photonics with FPGA-based recurrent neural networks, and his intern Yuxin Wang will present a finalist paper on scalable photonic neurons for automatic modulation classification.

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Energy-based Generative Models for Distributed Acoustic Sensing Event Classification in Telecom Networks

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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 solution for real-world deployment in complex environments.

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Computation Stability Tracking Using Data Anchors for Fiber Rayleigh-based Nonlinear Random Projection System

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We introduce anchor vectors to monitor Rayleigh-backscattering variability in a fiber-optic computing system that performs nonlinear random projection for image classification. With a ~0.4-s calibration interval, system stability can be maintained with a linear decoder, achieving an average accuracy of 80%-90%.

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Toward Intelligent and Efficient Optical Networks: Performance Modeling, Co-existence, and Field Trials

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Optical transmission networks require intelligent traffic adaptation and efficient spectrum usage. We present scalable machine learning (ML) methods for network performance modeling, andfield trials of distributed fiber sensing and classic optical network traffic coexistence.

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Span-based Polarization Sensing in Cables Without Reflectors

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Polarization-based, multi-span sensing over a link without reflection-back circuits is demonstrated experimentally. It is shown that distributed reflection from Rayleigh scattering can serveas an alternative to reflectors after spatial averaging of received state-of-polarization

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First City-Scale Deployment of DASs with Satellite Imagery and AI for Live Telecom Infrastructure Management

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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

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Text-guided Device-realistic Sound Generation for Fiber-based Sound Event Classification

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Recent advancements in unique acoustic sensing devices and large-scale audio recognition models have unlocked new possibilities for environmental sound monitoring and detection. However, applying pretrained models to non-conventional acoustic sensors results in performance degradation due to domain shifts, caused by differences in frequency response and noise characteristics from the original training data. In this study, we introduce a text-guided framework for generating new datasets to retrain models specifically for these non-conventional sensors efficiently. Our approach integrates text-conditional audio generative models with two additional steps: (1) selecting audio samples based on text input to match the desired sounds, and (2) applying domain transfer techniques using recorded impulse responses and background noise to simulate the characteristics of the sensors. We demonstrate this process by generating emulated signals for fiber-optic Distributed Acoustic Sensors (DAS), creating datasets similar to the recorded ESC-50 dataset. The generated signals are then used to train a classifier, which outperforms few-shot learning approaches in environmental sound classification.

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CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

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Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiberoptic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory recorded fiber-optic ESC-50 datasets and a real-world fiber optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.

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Field Trials of Manhole Localization and Condition Diagnostics by Using Ambient Noise and Temperature Data with AI in a Real-Time Integrated Fiber Sensing System

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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.

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Dual Privacy Protection for Distributed Fiber Sensing with Disaggregated Inference and Fine-tuning of Memory-Augmented Networks

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We propose a memory-augmented model architecture with disaggregated computation infrastructure for fiber sensing event recognition. By leveraging geo-distributed computingresources in optical networks, this approach empowers end-users to customize models while ensuring dual privacy protection.

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