Philip Ji NEC Labs America

Philip Ji is a Senior Researcher in the Optical Networking and Sensing Department at NEC Laboratories America, where he has been researching optical switching, networking, and sensing technologies since 2001. He received his B.E., M.E., and Ph.D. in Electrical Engineering & Telecommunications from the University of New South Wales (UNSW), where he focused on special polymer optical fiber and fiber optic device innovation.

His research interests include fiber optic sensing, WDM switching node and network architecture, fiber optic devices for communication, high-speed and high-capacity optical transmission, optical interconnects for the data center, and special optical fiber. Dr. Ji’s current research at NEC centers on the design and optimization of distributed fiber optic sensors and high-capacity optical networking technologies, with a focus on metro-access integration, network virtualization, and software-defined networking (SDN). He develops scalable, energy-efficient architectures that enable greater agility and automation in optical networks.

His work addresses key challenges in elastic bandwidth allocation, resource abstraction, and control-plane programmability, supporting the transition toward fully programmable, converged metro and access infrastructures. He leads initiatives that advance virtualization of transport resources and dynamic service provisioning, helping telecom and enterprise networks respond to rapidly shifting traffic patterns and service demands. His research contributes to the evolution of next-generation optical networks that are flexible, cost-effective, and ready for future application-driven architectures.

Posts

NEC Labs America Attends OECC June 28 – July 2, 2026

NEC Laboratories America is proud to participate in OECC 2026, the 31st Opto-Electronics and Communications Conference, taking place in Busan, South Korea. We look forward to connecting with the international photonics and communications community and sharing the work we’re doing to shape the next generation of optical networks.

Mobile Orbital Domain-based Hierarchical Routing in Satellite Networks

We propose a mobile orbital domain-based hierarchical routing scheme which addresses the challenges posed by constant satellite movement and the resulting dynamicnetwork topology, thus significantly improving the routing scalability and efficiency in satellite networks.

NEC Labs America Attending OFC 2026 Los Angeles, March 15-19

NEC Laboratories America’s Optical Networking & Sensing team will participate in OFC 2026 in Los Angeles, March 15–19, contributing to panels, workshops, and courses focused on optical sensing, multicore fibers, and next-generation high-capacity optical communication systems.

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

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.

Fiber sensing in IOWN Global Forum

Fiber sensing function was introduced in 2020 as one of the key technology features for the OpenAPN (all photonics network) developed by IOWN GF (Innovative Optical and Wireless NetworkGlobal Forum) in 2020.To our best knowledge, IOWN GF is the first global standard developmentorganization or technology forum that studied fiber sensing technology for telecommunication anddata communication networks, because it brings new feature and benefits to the networkoperators (such as making network operation more efficient, and bringing new values to theexisting network infrastructure), as shown in the examples above.

Accelerating Distributed Machine Learning with AllReduce Reconfiguration Based on Optical Circuit Switching

We propose to apply optical circuit switching to enable dynamic AllReduce reconfiguration for accelerating distributed machine learning. With simulated annealing-based optimization, theproposed AllReduce reconfiguration approach achieves 31% less average training time than existing solutions.

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

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.

400-Gb/s mode division multiplexing-based bidirectional free space optical communication in real-time with commercial transponders

In this work, for the first time, we experimentally demonstrate mode division multiplexing-based bidirectional free space optical communication in real-time using commercial transponders. As proof of concept, via bidirectional pairs of Hermite-Gaussian modes (HG00, HG10, and HG01), using a Telecom Infra Project Phoenix compliant commercial 400G transponder, 400-Gb/s data signals (56-Gbaud, DP-16QAM) are bidirectionally transmitted error free, i.e., with less than 1e-2 pre-FEC BERs, over approximately 1-m of free space

Free-Space Optical Sensing Using Vector Beam Spectra

Vector beams are spatial modes that have spatially inhomogeneous states of polarization. Any light beam is a linear combination of vector beams, the coefficients of which comprise a vector beam “spectrum.” In this work, through numerical calculations, a novel method of free-space optical sensing is demonstrated using vector beam spectra, which are shown to be experimentally measurable via Stokes polarimetry. As proof of concept, vector beam spectra are numerically calculated for various beams and beam obstructions.

Accelerating Distributed Machine Learning with an Efficient AllReduce Routing Strategy

We propose an efficient routing strategy for AllReduce transfers, which compromise of the dominant traffic in machine learning-centric datacenters, to achieve fast parameter synchronization in distributed machine learning, improving the average training time by 9%.