Giovanni Milione NEC Labs AmericaGiovanni Milione is a former Senior Researcher and Business Incubation Lead in the Optical Networking & Sensing Department at NEC Laboratories America, Inc. (NECLA), where he drove the development of groundbreaking optical technologies that span communications, sensing, and computing. With a focus on translating cutting-edge research into commercial and societal impact, Dr. Milione bridged the gap between scientific innovation and business viability. At NECLA, his work included pioneering research in space division multiplexing over optical fibers and free space, the application of machine learning to distributed acoustic sensing, and analog optical computing.

Dr. Milione earned his B.S. degree in Physics from Stony Brook University, his M.A. degree in Physics from CUNY (The City College of New York), and M.Phil. and Ph.D. degrees from CUNY The City College of New York/Graduate Center, where he was a National Science Foundation Graduate Research Fellow. He has authored over 100 publications and patents, with his work cited approximately 5,000 times. His research spans fundamental physics to applied technologies, including innovative approaches involving optical polarization, structured light, and optical communication and sensing systems. Among his notable contributions were advancements in real-time biometric authentication using photo-acoustic tomography, new techniques in high-speed and long-distance free-space and optical fiber communication using optical orbital angular momentum and multimode and multi-core optical fibers.

In recognition of his achievements, Dr. Milione was selected to participate in the National Academy of Engineering‘s prestigious U.S. Frontiers of Engineering Symposium—an honor reserved for the nation’s top early-career engineers. Additionally, he was named Top 40 Under Forty by his undergraduate alma mater, Stony Brook University. He is also a U.S. Army veteran, having served in Operation Iraqi Freedom.

Posts

Optical orbital angular momentum analogy to the Stern-Gerlach experiment

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Symmetry breaking has been shown to reveal interesting phenomena in physical systems. A notable example is the fundamental work of Otto Stern and Walther Gerlach [Stern and Zerlach, Z. Physik 9, 349 (1922)] nearly 100 years ago demonstrating a spin angular momentum (SAM) deflection that differed from classical theory. Here we use non-separable states of SAM and orbital angular momentum (OAM), known as vector vortex modes, to demonstrate how a classical optics analogy can be used to reveal this nonseparability, reminiscent of the work carried out by Sternand Gerlach. We show that by implementing a polarization insensitive device to measure the OAM, the SAM states can be deflected to spatially resolved positions.

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Accelerating Distributed Machine Learning with an Efficient AllReduce Routing Strategy

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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%.

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Distributed Fiber-Optic Sensor as an Acoustic Communication Receiver Array

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A novel acoustic transmission technique using distributed acoustic sensors is introduced. By choosing better incident angles for smaller fading and employing an 8- channel beamformer, over 10KB data is transmitted at a 6.4kbps data rate.

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OFDM Signal Transmission Using Distributed Fiber-Optic Acoustic Sensing

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Acoustic data transmission with the Orthogonal Frequency Division Multiplexing (OFDM) signal has been demonstrated using a Distributed Acoustic Sensor (DAS) based on Phase-sensitive Optical Time-Domain Reflectometry (?-OTDR).

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Distributed Optical Fiber Sensing Using Specialty Optical Fibers

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Distributed fiber optic sensing systems use long section of optical fiber as the sensing media. Therefore, the fiber characteristics determines the sensing capability and performance. In this presentation, various types of specialty optical fibers and their sensing applications will be introduced and discussed.

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Learning Phase Mask for Privacy-Preserving Passive Depth Estimation

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With over a billion sold each year, cameras are not only becoming ubiquitous, but are driving progress in a wide range of domains such as mixed reality, robotics, and more. However, severe concerns regarding the privacy implications of camera-based solutions currently limit the range of environments where cameras can be deployed. The key question we address is: Can cameras be enhanced with a scalable solution to preserve users’ privacy without degrading their machine intelligence capabilities? Our solution is a novel end-to-end adversarial learning pipeline in which a phase mask placed at the aperture plane of a camera is jointly optimized with respect to privacy and utility objectives. We conduct an extensive design space analysis to determine operating points with desirable privacy-utility tradeoffs that are also amenable to sensor fabrication and real-world constraints. We demonstrate the first working prototype that enables passive depth estimation while inhibiting face identification.

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Field Trials of Vibration Detection, Localization and Classification over Deployed Telecom Fiber Cables

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We review sensing fusion results of integrating fiber sensing with video for machine-learning-based localization and classification of impulsive acoustic event detection. Classification accuracy >97% was achieved on aerial coils, and >99% using fiber-based signal enhancers.

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Field Trial of Cable Safety Protection and Road Traffic Monitoring over Operational 5G Transport Network with Fiber Sensing and On-Premise AI Technologies

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We report the distributed-fiber-sensing field trial results over a 5G-transport-network. A standard communication fiber is used with real-time AI processing for cable self-protection, cable-cut threat assessment and road traffic monitoring in a long-term continuous test.

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Optics and Biometrics

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Forget passwords—identity verification can now be accomplished with the touch of a finger or in the blink of an eye as the biometrics field expands to encompass new techniques and application areas.

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3D Finger Vein Biometric Authentication with Photoacoustic Tomography

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Biometric authentication is the recognition of human identity via unique anatomical features. The development of novel methods parallels widespread application by consumer devices, law enforcement, and access control. In particular, methods based on finger veins, as compared to face and fingerprints, obviate privacy concerns and degradation due to wear, age, and obscuration. However, they are two-dimensional (2D) and are fundamentally limited by conventional imaging and tissue-light scattering. In this work, for the first time, to the best of our knowledge, we demonstrate a method of three-dimensional (3D) finger vein biometric authentication based on photoacoustic tomography. Using a compact photoacoustic tomography setup and a novel recognition algorithm, the advantages of 3D are demonstrated via biometric authentication of index finger vessels with false acceptance, false rejection, and equal error rates <1.23%, <9.27%, and <0.13%, respectively, when comparing one finger, a false acceptance rate improvement >10× when comparing multiple fingers, and <0.7% when rotating fingers ±30.

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