Lab overview
The Data and Devices Lab at Nokia Bell Labs is a multi-disciplinary team of physicists, chemists, engineers, and computer scientists that explore novel devices and AI that will intimately couple humanity to the digital world. We work on both distributed physical – machine vision, chemical detection, air quality – and non-invasive physiological – biochemical, bioelectrical, and bio-optical – sensors from the physical layer to AI perception. We believe the intimate co-development of hardware and AI is key to enabling future ubiquitous, unobtrusive, multi-sensory systems that support industrial autonomization and the evolution of humanity towards Homo augmentus.
Research projects
Research in our group centers around developing techniques and strategies that address critical human and industrial needs. We accomplish this by aligning our highly multi-disciplinary and diverse expertise with Bell Labs' vision of creating the digital twin and augmenting human capabilities through five main application areas. We aim to bring forth novel sensors, materials, and devices to obtain rich and meaningful data. Coupled with powerful data analytics methods, we formulate and infer deep knowledge about humans, the local environments, and their mutual interactions.
We Create Innovative Optical Technologies
Nokia Bell Labs developed a novel method for continuous biochemical sensing using OCT, combining deep-penetrating imaging and machine learning to enable non-invasive…
This research tackles real-time person localization in complex indoor environments using camera sensors and RFID locators, focusing on sensor fusion and data association…
This work focuses on developing domain-generalizable person re-identification models for real-time tracking across cameras, overcoming challenges like lighting, occlusions,…
This research develops an RL agent using sequence neural networks and domain randomization to enable robotic arms with varying link configurations to adapt to real-world tasks…
Gecko is an IoT module with a Nordic nRF52832 MCU, 6-axis motion sensor, environmental sensors, and microphone, offering BLE connectivity, extended battery life, and data…
Nokia Bell Labs is pioneering topological quantum computing, using ultra-stable GaAs quantum systems to overcome decoherence, enabling scalable, practical quantum applications…
Bell Labs' backscatter radio tech enables ultra-low-cost, battery-free RFID tags for long-range, high-accuracy asset tracking, bridging the digital-physical gap.
Blogs and presentations
TEDx Naperville: Our views on the future of digital medicine 11.11.2020
Join the world’s leading experts in information and communications technologies
We’re searching for creative and highly motivated technologists and researchers who share our mindset: curious, passionate and looking to make discoveries that will help change human existence for the better.
Bell Labs Machine Learning Researcher
Our team, Statistics and Data Science Research Group, is part of Bell Labs Artificial Intelligence Lab. We are statisticians, computer scientists, and engineers, designing and developing innovative solutions to advance the state-of-the art in AI/ML and data science. We have multiple openings for researchers with background in any of our research areas including, but not limited to, computer vision, time-series analytics, NLP, autoML, visual analytics, interactive machine learning, active learning, and deep learning.
Bell Labs - Research Statistician
We are looking for an individual who is a passionate problem-solver in data, Artificial Intelligence, and Machine Learning.
Bell Labs - Computer Vision Researcher
Locations
New Jersey, USA
Artificial Intelligence Research Lab (AIRL), Nokia Bell Labs, 600 Mountain Ave., New Providence New Jersey, USA
Cambridge, United Kingdom
Artificial Intelligence Research Lab (AIRL), Nokia Bell Labs, 21 JJ Thomson Ave., CB3 0FA, Cambridge, UK
APA style publications
- R. L. Willett, et al., “Interference Measurements of Non-Abelian e/4 & Abelian e/2 Quasiparticle Braiding,” Phys. Rev. X 13, 011028 (2023). (Link)
- L. Meegahapola, et al., “Quantified Canine: Inferring Dog Personality From Wearables,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). (Link)
- J. Stuchbury-Wass, et al., "Heart Rate Extraction from Abdominal Audio Signals," 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (Link)
- R. L. Willett, et al., “Atypical edge current interference in high quality 2D electron systems: both Ising and Fibonacci anyons?” APS March Meeting 2023. (Link)
- M. Zheng, M. S. Crouch, and M. S. Eggleston, "Surface Electromyography as a Natural Human-Machine Interface: A Review," in IEEE Sensors Journal (2022). (Link)
- B. R. Samanta, et al., "Low-cost electrothermally actuated MEMS mirrors for high-speed linear raster scanning," Optica 9, 251-257 (2022). (Link)
- M. Zheng, H. Jahanandish and H. Li, "Dynamic Classification of Imageless Bioelectrical Impedance Tomography Features with Attention-Driven Spatial Transformer Neural Network," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022. (Link)
- E. Bondareva et al., "Stress Inference from Abdominal Sounds using Machine Learning," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022. (Link)
- B. R. Samanta and S. Zilpelwar, "Photothermal LiDAR for materials characterization," in Frontiers in Optics + Laser Science 2022. (Link)
- M. Zheng, H. Jahanandish and B. R. Samanta, "Imageless Electrical Impedance Tomography for Highly Sensitive Object Dynamics Detection," 2022 IEEE Sensors, 2022. (Link)
- H. Li, M. Zheng and M. S. Eggleston, "The Gecko Sensor: An Ultra-Compact, Low-Cost, Solar-Powered Environment Monitoring Device," 2022 IEEE Sensors, 2022. (PDF)
- K. Bimbraw and M. Zheng, "Towards the Development of a Low-Latency, Biosignal-Controlled Human-Machine Interaction System," 2023 IEEE/SICE International Symposium on System Integration (SII), 2023. (Link)
- S. Shah, Q. Mac, J. Kim, and M. S. Eggleston, "Aptamer-Based Microparticles for Biochemical Sensing Using Optical Coherence Tomography," in Frontiers in Optics + Laser Science 2022. (Link)
- R. L. Willett, et al., “Probing non-Abelian candidate states in 2D electron systems through interferometry using single quasiparticle manipulation,” 24th International Conference on High Magnetic Fields in Semiconductor Physics, 2022.
- B. R. Samanta, et al., "Phase-sensitive OCT on a silicon photonic chip: characterization and functional ear imaging," in Biophotonics Congress: Biomedical Optics 2022. (Link)
- R. L. Willett, et al., “Interferometry measurements on candidate non-Abelian states and their nearby Abelian states,” APS March Meeting 2022. (Link)
- Samanta, B. R.*, et al., "Phase-sensitive OCT on a silicon photonic chip: characterization and functional ear imaging," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper CM2E.3. (Link)
- Hosangadi, H.*; Kumar, A. R. ”Malleable Agents for Re-Configurable Robotic Manipulators” 2022 ArXiv preprint - arXiv:220.02395 (Link)
- Kim, J. D.; Amalfi, R. L.* "Machine learning-based model for optimal operating conditions of thermosyphons for electronic cooling applications” 2021 ASME InterPACK. (Link)
- Amalfi, R. L.*; Kim, J. D. ”Machine learning-based prediction methods for flow boiling in plate heat exchangers” 2021 IEEE ITHERM (Link)
- Crouch, M.S.*, Zheng, M. and Eggleston, M.S., 2021. Natural Typing Recognition via Surface Electromyography. arXiv preprint arXiv:2109.10743. (Link)
- Zheng, M.*; Zarra, S.; Samanta, B. R. "A Screen-Printed Stretchable Bioelectrical Sensing Sleeve with Quasi-Dry Microfluid-Wicking Interface". 2021 IEEE Sensors Annual Conference, pp. 1-4. (Link)
- Zheng, M.*; Ibrahim, B. "Performance Prediction, Sensitivity Analysis and Parametric Optimization of Electrical Impedance Tomography Using a Bioelectrical Tissue Simulation Platform". 2021 IEEE Engineering in Medicine & Biology Society Annual Conference (EMBC), pp. 1-7. (Link)
- Zheng, M.*; Samanta, B. R.; Lattarulo, M. "Unsuspecting Baseline Measurement Variations in Commercial Bioelectrical Impedance Analysis-Driven Wellness Machines". 2021 IEEE Engineering in Medicine & Biology Society Annual Conference (EMBC), pp. 1. (PDF)
- Samanta, B. R.*; Pardo, F.; Kopf, R.; Eggleston, M.S. "Low-cost Electrothermally Actuated MEMS Mirrors for High-Speed 3D Laser Scanning Applications." 2021 IEEE Photonics Conference (IPC), pp. 1-2. (PDF)
- Chuang, C.-Y.; Eggleston, M.S.; Shah, S.* "Monitoring Human Blood Flow Dynamics with Quantitative Speckle Variance Optical Coherence Tomography." 2021 IEEE Photonics Conference (IPC), pp. 1-2. (PDF)
- Shah, S.*; Yu, C. -N.; Zheng, M.; Kim, H.; Eggleston, M.S. “Microparticle-based Biochemical Sensing using Optical Coherence Tomography and Deep Learning.” ACS Nano. (2021). 15: 9764-9774. (PDF)
- Farooq, A.*; Radivojevic, Z.; “Video Conferencing in the Age of Covid-19: Engaging Online Interaction Using Facial Expression Recognition and Supplementary Haptic Cues", Advances in Intelligent Systems and Computing series, 2021, Volume 1378. http://ihiet-ai.org/program.html. (PDF)
- Constantinides, M.*; Šćepanović, S.; Quercia, D.; Li, H.; Sassi, U.; and Eggleston, M. S. “ComFeel,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 4, no. 4, pp. 1–21, Dec. 2020. (PDF)
- Kim, H.; Yu, C.-N.; Kennedy, W.; Eggleston, M.S.; Shah, S.* "Automated Monitoring for Optical Coherence Tomography-based Biosensing Using Deep Learning," (2020). IEEE Photonics Conference (IPC), pp. 1-2. (PDF)
- Zheng, M.*; Crouch, M.S.*; Eggleston, M.S. "Surface Electromyography as a Natural Human-Machine Interface: A Review". ArXiv preprint. 2020. (PDF)
- Crouch, M.S.*; Zheng, M.; Eggleston, M.S. "An LSTM-CNN Enabled Fine-Grained Human-Machine Interface Using sEMG Trained with a Natural Typing Task". Oral Presentation on October 14-17, 2020. Annual Conference of Biomedical Engineering Society. (PDF).
- Shah, S.*; Zheng, M.; Eggleston, M.S. "Remote Monitoring of Microparticle Biosensors Using Optical Coherence Tomography". The Annual Conference of the IEEE Photonics Society. September 28 – October 1, 2020. (PDF)
- Simsarian, J.E.; Hall, M.N.; Hosangadi, G.*; Gripp, J.; Raemdonck, W.V.; Yu, J.; and Sizer, T. "Stream Processing for Optical Network Monitoring with Streaming Telemetry and Video Analytics". In Proceedings of 46th European Conference on Optical Communication (ECOC ’20), Brussels, Belgium, December 2020. (PDF)
- Wang, D.*; Hosangadi, G.; Monogioudis, P.; Rao, A. “Mobile Device Localization in 5G Wireless Networks” 2019 International Conference on Computing, Networking and Communications (ICNC). (PDF)
- Eggleston, M.S., et al., “Towards Ubiquitous 3D Sensing: Chip-Scale Swept-Source Optical Coherence Tomography,” 2019. 24th OptoElectronics and Communications Conference (OECC). (PDF)
- Eggleston, M.S., et al., “90dB Sensitivity in a Chip-Scale Swept-Source Optical Coherence Tomography System,” in Conference on Lasers and Electro-Optics, 2018, p. JTh5C.8. (PDF)
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