About
Shaobo Han is a Senior Researcher in the Machine Learning Department at NEC Laboratories America. His research focuses on generalization, adaptation, and efficient AI, spanning foundation-model adaptation and post-training, multimodal learning and reasoning, probabilistic machine learning, and AI for physical sensing systems. His work connects advances in machine learning with real-world deployment, including large-scale sensing using deployed telecommunication fiber networks, and has contributed to multiple world-first and industry-first technology field trials and commercial products.
He has authored more than 50 peer-reviewed papers in venues including NeurIPS, ICML, ICLR, CVPR, AISTATS, ICASSP, OFC, ECOC, Journal of Lightwave Technology, and Optics Express, and is an inventor on 19 granted U.S. patents. He is a Senior Member of IEEE.
He received his Ph.D. in Electrical and Computer Engineering and M.S. in Statistical Science from Duke University, and his M.Eng. in Signal and Information Processing from the University of Chinese Academy of Sciences.
Media & Press
2026 — Interview: Shaobo Han: Photonics Liaison & Associate Editor of IEEE Transactions on Big Data
2025 — NEC Fiber Optic Smart Sensing (FOSS): commercial-grade AI analytics over deployed fiber, based on patented research by NEC Labs America. Product datasheet
2023 — AI-enabled fiber sensing research featured by Laser Focus World: Top Story · Photonics Hot List
2021 — Two AI-based fiber sensing solutions commercialized by NEC: NEC press release
Professional Service
Editorial Service
- Associate Editor, IEEE Transactions on Big Data
Conference Service
- Area Chair, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
- Program Committee / Reviewer: NeurIPS, ICML, ICLR, CVPR, AISTATS, IJCAI, AAAI, and UAI
Journal Reviewing
Reviewer for journals including JMLR, JASA, IEEE TSP, IEEE TIP, IEEE TKDE, SIAM Journal on Imaging Sciences, and Optics Express.
Mentoring & Teaching
I have mentored 10+ research interns and students across machine learning, multimodal AI, and sensing AI, with projects leading to publications, patents, open‑source software, and field‑tested prototypes.
Selected Mentored Research
Reliable Knowledge Distillation for Multimodal Models
2025–2026
Led and mentored research on uncertainty-aware knowledge distillation for multimodal models, developing a Bayesian framework that adaptively balances data supervision and teacher guidance.
CVPR 2026 Paper → · NEC Research Story →
Earthquake Localization through Submarine Fiber Sensing
2022–2023
Mentored research on polarization sensing over submarine fiber links, developing a matrix-eigenvalue method to separate and localize disturbances along the fiber, with applications to earthquake localization.
OFC 2023 Paper → · U.S. Patent →
Weakly Supervised Learning from Ambient Traffic
2021–2022
Mentored research on localizing manholes over deployed fiber networks from ambient traffic, developing a deep multiple-instance learning approach that reduces reliance on precise annotations and field surveys.
Teaching
STA 111: Probability and Statistical Inference
Duke University · 2018 · Course Website →
More about my professional background → Experience, Education & Honors