Shaobo Han is a Senior Researcher at NEC Laboratories America, working on the design and development of machine learning and signal processing algorithmic solutions for real-world sensing applications. He is interested in generalizable AI methods that can cope with limitations in data acquisition and domain shifts in deployment environments. He received Ph.D. in Electrical and Computer Engineering and M.S. in Statistical Science from Duke University. His Ph.D. advisor was Lawrence Carin.
His research interest includes:
[2024/09] Our paper, “VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks”, has been accepted at NeurIPS 2024. The code has been merged into the Hugging Face PEFT library.
[2024/05] Our paper “Deep learning-based intrusion detection and impulsive event classification for distributed acoustic sensing across telecom networks” has been accepted by Journal of Lightwave Technology.
[2023/08] UAI 2023 Top Reviewer.
[2023/05] Our work on fiber sensing AI was featured by Laser Focus World: Top Stories and Photonics Hot List.
[2023/05] Guest Lecture: Automatic Differentiation and Differentiable Programming, In CS 610: Data Structure and Algorithms, NJIT.
[2023/02] Our paper “Ambient noise-based weakly supervised manhole localization methods over deployed fiber networks” has been accepted by Optics Express.
[2022/09] Our paper Using Global Existing Fiber Networks for Environmental Sensing" has been accepted by Proceedings of the IEEE.
[2022/07] Received Outstanding Performance Award 2022, Global Innovation Unit, NEC Corporation.
[2022/01] Two papers accepted to ICLR 2022. Topics include (i) test-time policy adaptation under ordinal reward, and (ii) zero-shot domain adaptation with multiway categorical domain descriptor.
[2021/06] Two AI-based fiber sensing solutions have been commercialized: NEC’s press release.