I am a Senior Researcher at NEC Laboratories America, where I work on the design and development of machine learning and signal processing algorithmic solutions for real-world sensing applications. I’m interested in generalizable AI methods that can address limitations in training data acquisition and domain shifts in deployment environments. I received my Ph.D. in Electrical and Computer Engineering and M.S. in Statistical Science from Duke University. My Ph.D. advisor was Lawrence Carin. I am a Senior Member of IEEE.
His research interest includes:
[2024/12] Two papers were accepted to ICASSP 2025, and four papers were accepted to OFC 2025. The topics include (i) disaggregated inference and fine-tuning over optical networks, and (ii) the adaptation of large-scale pre-trained audio language models to downstream tasks.
[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.
[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.