Xi Chen (陈熹)

xc.JPG

CoRE 735, Busch Campus

Rutgers University

Piscataway, NJ, 08854

I am a 3rd year Ph.D. candidate advised by Prof. Shirin Jalali at the Electrical and Computer Engineering Department of Rutgers University. My background is in inverse problems, generative models, computational imaging and statistical machine learning.

I am particularly interested in developing denoisers/regularizers (e.g. generative models: VAE, Diffusion models; image implicit/explicit priors: sparsity, DIP, NeRF etc.), under an optimization framework (e.g. ADMM, Plug-and-Play, SURE, RED etc.), to solve inverse problems in 2D/3D imaging systems (e.g. coherent imaging, snapshot compressive imaging etc.), with theoretical guarantees, and understandings of the fundamental limits on the achievable recovery performance from the information theoretic and high-dimensional perspective.

I received my M.Sc. degree in Data Science from Tufts University, MA, worked with Prof. Mike Hughes and Liping Liu. I received my B.Sc. degree in Electrical Engineering from Beijing Institute of Technology, China, worked with Prof. Jie Tian.

Email: firstname.chen15 [at] rutgers [dot] edu

news

Nov 04, 2024 Recognized as a Top Reviewer by NeurIPS 2024.
Sep 30, 2024 Invited as a reviewer of AISTATS 2025.
Sep 25, 2024 Our paper is accepted by NeurIPS 2024.
Aug 15, 2024 Invited as a reviewer of ICLR 2025.
Jun 17, 2024 Glad to receive the SPIE student travel award to present our work on novel approach for coherent imaging system in SPIE Optics + Photonics 2024, San Diego.
May 24, 2024 Invited as a reviewer of NeurIPS 2024.
May 20, 2024 Presented our work on compressive coherent imaging system at Conference on Inverse Problems for Partial Differential Equations.
May 01, 2024 Our paper is accepted by ICML 2024.
Apr 04, 2024 Presented our work on image acquisition with speckle noise on Columbia Data Science Day.
Mar 15, 2024 Invited as a reviewer of ICML 2024.
Feb 09, 2024 Invited as a reviewer of ISIT 2024.
Dec 16, 2023 Presented our work in the NeurIPS Deep learning and Inverse Problems workshop.
Aug 13, 2023 Invited as a reviewer of IEEE Transaction on Information Theory.

selected publications

  1. SPIE
    Novel approach to coherent imaging in the presence of speckle noise
    Xi Chen, Christopher A Metzler, Arian Maleki, and Shirin Jalali
    In Unconventional Imaging, Sensing, and Adaptive Optics 2024 , 2024
  2. NeurIPS
    Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms
    Mengyu Zhao, Xi Chen, Xin Yuan, and Shirin Jalali
    In Advances in Neural Information Processing Systems , 2024
  3. ICML
    Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise
    Xi Chen, Zhewen Hou, Christopher A Metzler, Arian Maleki, and Shirin Jalali
    In International Conference on Machine Learning , 2024
  4. NeurIPSW
    Multilook compressive sensing in the presence of speckle noise
    Xi Chen, Zhewen Hou, Christopher Metzler, Arian Maleki, and Shirin Jalali
    In NeurIPS 2023 Workshop on Deep Learning and Inverse Problems , 2023
  5. TMLR
    Interpretable Node Representation with Attribute Decoding
    Xiaohui Chen, Xi Chen, and Li-Ping Liu
    Transactions on Machine Learning Research, 2022
  6. Journal
    Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer
    Qingxia Wu, Shuo Wang, XI Chen, Yan Wang, Li Dong, and 3 more authors
    Radiotherapy and Oncology, 2019
  7. SPIE
    Radiomics analysis on T2-MR image to predict lymphovascular space invasion in cervical cancer
    Shou Wang, Xi Chen, Zhenyu Liu, Qingxia Wu, Yongbei Zhu, and 2 more authors
    In Medical Imaging 2019: Computer-Aided Diagnosis , 2019
  8. ISMRM
    Radiomics Analysis of tumor and peri-tumor tissue on T2-Weighted Imaging Improves Diagnostic Performance of Lymph Node Metastasis in Patients with Cervical Cancer
    Qingxia Wu, Shuo Wang, Xi Chen, Yan Wang, Yusong Lin, and 1 more author
    International Society for Magnetic Resonance in Medicine, 2019
  9. EMBC
    Unsupervised deep learning features for lung cancer overall survival analysis
    Shuo Wang, Zhenyu Liu, Xi Chen, Yongbei Zhu, Hongyu Zhou, and 5 more authors
    In 2018 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC) , 2018