• Dr. Qi (Roger) She


    Intel Labs

    I am currently a research scientist in Intel Labs, China focusing on statistical machine learning, deep learning with applications in robotic vision. I obtained my Ph.D. degree from the Department of Electronic Engineering at the City University of Hong Kong where I was advised by Prof. Rosa H.M. Chan. During 2017, I was a Visiting Student Research Collaborator (VSRC) at Princeton University, advised by Prof. Jonathan Pillow. I also worked as a research intern in Tencent AI Lab focusing on computer vision project.


    Previously, my research focused on statistical machine learning methods to extract hidden structure from high-dimensional neural data, infer brain connectivity using fully & empirical Bayes, and complex network study on multiple brain regions. I am interested in studying how information is encoded, decoded, and processed in our brain. Currently, motivated from brain computing, I would prefer to develop lifelong/continual adapation agent that can shape a cultivated understanding of the world from the current scene and their previous knowledge via an autonomous lifelong development. The ongoing project is listed in "Lifelong Robotic Vision" project page.


    Before directly pursuing my Ph.D., I completed my B.Eng. within 1% (2/230) in Information Engineering in September 2014, and my bachelor final year project ranked #1 out of 230.

  • Experiences

    Intel Labs

    Senior Research Scientist, 2018/8-Current

    • Statistical Machine Learning
    • Deep Learning (computer vision area)
    • Lifelong/Continual Learning in Human Robot Interaction

    Tencent AI Lab

    Research Intern, 2017/12-2018/3

    • Established deep generative models for high-dimensional time series data, aiming at retrieving the intrinsic latent structure of the large-scale dataset.
    • Explored interpretable and intuitive model & tractable and efficient inference method for prediction and visualization of data.

    Princeton Neuroscience Institute, Princeton University

    Visiting Student, 2017/3-2017/12

    • Developed advanced statistical machine learning algorithms for neural data, including stochastic dynamical system, Bayesian inference.
    • Extracted interpretable latent structure of Motor Neuronal Spikings via RNN dynamics and Gaussian Process nonlinear embeddings.

    Department of Electronic Engineering, City University of Hong Kong

    Ph.D. Student, 2014/9-2018/8

    • GPA: 4.22/4.3
    • Ph.D. Thesis: Flexible and interpretable multivariate point processes for neural dynamics
    • Field: Neural Computation and Machine Learning
    • Outstanding Academic Performance Award (2016)
    • Research Tuition Scholarship (2017-2018)
    • One Research Activities Fund and Two Conference Grants (UGC funded)

    Department of Information Engineering, Peking University

    Exchange Student, 2013-2014

    • Designed new optical structure for projection
    • Accomplished final-year project with best graduate thesis award in NUPT.

    Nanjing University of Posts and Telecommunications

    B.Eng. 2010/9-2014/6

    • GPA: 93/100 (Ranking 2/230)
    • GRE: 320; TOEFL: 110 
    • 1st Prize in National Undergraduate Mathematical Contest in Modeling
    • Excellent Graduation Award (Top 1%)
    • Excellent Final Year Project Award (1st place in the college)
  • Current Research

    Extracting Interpretable Latent Structure from Neural Spike Trains

    We show how to capture the intrinsic dynamics and learn a concise, structured, and interpretable latent space.

    NIPS 2018 (submitted), ICASSP 2018, AAAI 2018

    Network Modeling of Spike Counts


    We show how to model over-dispersion of count data under population settings with Generalized Linear Models


    Submitted to IEEE Transactions on Signal Processing

    Graph-theoretic Analysis of Multi-neuronal recordings


    We apply graph theory to analyze the latent structure of neural data., giving insight into the graph-structured time series data.


    Scientific Reports

    Neural Dyanamics Modeling Using Kernal Generalized Linear Models


    We show how to capture linear-nonlinear neuronal interactions with kernel-GLMs


    EMBC 2015, 2016, 2018

    Time-varying network modeling using Polya-Gamma augmentation



    In preparation

  • My Hobbies




    起初因为想锻炼身体爱上了这种运动,后来渐渐发现这种运动带给自己的不仅仅是健硕的身体,更多的是精疲力竭后内心的平静,什么都不想的感觉。Swimming is really wonderful !





  • News

    Interesting Life !