Lingkai Kong

Lingkai Kong

Ph.D. student

Geogira Institue of Technology

About Me

Welcome to Lingkai Kong (孔令恺)’s homepage! I am a Ph.D student in the School of Computational Science and Engineering at Georgia Institute of Technology. I am working with Prof. Chao Zhang. I recieved my B.E. in Information Engineering from Southeast University.

My research spans across machine learning, natural language processing and data mining. I am particularly interested in making trustworthy intelligent system in open-world settings. Toward this goal, I am currently working on the following research thrusts:

  • Uncertainty quantification and out-of-distribution detection in deep learning;
  • Out-of-distribution generalization and zero shot learning;
  • Application of uncertainty-aware deep learning in natural language processing and healthcare.

Email: lkkong [at] gatech [dot] edu

Education


  • Georgia Institute of Technology, Atlanta, USA
    Ph.D. in Computational Science and Engineering, 2019 - present
  • Georgia Institute of Technology, Atlanta, USA
    Ph.D. in Electrical and Computer Engineering (transferred to CSE), 2017 - 2019
  • Southeast University, Nanjing, China
    B.E. in Information Engineering, 2013 - 2017

Preprint


  • CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B Aditya Prakash
    [Paper] [Code]

Publication


  • When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B Aditya Prakash
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    [Paper] [Code]

  • Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
    Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang
    Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
    [Paper] [Code]

  • SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
    Lingkai Kong, Jimeng Sun and Chao Zhang
    International Conference on Machine Learning (ICML), 2020
    [Paper] [Code] [Video]

  • Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
    Yisen Wang, Bo Dai, Lingkai Kong, Sarah Erfani, James Bailey and Hongyuan Zha
    Conference on Uncertainty in Artificial Intelligence (UAI), 2018
    [Paper]

  • Wide-range Dimmable Clipped Flip-OFDM For Indoor Visible Light Communication
    Liang Wu, Lingkai Kong, Zaichen Zhang, Jian Dang and Huaping Liu
    IEEE/CIC International Conference on Communications in China (ICCC), 2018
    [Paper]

  • A Novel OFDM Scheme for VLC Systems under LED Nonlinear Constraints
    Lingkai Kong, Congcong Cao, Siyuan Zhang, Mengchao Li and Liang Wu
    EAI International Conference On Communications and Networking in China (ChinaCom), 2016
    [Paper]

Experience


  • Amazon, Seattle, May 2021 - Nov 2021
    Applied Scientist Intern, Product Graph Team
    Mentor: Xiang He, Chenwei Zhang; Manager: Luna Xin Dong
  • IQVIA, Cambridge, June 2020 - Aug 2020
    Research Intern, Analytics Center of Excellence
    Mentor: Danica (Cao) Xiao

Award


  • Otto & Jenny Krauss Fellowship, Georgia Tech, 2017
  • Outstanding undergraduate thesis, Southeast University (Top 5%), 2017

Academic Services


Program Committee/Reviewer: EACL 2021, EMNLP 2020, KDD 2020, ACL 2020, AAAI 2020, CIKM 2019

Teaching


  • Teaching Assistant, CSE8803 Deep Learning for Text Data, Fall, 2020
  • Teaching Assistant, CSE8803 Deep Learning for Text Data, Fall, 2019
  • Teaching Assistant, CS7641 Machine Learning (Online), Spring, 2019
  • Teaching Assistant, CS7641 Machine Learning (Online), Fall, 2018