Kevin Xia

About Me

I'm a computer scientist from Cupertino, California. I'm currently doing my PhD in causal inference at Columbia University, advised by Elias Bareinboim. Previously, I obtained a B.S. in CS, Math, and Statistics at Purdue University. I'm interested in causal structure representations, their generalizability to different domains, and their potential role in deep learning.

My research is supported by the Columbia Center of Artificial Intelligence Technology (CAIT) Amazon PhD Fellowship.

You can contact me via email at kmx2000 [at] columbia [dot] edu.

Blog

Read about my thoughts on causality on my blog (in collaboration with Hannah Ho).

Link to blog: Counteractual

Latest Posts

• "A Thin Line between Chance and Responsibility", October 3rd, 2022
  
• "How 'Rich' is your Question?", August 17th, 2020
  
• "Introducing Counteractual", March 19th, 2020

Publications

2023

• Neural Causal Models for Counterfactual Identification and Estimation
  Xia, K., Pan, Y., and Bareinboim, E.
  (ICLR-23) In Proceedings of the 11th Eleventh International Conference on Learning Representations, Feb 2023 [pdf, arXiv]
  

2022

• Causal Transportability for Visual Recognition
  Mao, C.*, Xia, K.*, Wang, J., Wang, H., Yang, J., Bareinboim, E., and Vondrick, C.
  (CVPR-22) In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Apr 2022 [pdf, arXiv]
  

*Indicates equal contribution.

2021

• The Causal-Neural Connection: Expressiveness, Learnability, and Inference
  Xia, K., Lee, K., Bengio, Y., and Bareinboim, E.
  (NeurIPS-21) In Proceedings of the 34th Annual Conference on Neural Information Processing Systems, Jun 2021 [pdf, arXiv]
  

Pre-PhD

• An S-band Automatically Tunable Bandpass Filter Based on a Machine Learning Approach
  Adhikari, P., Xia, K., Shaffer, G., Ribeiro, B., and Peroulis D.
  2021 IEEE 21st Annual Wireless and Microwave Technology Conference, Apr 2021
  
• Development of a Speed Invariant Deep Learning Model with Application to Condition Monitoring of Rotating Machinery
  Lee, W.J., Xia, K., Denton, N.L., Ribeiro, B., and Sutherland, J.W.
  Journal of Intelligent Manufacturing, May 2020
  
• A Distributed Kernel SVM Algorithm for Predicting Non-Canonical MicroRNA Targets and Verified Using TCGA-derived Expression Data
  Ghoshal, A., Zhang, J., Roth, M.A., Xia, K.M., Grama, A., and Chaterji, S.
  IEEE/ACM Transactions on Computational Biology and Bioinformatics, Apr 2018