Jing Wang

Applied Scientist

Amazon

NYC, NY

I joined Amazon (Forecasting and Amazon Web Services) in 2019. At Forecasting team, I have developed an end-to-end time-series forecasting system for the global retail business. Between 2019 to 2021, I worked on natural language processing, search engine and recommendation service at AWS AI Labs. The high performance services attract clients from finance, media and retail.

Prior to joining Amazon, I spent three years as a postdoctoral researcher at the Department of Statistics in Rutgers University. From 2018 to 2019, I worked at the intersection of Healthcare and machine learning in Cornell Medical School and Icahn Genomics Institute.

I have spent two years at National University of Singapore when pursuing my Ph.D. degree in computer science.

news

May 22, 2023	Attend Center for Approximation and Mathematical Data Analytics at Texas A&M University.
Nov 29, 2022	Attend Neurips.
Nov 16, 2022	Attend Harvard Data Science Initiative Conference.
May 11, 2022	Paper Fairness accepted to ICML’22. Congratulations to Nan and Jie!
Mar 10, 2022	Paper Approximate nearest neighbor search accepted to ML. Congratulations to Jie!
Apr 12, 2021	Start the new position at Amazon Forecasting team.

selected publications

ICML

Metric-Fair Active Learning

Jie Shen, Nan Cui, and Jing Wang

International Conference on Machine Learning 2022

Bib

@article{shen2022metric,
  title = {Metric-Fair Active Learning},
  author = {Shen, Jie and Cui, Nan and Wang, Jing},
  journal = {International Conference on Machine Learning},
  volume = {18},
  number = {6},
  pages = {403--404},
  year = {2022},
  publisher = {American Association of Physics Teachers},
}

Fast spectral analysis for approximate nearest neighbor search

Jing Wang, and Jie Shen

Machine Learning 2022

Bib

@article{wang2022fast,
  title = {Fast spectral analysis for approximate nearest neighbor search},
  author = {Wang, Jing and Shen, Jie},
  journal = {Machine Learning},
  pages = {1--26},
  year = {2022},
  publisher = {Springer},
}

JACC

Deep learning based automatic segmentation of cardiac computed tomography

Gurpreet Singh, Subhi Alaref, Gabriel Maliakal, Mohit Pandey, and 5 more authors

Journal of the American College of Cardiology 2019

Bib

@article{singh2019deep,
  title = {Deep learning based automatic segmentation of cardiac computed tomography},
  author = {Singh, Gurpreet and Alaref, Subhi and Maliakal, Gabriel and Pandey, Mohit and van Rosendael, Alexander and Lee, Benjamin and Wang, Jing and Xu, Zhouran and Min, James},
  journal = {Journal of the American College of Cardiology},
  volume = {73},
  number = {9S1},
  pages = {1643--1643},
  year = {2019},
  publisher = {American College of Cardiology Foundation Washington, DC},
}

ICML

Provable variable selection for streaming features

Jing Wang, Jie Shen, and Ping Li

2018

Bib

@inproceedings{wang2018provable,
  title = {Provable variable selection for streaming features},
  author = {Wang, Jing and Shen, Jie and Li, Ping},
  booktitle = {International Conference on Machine Learning},
  pages = {5171--5179},
  year = {2018},
  organization = {PMLR},
}

WSDM

Online matrix completion for signed link prediction

Jing Wang, Jie Shen, Ping Li, and Huan Xu

2017

Bib

@inproceedings{wang2017online,
  title = {Online matrix completion for signed link prediction},
  author = {Wang, Jing and Shen, Jie and Li, Ping and Xu, Huan},
  booktitle = {Proceedings of the Tenth ACM International Conference on Web Search and Data Mining},
  pages = {475--484},
  year = {2017},
}