Biography

Hello, welcome to my homepage! 

I have been a research fellow at the National Library of Medicine since 2024, where I bring a wealth of experience and a deep passion for the intersection of machine learning and medicine. Before joining NLM, I was part of the Amazon Forecasting team from April 2021 to 2023, where I specialized in the development and launch of advanced time series forecasting systems. My journal as an Amazonian starts from AWS AI Labs in September 2021, focusing on pioneering work in NLP-based search engines and recommendation systems. Since then, I started the new research projects such as Neural Networks based NLP and bandits. My enthusiasm for search technologies is deeply rooted in my three-year postdoctoral experience at Rutgers University, where I explored the theory and algorithms of hashing and approximate nearest neighbor search. This experience solidified my expertise in AI and its applications in complex problem-solving. In the medical field, my work has been equally transformative. I held positions as a postdoctoral associate in the Department of Radiology at Cornell Medical College under Dr. James K. Min, and as a postdoctoral fellow in the Icahn School of Medicine at Mount Sinai within the Huang Lab|Computational Omics. There, I leveraged neural networks to analyze cardiac imaging and detect coronary thrombosis and applied machine learning to identify genes associated with Alzheimer's Disease. My academic journey also included a significant stint from 2013 to 2015 as a visiting student in the Department of Electrical and Computer Engineering at the National University of Singapore, under the guidance of Shuicheng Yan and Huan Xu. These experiences have not only honed my expertise in AI but have also ignited my ongoing passion for using technology to solve real-world challenges, making each step of my career deeply rewarding.

My research interests are mainly about time series forecasting, natural language processing, feature selection, and approximate nearest neighbor search. Here are my Google Scholar profile, Github profile, and the new website.

News

• May 2022: One paper about algorithmic fairness accepted to ICML 2022.

• March 2022: One paper about approximate nearest neighbor search accepted to Machine Learning.

Working papers:

1. Time series forecasting with LLM, in submission.

2. Search in healthcare, in submission.

3. LLM for data annotation, in progress.

Publications

18. "Uncertainty-Based Active Learning for Reading Comprehension"

    J. Wang,  J. Shen, Xiaofei Ma, Andrew Arnold 

    Transactions on Machine Learning Research, 2022  (TMLR)  

    [link] [bibTex]

17. "Metric-Fair active learning"

     J. Shen, C. Nan, J. Wang

    The 39th International Conference on Machine Learning, 2022  (ICML)  

    [link] [bibTex]

16. "Fast spectral analysis for approximate nearest neighbor search"

     J. Wang, J. Shen

    Machine Learning, 2022 

    [link] [bibTex]

15. "Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing"

     PloS one, 15 (7), 2020

     With M. Pandey, Z. Xu, James K. Min and others

     [link][bibTex]

14. "Deep learning based automatic segmentation of cardiac computed tomography"

      Journal of the American College of Cardiology 73 (9S1), 1643-1643, 2019

      With G. Singh, S. J. Alaref, James K. Min and others

      [link][bibTex]

13. "Determinants of in-hospital mortality after percutaneous coronary interventions: a machine learning approach"

      Journal of the American Heart Association, 8 (5), 2019

      With S. J. Alaref, G. Singh, James K. Min and others

      [link][bibTex]

12. "TCT-55 Clinical Predictors of Obstructive Coronary Artery Disease in Individuals with Suspected Coronary Artery Disease"

      Journal of the American College of Cardiology, B24-B25, 72 (13S), 2018

     With S. J. Alaref, A. Rosendael, James K. Min and others

     [link][bibTex]

11. "Machine Learning in cardiac CT: basic concepts and contemporary data"

      Journal of Cardiovascular Computed Tomography 12 (3), 192-201, 2018

      With G. Singh, S. J. Alaref, James K. Min and others

      [link][bibTex]

10. "Provable Variable Selection for Streaming Features"

     J. Wang, J. Shen, P. Li

    The 35th International Conference on Machine Learning, 2018  (ICML)  

    [link] [bibTex]

9. "Object Proposal with Kernelized Partial Ranking"    

     J. Wang, J. Shen, P. Li

    Pattern Recognition, 69(1): 299-309, 2017  (PR)

    [link] [arXiv] [code] [bibTex] 

8. "Online Matrix Completion for signed Link Prediction"

    J. Wang, J. Shen, P. Li, H. Xu

    The 10th ACM International Conference on Web Search and Data Mining, 475-484, 2017   (WSDM)

    [link] [code] [bibTex] 

5. "Robust Face Recognition via Adaptive Sparse Representation"

    J. Wang, C. Lu, M. Wang, P. Li, S. Yan, X. Hu

    IEEE Transactions on Cybernetics, 44(12): 2368-2378, 2014   (TSMC-B)

    [link] [code] [bibTex]

4. "Online Group Feature Selection"

    J. Wang, Z. Zhao, X. Hu, Y. Cheung, M. Wang, X. Wu

    International Joint Conference on Artificial Intelligence, 1757-1763, 2013   (IJCAI)

    [link] [code] [bibTex]

3. "Online Learning towards Big Data Analysis in Health Informatics"

    J. Wang, Z. Zhao, X. Hu, Y. Cheung, H. Hu, F. Gu

    Proceedings of the 2013 International Conference on Brain and Health Informatics, 516-523, 2013  (BHI)

   [link][bibTex]

2. "ApLeafis: an Android-based Plant Leaf Identification System"

    L. Ma, Z. Zhao, J. Wang

    Proceedings of the 2013 International Conference on Intelligent Computing, 106-111, 2013  (ICIC)

    [link][bibTex]

1. "A Heuristic Algorithm for Scheduling on Grid Computing Environment"

    J. Wang, G. Wu, X. Hu

    Proceedings of the 2012 ChinaGrid Annual Conference, 36-42, 2012  (ChinaGrid)

    [link][bibTex]

Academic Services

• Conference Reviewer: AISTATS 2022, ICML 2022, ICLR 2022, AISTATS 2022, NeurIPS 2021, ICML 2021, IJCAI 2021, ICLR 2021, ICML 2020, NeurIPS 2019, NeurIPS 2018, AAAI 2018.

• Journal Reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Neural Networks and Learning Systems (TNNLS).