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Department of Statistics

Columbian College of Arts & Sciences

Fang Jin

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Fang Jin

Assistant Professor

Contact:

Email: Fang Jin
https://www.fangjinlab.com/
801 22nd St NW Washington, DC 20052

2020 – Present, Assistant Professor, Department of Statistics, George Washington University
2017 – 2020, Assistant Professor, Department of Computer Science, Texas Tech University
2009 – 2011, Software Engineer, Beijing High Performance Computing Center

Deep Learning Interpretation, Explainable AI,  Machine learning, Data mining, Deep Reinforcement Learning, Social Network Analysis

  1. Zhou Yang, Ninghao Liu, Xia Ben Hu, and Fang Jin. Tutorial on Deep Learning Interpretation: A Data Perspective. CIKM 2022. Oct 2022.
  1. Hongfei Du, Si Wen, Yufei Guo, Fang Jin, and Brandon Gallas. Single Reader Between-Cases AUC Estimator with Nested Data , Statistical Methods in Medical Research. June 2022.
  1. Muzhe Guo, Long Nguyen, Hongfei Du, and Fang Jin. When Patients Recover from COVID-19: Data-driven Insights from Wearable Technologies. , Frontiers in Big Data-Medicine and Public Health. April 2022.
  1. Juntao Su, Edward Dougherty, Shuang Jiang and Fang Jin*. An Interactive Knowledge Graph Based Platform for COVID-19 Clinical Research, The 15th ACM International Conference on Web Search and Data Mining, demo paper. WSDM 2022. [Rank No. 4 in Data Mining]
  1. Zhou Yang, Spencer Bradshaw and Fang Jin*. Discovering Opioid Use Patterns from Social Media for Relapse Prevention, IEEE Computer Society. 2022.
  1. Qiyue Wang, Wu Xue, Xiaoke Zhang, Fang Jin, and James Hahn. S2FLNet: Hepatic Steatosis Detection Network with Body Shape, Computers in Biology and Medicine. Volume 140, January 2022, 105088.
  1. Shunyan Luo, Emre Barut, and Fang Jin*. Statistically Consistent Saliency Estimation, International Conference on Computer Vision, ICCV 2021. Acceptance Rate: 25%. 2021. [Rank No. 3 in Computer Vision]
  1. Hongfei Du, Emre Barut, and Fang Jin*. Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks, Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). Feb 2021. Acceptance Rate: 21%. [Rank No. 4 in Artificial Intelligence]
  1. Shang Zhao, Xiaoke Zhang, Fang Jin, and James Hahn. An Auxiliary Tasks Based Framework for Automated Medical Skill Assessment with Limited Data, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021.
  1. Qiyue Wang, Wu Xue, Xiaoke Zhang, Fang Jin, and James Hahn. Pixel-Wise Body Composition Prediction with a Multi-Task Conditional Generative Adversarial Network, Journal of Biomedical Informatics. 2021.
  1. Zhou Yang, Long Nguyen, Zhu Jiazhen, Jia Li, and Fang Jin*. Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning, the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ASONAM 2020. Acceptance Rate: 16%.
  1. Vinay Jayachandra, Rashmi Kesidi, Zhou Yang, Chen Zhang, Victor Sheng, and Fang Jin*. BeSober: Assisting relapse prevention in Alcohol Addiction using a novel mobile app-based intervention., The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Demo, ASONAM 2020.
  2. Zhenhe Pan, Dhruv Mehta, Anubhav Tiwari, Siddhartha Ireddy, Zhou Yang, and Fang Jin*. An Interactive Platform to Track Global COVID-19 Epidemic. , The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Demo, ASONAM 2020.
  1. Hashim Abu-gellban, Long Nguyen and Fang Jin*. GFDLECG: PAC Classification for ECG Signals Using Gradient Features and Deep Learning, Springer Nature – Book Series: Transactions on Computational Science & Computational Intelligence, 2020.
  1. Zhou Yang, Jiwei Xu, and Fang Jin*. COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform. , The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Demo, ASONAM 2020.
  1. Zhou Yang, Long Nguyen, and Fang Jin*. Opioid Relapse Prediction with GAN, the 2019 IEEE/ACM International Conference on Social Networks Analysis and Mining, ASONAM 2019. August 2019. Vancouver, Canada. Acceptance rate 14%. 
  1. Zhou Yang and Fang Jin*. Pneumonia Detection on Chest X-Rays with Deep Learning, 2019 Conference on Computer Vision and Pattern Recognition, CVPR Workshop on Towards Causual, Explanable and Universal Medical Visual Diagnosis. June 16-20, 2019.
  1. Sisheng Liang, Zhou Yang, Fang Jin, and Yong Chen. Data Centers Job Scheduling with Deep Reinforcement Learning, The 24th Pacific-Asia conference on Knowledge Discovery and Data Mining (PAKDD). Acceptance rate 21%. May 2020.
  1. Mehdi Jamali, Ali Nejat, Souparno Ghosh, Fang Jin, and Guofeng Cao. Social Media Data and Post-disaster Recovery, International Journal of Information Management. Volume 44, Pages 25-37, February 2019.  [Rank No. 5 in Information System & Management]
  1. Zhou Yang, Vinay Jayachandra, Rashmi Kesidi and Fang Jin*. Addict Free - A Smart and Connected Relapse Intervention Mobile App, 16th International Symposium on Spatial and Temporal Databases, SSTD 2019. August 2019. Vienna, Austria.
  1. Long Nguyen, Zhou Yang, Jia Li, and Fang Jin*. Forecasting People's Needs in Hurricane Events from Social Network, IEEE Transactions on Big Data, 2019.
  1. Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu and Naren Ramakrishnan. Online flu epidemiological deep modeling on disease contact network, Geoinformatica. 2019.
  1. Long H. Nguyen, Siyuan Jiang‡, Hashim Abu-gellban, Hanxiang Du, and Fang Jin*. NiRec: Need Recommender for Hurricane Disaster Relief, 16th International Symposium on Spatial and Temporal Databases, SSTD 2019. August 2019. Vienna, Austria.
     
  2. Long Nguyen, Jiazhen Zhu, Zhe Lin, Hanxiang Du, Zhou Yang, Wenxuan Guo, and Fang Jin*. Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction, The 23rd Pacific-Asia conference on Knowledge Discovery and Data Mining (PAKDD). Acceptance rate 24%. April 14-17, 2019.
  1. Zhou Yang, Long H. Nguyen, and Fang Jin*. Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning, 1st Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, at 25th ACM SIGKDD conference on knowledge discovery and data mining. KDD 2019 workshop.
  1. Hanxiang, Long Nguyen, Zhou Yang, Xingyu Zhou‡, and Fang Jin*. Twitter vs News: Concern Analysis of the 2018 California Wildfire Event, DADA 2019: The 1st IEEE International Workshop on Deep Analysis of Data-Driven Applications. IEEE. July 2019. 
     
  2. Long H. Nguyen, Rattikorn Hewett, Akbar S. Namin, Nicholas Alvarez‡, Cristina Bradatan, and Fang Jin*. Smart and Connected Water Resource Management via Social Media and Community Engagement, The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018). Barcelona, Spain, 28-31 August 2018. 
  1. Sisheng Liang, Long Nguyen, and Fang Jin*. A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting, IEEE BigData 2018, Big Data Engineering and Analytics in Cyber-Physical Systems (BigEACPS) workshop. Dec 10-13, 2018. Seattle, WA, USA. 
  1. Vinh T. Nguyen, Tommy Dang, and Fang Jin*. Predict Saturated Thickness using TensorBoard Visualization, Visualization in Environmental Sciences 2018 (EnvirVis 2018). Brno, Czech Republic. June 4-8th, 2018.
  1. Zhou Yang, Long Nguyen, Joshua Stuve‡, Guofeng Cao, and Fang Jin*. Harvey Flooding Rescue in Social Media, Proceedings of the IEEE International Conference on Big Data, Boston, Dec. 11-14, 2017. 
  1. Long Nguyen, Andrew Salopek‡, Liang Zhao and Fang Jin*. A Natural Language Normalization Approach to Enhance Social Media Text Reasoning, Proceedings of the IEEE International Conference on Big Data, Boston, Dec. 11-14, 2017. 
  1. Fang Jin, Wei Wang, Prithwish Chakraborty, Nathan Self, Feng Chen, Naren Ramakrishnan. Tracking Multiple Social Media for Stock Market Event Prediction, Industrial Conference on Data Mining, ICDM 2017. 
     
  2. Fang Jin, Feng Chen, Rupinder Khandpur, Chang-Tien Lu, Naren Ramakrishnan. Absenteeism Detection in Social Media, in Proceedings of the SIAM International Conference on Data Mining (SDM'17), Houston, TX, Apr 2017. Acceptance rate: 26%. 
  1. Fang Jin. Algorithms for Modeling Mass Movements and their Adoption in Social Networks, Dissertation. Arlington, VA. June 2016.
     
  2. Fang Jin, Rupinder Khandpur, Nathan Self, Edward Dougherty, Sheng Guo, Feng Chen, B. Aditya Prakash, Naren Ramakrishnan. Modeling Mass Protest Adoption in Social Network Communities using Geometric Brownian Motion, in Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'14), Aug 2014. Acceptance rate: 15%. Recipient of KDD 2014 NSF student travel award.  [Rank No. 1 in Data Mining & Analysis]
  1. Fang Jin, Wei Wang, Liang Zhao, Edward Dougherty, Yang Cao, Chang-Tien Lu, Naren Ramakrishnan. Misinformation Propagation in the age of Twitter, IEEE Computer, Volume 47, Issue 12, pages 90-94, Dec 2014. 
     
  2. Fang Jin, Edward Dougherty, Parang Saraf, Peng Mi, Yang Cao, and Naren Ramakrishnan. Epidemiological modeling of news and rumors on twitter, in Proceedings of the 7th ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD 2013), Chicago, IL, 2013, pages 8:1-8:9. Recipient of Best Paper Award & Student Travel Award. 
     
  3. Fang Jin, Nathan Self, Parang Saraf, Patrick Butler, Wei Wang, Naren Ramakrishnan. Forex-Foreteller: Currency Trend Modeling using News Articles, in Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - Demo Track, pages 1470--1473, Aug 2013.
  4. Book chapter: Edward A. Fox, Monika Akbar, Sherif Hanie El Meligy Abdelhamid, Noha Ibrahim Elsherbiny, Mohamed Magdy Gharib Farag, Fang Jin, Jonathan P. Leidig, Sai Tulasi Neppali. Computing Handbook, Third Edition, Vol. 2 (Information Systems and Information Technology). Section 3, Ch. 18, ed. by Heikki Topi, Allen Tucker, Chapman & Hall/CRC Press, Taylor and Francis Group, ISBN 9781439898444, http://www.crcpress.com/product/isbn/9781439898543, May 2014.

 

Ph.D. Computer Science, Virginia Tech, 2016
Master. Chinese Academy of Sciences, 2009