GW Monogram

Department of Statistics

Columbian College of Arts & Sciences

Fang Jin

FangJin

Fang Jin

Associate Professor

Contact:

Email: Fang Jin
https://fangjin.github.io/
801 22nd St NW Washington, DC 20052

2024 – Present, Associate Professor, Department of Statistics, George Washington University
2020 – 2024, Assistant Professor, Department of Statistics, George Washington University
2017 – 2020, Assistant Professor, Department of Computer Science, Texas Tech University

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

  1. Yi Liu, Fang Jin*. From Static Benchmarks to Living Benchmark Ecosystems for Autonomous Agents. 2026 IEEE International Conference on Big Data, Dec. 2026.
  2. Zhiyu Cheng, Fang Jin*. Reliable Sparse Circuit Mining: False Discovery Rate Control and Valid Post-Selection Inference for Language Model Behavior. The IEEE International Conference on Data Mining (ICDM 2026), accepted, Nov. 2026.
  3. Min Yu, Lucas Saenz, Alejandro Lopez Magallon, Craig Futterman, Fang Jin, Marius George Linguraru DPhil, Syed Muhammad Anwar, Ricardo Munoz. Prediction of adverse events in single ventricle physiology infants using artificial intelligence tool. Pediatric Critical Care Medicine (PCCM). Jan. 2026.
  4. Juntao Su, Syed Anwar, and Fang Jin*. ExplainSegNet: Interpretable Segmentation for Alzheimer's Diagnosis. 2025 IEEE International Symposium on Biomedical Imaging. ISBI 2025. April, 2025.
  5. Zhou Yang, Shunyan Luo, Jiazhen Zhu, and Fang Jin*, MASE: Interpretable NLP Models via Model-Agnostic Saliency Estimation. 2025 IEEE International Conference on Big Data. Dec 2025.
  6. Kehan Sui, Jinxu Xiang, and Fang Jin*. SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture. arXiv preprint arXiv:2506.21478. 2025.
  7. Kehan Sui, Jinxu Xiang, and Fang Jin*. RDSinger: Reference-based Diffusion Network for Singing Voice Synthesis. arXiv preprint arXiv:2410.21641, 2024.
  8. Muzhe Guo, ..., and Fang Jin*. Bayesian Iterative Prediction and Lexical-based Interpretation for Disturbed Chinese Sentence Pair Matching, The 2024 ACM International World Wide Web Conference, WWW 2024.
  9. Juntao Su, Zhou Yang, Zexin Ren, and Fang Jin*. Explanation-based Adversarial Detection with Noise Reduction. IEEE BigData 2024 conference. Dec 2024.
  10. Muzhe Guo, Yong Ma, Efe Eworuke, Melissa Khashei, Jaejoon Song, Yueqin Zhao, and Fang Jin*, Identifying COVID-19 cases and extracting patient reported symptoms from Reddit using natural language processing. Nature Scientific Reports 13, 13721, 2023. https://www.nature.com/articles/s41598-023-39986-7
  11. Muzhe Guo, Muhao Guo, Edward Dougherty, Fang Jin*.  MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering. The 2023 ACM International World Wide Web Conference, WWW 2023, pages: 4020-4028. Acceptance Rate: 19%.
  12. Zhou Yang, Ninghao Liu, Xia Ben Hu, and Fang Jin*. Tutorial on Deep Learning Interpretation: A Data Perspective. CIKM 2022, pages: 5156-5159, Oct 2022.
  13. 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, pages: 1609-1612. 2022.
  14. 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. Volume 31(11), pages 2069-2086, June 2022.
  15. 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. page 34. April 2022.
  16. Zhou Yang, Spencer Bradshaw, Rattikorn Hewett, and Fang Jin*. Discovering Opioid Use Patterns from Social Media for Relapse Prevention, IEEE Computer Society. Volume 55(2), pages 23-33, January 2022.
  17. 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, 105088, January 2022.
  18. Shunyan Luo, Emre Barut, and Fang Jin*. Statistically Consistent Saliency Estimation, International Conference on Computer Vision, ICCV 2021, pages: 745-753. Acceptance Rate: 25%. 2021.
  19. 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). Volume 35, No. 13, pages: 12078-12085, May 2021. Acceptance Rate: 21%.
  20. 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. Volume 120, 103866. 2021.
  21. 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), pages: 1613-1617, 2021.
  22. Hashim Abu-Gellban, Yu Zhuang, Long Nguyen, Fang Jin, Zhenkai Zhang. OnlineDC: Leveraging Temporal Driving Behavior to Facilitate Driver Classification. 2021 IEEE International Conference on Big Data (Big Data), pages: 2857-2866. IEEE, 2021 Dec 15.
  23. Hashim Abu-gellban, Long Nguyen and Fang Jin*. GFDLECG: PAC Classification for ECG Signals Using Gradient Features and Deep Learning, Advances in Data Science and Information Engineering: Proceedings from ICDATA 2020 and IKE 2020. Pages: 369-382. 2021.
  24. 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, pages: 565-572, ASONAM 2020. Acceptance Rate: 16%.
  25. Mehdi Jamali, Ali Nejat, Saeed Moradi, Souparno Ghosh, Guofeng Cao, and Fang Jin. Social media data and housing recovery following extreme natural hazards. International Journal of Disaster Risk Reduction. 51:101788. Dec 1, 2020.
  26. 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, pages: 944-947, ASONAM 2020.
  27. 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, pages: 948-951, ASONAM 2020.
  28. 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). Singapore, May 11–14, 2020, Proceedings, Part II 24, pages: 906-917. Acceptance rate 21%. May 2020.
  29. 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, pages: 941-943, ASONAM 2020.
  30. Zhou Yang, Zhenhe Pan, Sisheng Liang, Fang Jin*. Not All Areas Are Equal: Detecting Thoracic Disease with ChestWNet. 2020 IEEE International Conference on Big Data (Big Data), pages: 3447-3452. IEEE, 2020 Dec 10.
  31. Hashim Abu-Gellban, Long Nguyen, Mahdi Moghadasi, Zhenhe Pan, and Fang Jin*. Livedi: An anti-theft model based on driving behavior. In Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security, pages: 67-72. 2020.
  32. 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.
  33. Long Nguyen, Zhou Yang, Jia Li, and Fang Jin*. Forecasting People's Needs in Hurricane Events from Social Network, IEEE Transactions on Big Data, Volume 8(1), pages 229-240, 2019.
  34. 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. pages: 560-567, August 2019. Vancouver, Canada. Acceptance rate 14%.
  35. 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. Volume 24, pages 443-475. 2019.
  36. 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). Proceedings, Part I 23 2019 (pages: 343-354). April 14-17, 2019. Acceptance rate 24%.
  37. 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, Demo. Pages: 202-205, August 2019. Vienna, Austria.
  38. 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. Demo. Pages: 190-193, August 2019. Vienna, Austria.
  39. Hanxiang Du, Long Nguyen, Zhou Yang, Xingyu Zhou‡, and Fang Jin*. Twitter vs News: Concern Analysis of the 2018 California Wildfire Event, 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Volume 2, pages: 207-212. IEEE, July 2019.
  40. 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), Demo. Pages: 613-616, IEEE, 2018.
  41. Sisheng Liang, Long Nguyen, and Fang Jin*. A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting, 2018 IEEE international conference on big data (Big Data), Dec 10, pages: 4561-4564. IEEE, 2018.
  42. 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.
  43. 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, pages: 2019-2026, IEEE, 2017.
  44. 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), pages: 606 -614, Houston, TX, Apr 2017. Acceptance rate: 26%.
  45. 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, pages: 16-30, ICDM 2017.
  46. 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, pages: 2177-2185, IEEE, 2017.
  47. 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.
  48. 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), pages: 1660-1669, Aug 2014. Acceptance rate: 15%. Recipient of KDD 2014 NSF student travel award.
  49. 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), pages: 1-9, Chicago, IL, 2013, pages 8:1-8:9. Recipient of Best Paper Award & Student Travel Award.

  50. 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. 

     

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