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Fang Jin
Assistant professor
Department of Computer Science
Texas Tech University
Email: [email protected]
Research Interests
My research area is Machine Learning and Data Mining. Most recent work has been
focused on Information propagation modeling, graph mining, group anomaly detection,
and spatiotemporal data analysis. Examples of typical applications include detecting
disease outbreaks using public health data such as hospital visits and medication sales;
the detection and prediction of civil unrest events using historical crime records and
streaming Twitter data; and detecting rumors in social networks; and social media data to
detect traffic congestion, excessive air pollution, and power outages.
Employment
Assistant Professor
Department of Computer Science, Texas Tech University.
Jan 2017 to Present
Research Scientist
Department of Electrical & Computer Engineering, Virginia Tech.
Aug 2016 to Dec 2016
Software Engineer
Beijing High Performance Computing Center, Beijing, China.
Jul 2009 to Aug 2011
Education
Ph.D. Computer Science
Virginia Tech, Jan 2012 to Jun 2016
M.S.
Information processing
Chinese Academy of Science, 2009
B.S.
Electronics Science & Technology
Nanjing University of Posts
Telecommunications, China, 2006
and
Publications
1. Fang Jin, Feng Chen, Rupen Paul, 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.
2. Fang Jin, Wei Wang, Prithwish Chakraborty, Nathan Self, Feng Chen, Naren
Ramakrishnan. Tracking Multiple Social Media for Stock Market Event Prediction.
Under review, ICDM, 2017.
3. Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu,
Naren Ramakrishnan. Social Media-driven Online Epidemics Modeling by Adaptive
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Semi-supervised Multilayer Perceptron, ACM Transactions on Knowledge Discovery
from Data (TKDD), 2016, submitted.
4. Fang Jin, Rupinder Paul 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. Recipient of KDD 2014 NSF student travel
award.
5. 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, Chicago, IL, 2013, pages 8:1-8:9. Recipient of Best Paper Award,
Recipient of Student Travel Award.
6. 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.
7. 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.
8. Yan Wang, Guofu Wang, Wenchuan He, Feng Gao, Jiang Zhu, Mingnong Feng, Fang
Jin. Tiered storage technology of meteorological spatial data. Chinese Meteorological
Society & Meteorological Communications and Information Technology Committee
Scientific Meeting, 2011.
9. Min Wei, Lanning Wang, Fang Jin. The implementation of coupling algorithm for
MOM4 and BCC_CSM Model, in Information Science and Engineering (ICISE), 2010
2nd International Conference on, pp. 1600-1604. IEEE, 2010.
10. Fang Jin, Hongqun Zhang, Xiaoqing Ge. Research of Fault Diagnosis Expert System
for Satellites Receiving System. Microcomputer Information, 7 (2009): 107-108.
Book Section
1.
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.
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Research Experience
Analyze the Influence of Climate Change on Civil Unrest
Jul 2015 to Jul 2016
Climate changes significantly affect people's behavior, potentially exacerbating social and
politics unrest. This project seeks to identify climate-related unrest events in Latin American by
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investigating their generation, development and evolution in both real-world and social media
networks. Some highlights of this research include:
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Design document classifier to identify historical climate events using heterogeneous
nearest neighbor strategy. Providing a pre-defined climate events description pool, the
classifier is able to learn their similarities (semantically, temporally and spatially) and
integrate these similarities with SVD to determine a new event's category.
Improve StoryTelling algorithms capable of infering specific information chains related to
a specific climate event and then track its generation, development, and evolution.
These enhanced StoryTelling algorithms will automatically highlight an individual climate
event's causalities.
Develop dynamical query model to investigate information propagation related to specific
events in social media,making it possible to mitigate the effects of unrest by blocking key
players with subgraph optimization aglotithms.
Forecasting Civil Unrest Events in Latin American
Jan 2015 to Jun 2015
This project sought to understand and quantify the way ideas are spread to provide the basis for
future research in this area by modeling and predicting the movement of information within
social media outlets like Twitter.
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Adapt geometric Brownian motion and traditional network graph theory to quantify the
stochastic nature of Twitter topic propagation. A model of civil unrest propagation pattern
in social media, and a design simulation algorithm for civil unrest event prediction were
created.
A new algorithm to automatically extract protest keywords dynamically from social media
was designed to identify those indicators closely linked to civil unrest, such as group
absenteeism signals. New models were developed from scratch to predict civil unrest
events’ location, time, group size, and event type.
Ensemble multiple models, by depressing potential negatve warnings and boosting
positive warnings, or rewriting one or more warning properties like population or event
type, to improve prediction performance.
Forecasting Disease Outbreaks (Ebola, influenza)
Jan 2014 to Dec 2014
Modern epidemiological forecasts of common illnesses are difficult because of the delays
associated with traditional surveillance sources and digital surveillance data such as social
network activity and search queries. This project aims to develop robust quantitative predictions
of temporal trends of epidemiological disease incidence using several surrogate data sources
for Latin American countries.
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Design algorithms to extract related features from social media data like Twitter,
Facebook, news/blogs, twitter-urls and Wikipedia, and new frameworks created to
streamline the resulting large data flows.
Integrating social indicators and physical indicators leveraged the selective superiorities
of both types of feature sets, making it possible to develop matrix factorization models
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using neighborhood embedding to forecast disease outbreaks based on a combination
of social indicators and physical indicators like weather data.
Investigate the efficacy of combining diverse different sources at two levels, the data
level and the model level, were investigated.
Forecasting Stock Market Fluctuations using Multiple Social Media Sources Jul 2012 to
Dec 2013
The rapid growth of highly diverse forms of social media has enabled economists to leverage
micro- and real-time indicators related to the factors that could possibly influence the market,
such as public emotion, anticipation and behavior. By mining specific market features from
varied sources such as news, Google Trends and Twitter, this project investigated the
correlations between these features and stock market fluctuations, and constructed a prediction
model that combined all those features.
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Design text mining algorithms to study extreme fluctuations in Latin American stock
markets through analyzing the patterns in news sources. The new algorithms focused on
learning topic distributions and identifying sentiment patterns in the news for various
extreme fluctuation scenarios.
Apply group Lasso to identify the most informative terms from Google Trends, construct
a tweet entities network, and apply a one class SVM to learn the anomaly patterns in this
tweet entity network.
Build stock market fluctuations model by combining the features learned from the news,
Google Trends and Twitter using feature level fusion and model level fusion.
Honors and Awards
1. NSF student travel award, the 20th ACM SIGKDD Conference on Knowledge Discovery
and Data Mining (KDD 2014), New York, Aug 2014.
2. Best paper award, the 7th ACM SIGKDD Workshop on Social Network Mining and
Analysis (SNA-KDD 2013), Chicago, IL, Aug 2013.
3. Student Travel Award, the 7th ACM SIGKDD Workshop on Social Network Mining and
Analysis (SNA-KDD 2013), Chicago, IL, Aug 2013.
4. Honor student and excellent student cadre of Graduate University of Chinese Academic
of Sciences (GUCAS), 2008.
5. Honor student and excellent student cadre of GUCAS (the only one in my class), 2007.
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