Work Hard and Play Hard...

Jierui(Jerry) Xie

Ph.D Student
Department of Computer Science
Rensselaer Polytechnic Institute
110 8th Street
Troy, New York 12180
USA

Email:
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I am currently a Ph.D. student at Rensselaer Polytechnic Institute (RPI). My advisor is Boleslaw K. Szymanski.

In general, my research interests include data mining, machine learning and social network. I am interested in applying data mining and machine learning technologies to knowledge/pattern discovery, classification, clustering in Internet, sensor network and social network.

Recently I work on simulating and analyzing the opinion spreading and community detection in large-scale networks (our center).

 

I am graduating and looking for a challenging research or developing position (CV). I have background in

- Opinion/influence spreading in social networks
- Community detection in social networks
- Pattern/event/human activity recognition for context-aware applications
- Density based anomaly detection/change detection
- Learning similarity/dissimilarity for categorical data
- Text mining (e.g., burstiness-aware doc search)
- Simulation of stochastic dynamics in networks
- Resource optimization using neural networks
- Routing protocol in wireless sensor networks

Software:

Linear time community detection: SLPA

Recent Publication:

Opinion Dynamics in Social Networks

Fast Community Detection in Social Networks
  • J. Xie and B. Szymanski, "Towards Linear Time Overlapping Community Detection in Social Networks", PAKDD 2012. (pdf,arxiv,BibTex, software )
  • J. Xie, S. Kelley and B. K. Szymanski, "Overlapping Community Detection in Networks: the State of the Art and Comparative Study", Technical Report, 2012 (pdf,arxiv,BibTex)
  • J. Xie, B. K. Szymanski and X. Liu, "SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process", IEEE ICDM workshop on DMCCI 2011, Vancouver, CA. (pdf,arxiv,BibTex )
  • J. Xie, B. K. Szymanski, "Community Detection Using A Neighborhood Strength Driven Label Propagation Algorithm", IEEE NSW 2011, West point, NY. (pdf,arxiv,BibTex)

Pattern Recognition and Machine Learning

  • J. Xie, B. K. Szymanski, M. Zaki, "Learning Dissimilarities for Categorical Symbols", Feature Selection in Data Mining 2010.(pdf,BibTex)
  • S. Geyik, J.Xie, B. Szymanski, "Behavior Modeling with Probabilistic Context Free Grammars", International Conference on Information Fusion 2010.(pdf,BibTex)
  • J. Xie, M. Beigi. "A Scale-invariant Local Descriptor for Event Recognition in 1D Sensor Signals", IEEE International Conference on Multimedia & Expo(ICME),pp:1226 - 1229, 2009.(pdf,BibTex)
  • C. Liu, J. Xie and Y. Hu. "Using Hopfield Neural Network to Solve Resource-leveling Problem". Proc. of the 11th joint international computer conference(JICC),pp:564-567, 2005. (pdf)

For more, see CV.

Talk & Poster

  • Abstract: The effect of committed groups on consensus formation in social networks,3rd Workshop on Complex Networks (CompleNet), March 2012, Florida.
  • Poster: Interdisciplinary Workshop on Information and Decision in Social Networks (WIDS).May 31-June 1, 2011, MIT, Boston.
  • Talk: Influencing with committed agents in the two-word naming game, 12th Annual Greater Boston Area Statistical Mechanics Meeting (GBASM), Oct 9, 2010, Boston.
  • Talk: The Social Influence of Committed Minorities in a Two-party System, 13th Annual Greater Boston Area Statistical Mechanics Meeting (GBASM), Oct 15, 2011, Boston.(PPT)
  • Talk: A New Scale Invariant Features Descriptor for Event Detection in Sensor Data Streams, IBM, Aug. 2008.

Internship:

  • Jun.-Aug 2009, Summer Intern at IBM T.J. Watson Research Center, Hawthorne, NY, Mentor:
    David Wood. I designed and developed a prototype for sensor monitoring and fault management system in battlefield scenario.
  • Jun.-Aug 2008, Summer Intern at IBM TJ Watson Research Center, Hawthorne, NY. I worked with
    Mandis Beigi on density based change detection and event recognition. I proposed a
    shape-base scale invariant feature descriptor, which takes advantage of scale space theory to detect multiple temporal scale events from various kinds of sensors (Infrared, seismic, accelerometer and acoustic).

RA/TA:

  • TA: Computer organization 2007, Computation complexity 2008, Machine learning 2009
  • RA: 2009,2010,2011

Award:

  • ICDM 2011 Student Travel Awards