Jessica Lin, Ph.D (林 虹汎)    Assistant Professor Department of Computer Science George Mason University 4400 University Drive, MS 4A4 Fairfax, VA 22030 Office: Science & Technology II, Room 453 Tel: (703) 993-4693 Fax: (703) 993-1638 Email: jessica [AT] cs [DOT] gmu [DOT] edu Office hours for Fall '08: Monday 3-5pm CV

Announcement: I have one opening for Graduate Research Assistant, starting Fall 2008. Contact me if you are interested AND meet the qualifications.

Education

• 2002-2005        Ph.D in Computer Science, UC Riverside
• 1999-2002        M.S in Computer Science, UC Riverside
• 1995-1999        B.S in Computer Science, UC Riverside

Teaching

• Fall 2008: INFS 614, CS 780 (cross-listed as INFS 780) NEW!
• Spring 2008: CS 450
• Fall 2007: INFS 795 (topic: Mining Temporal Data), INFS 614
• Spring 2007: INFS 795
• Fall 2006: INFS 590, INFS 795
• Spring 2006: INFS 590
• Fall 2005: INFS 590

Research

• Research interests: My research has focused on the mining of large datasets. While most of my work has focused on mining time series data, I have also considered and am interested in mining images, texts and other types of multimedia data. I have published work on time series clustering, motif discovery, symbolic representation, visualization, anomaly detection, etc.

• Publications   (in DBLP)

[Journals]

o Lonardi, S., Lin, J., Keogh, E. & Chiu, B. (2008). Efficient Discovery of Unusual Patterns in Time Series. Special Issue of New Generation Computing Journal. p. 61-93.

o Lin, J., Keogh, E., Li, W. & Lonardi, S. (2007). Experiencing SAX: A Novel Symbolic Representation of Time Series. Data Mining and Knowledge Discovery Journal. p. 107-144.
(This work also appears in: Lin, J., Keogh, E., Lonardi, S. & Chiu, B. (2003). A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. San Diego, CA. June 13.)   
[SAX page] [Eamonn's SAX page]

o Keogh, E., Lin, J. & Fu, A. (2006). Finding the Most Unusual Time Series Subsequence: Algorithms and Applications. Knowledge and Information Systems (KAIS). Springer-Verlag.

o Keogh, E., Lin, J., Fu, A. & Van Herie, H. (2005). Finding the Unusual Medical Time Series: Algorithms and Applications. IEEE Transactions on Information Technology in Biomedicine.

o Lin, J., Keogh, E. & Lonardi, S. (2005). Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases. Information Visualization Journal.

o Keogh, E & Lin, J. (2004). Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research. Knowledge and Information Systems (KAIS), Springer-Verlag.

o Lin, J. & Keogh, E (2004). Finding or Not Finding Rules in Time Series. Special Issue of Advances in Econometrics.

[Conference/Workshop Papers]

o Al-Shammari, E. & Lin, J. (2008). Automated Corpora Creation Using a Novel Arabic Stemming Algorithm. In proceedings of the 2008 International Symposium on Using Corpora in Contrastive and Traslation Studies. Hanzhou, China. Sept 25-27. To Appear.

o Al-Shammari, E. & Lin, J. (2008). A Novel Arabic Lemmatization Algorithm. In proceedings of the 2nd SIGIR Workshop on Analytics for Noisy Unstructured Text Data. Singapore, July 24-27.

o Al-Shammari, E. & Lin, J. (2008). A New Arabic Stemming Algorithm. In proceedings of the 2008 ISCA Workshop on Experimental Linguistics. Athens, Greece. Aug 25-27.

o   Lin, J., Etter, D. & DeBarr, D. (2008). Exact and Approximate Reverse Nearest Neighbor Search in Multimedia Data. In proceedings of the 2008 SIAM Conference on Data Mining (SDM). Atlanta, GA. Apr 24-26. 

o Lin, J. & Keogh, E. (2006). Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. In proceedings of the 10^th European Conference on Principles and Practice of Knowledge Discovery in Databases. Berlin, Germany. Sept 18-22. To Appear.

o Fu, A., Leung, O., Keogh, E. & Lin, J. (2006). Finding Time Series Discords Based on Haar Transform. In proceedings of the 2^nd International Conference on Advanced Data Mining and Applications. Xi’An, China. Aug 14-16. pp 31-41.

o Keogh, E., Lin, J. & Fu, A. (2005). HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In the 5^th IEEE International Conference on Data Mining. New Orleans, LA. Nov 27-30.

o Lin, J., Keogh, E., Fu, A. & Van Herie, H. (2005). Approximations to Magic: Finding Unusual Medical Time Series.. In proceedings of the 18^th IEEE Int'l Symposium on Computer-Based Medical Systems. June 23-24. Dublin, Ireland.

o Lin, J., Keogh, E., Lonardi, S., Lankford, J. P. & Nystrom, D. M. (2004). Visually Mining and Monitoring Massive Time Series. In proceedings of the 10^th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA. Aug 22-25.
(This work also appears as a VLDB 2004 demo paper, under the title "VizTree: a Tool for Visually Mining and Monitoring Massive Time Series" - Click on the link to view the demo)

o Lin, J., Vlachos, M., Keogh, E. & Gunopulos, D. (2004). Iterative Incremental Clustering of Time Series. In proceedings of the IX Conference on Extending Database Technology (EDBT 2004). Crete, Greece. March 14-18, 2004.   

o Keogh, E., Lin, J. & Truppel, W. (2003). Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research. In proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003). Melbourne, FL. Nov 19-22. p.115-122.   

o Lin, J., Keogh, E. & Truppel, W. (2003). (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. In proceedings of the 2003 International Conference on Artificial Intelligence, Special Session on Applications of AI in Finance and Economics. Las Vegas, NV. June 23-26.

o Patel, P., Keogh, E., Lin, J. & Lonardi, S. (2002). Mining Motifs in Massive Time Series Databases. In proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002). Maebashi City, Japan. Dec 9-12.

[Book Chapters]

o Lin, J., Vlachos, M., Keogh, E. & Gunopulos, D. (2006) Multi-Resolution Time Series Clustering and Application to Images. Multimedia Data Mining and Knowledge Discovery. Eds. Valery Al Petrushin and Latifur Khan. Springer. To Appear.

o Ratanamahatana, C. A., Lin, J., Gunopulos, D., Keogh, E. & Vlachos, M. (2005). Mining Time Series Data. Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers.

• Resources

• VizTree demo New! Added on 09/06/04
• SAX (Symbolic Aggregate approXimation)
• Dr. Eamonn Keogh’s SAX website
• Danger of Dimensionality Reduction

Working Experience

• 8/05-Present Assistant Professor, George Mason University
• 04/02-06/05 Research Assistant, UC Riverside
• 06/04-09/04 Research Intern, AT&T Research (Information Visualization, Supervisor: Dr. Stephen North)
• 06/03-09/03 Research Intern, Microsoft Research (Machine Learning and Applied Statistics, Mentor: Dr. Max Chickering)
• 06/01-09/01 Intern, ESRI
• 09/99-12/01 Teaching Assistant, UC Riverside
• 06/98-06/00 Programmer, U.S. Salinity Laboratory at the U.S. Department of Agriculture (U.S.D.A.)

Professional Activities

• Program Committee Member:

o The 12^th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006)

o The 2006 IEEE International Conference on Data Mining (ICDM 2006)

o The First Workshop on Spatial and Spatial-temporal Data Mining, in conjunction with ICDM (SSTDM 2006)

• Journal Reviewer:

o IEEE Tran. on Knowledge and Data Engineering (TKDE, IEEE Computer Society)

o Data Mining and Knowledge Discovery (DAMI, Springer)

o The Visual Computer (Springer)

o Special Issue of Advances in Econometrics (AIE, Elsevier Science)

• External Reviewer:

o EDBT 2006

o ICDE 2006

o CBMS 2005

o VLDB 2004

o DMWI 2004

o ICML 2003

o CIKM 2002

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