Download CURRICULUM VITAE Reuven Kashi

CURRICULUM VITAE
Reuven Kashi
CONTACT INFORMATION:
RUTCOR – Rutgers University
640 Bartholomew Road
Piscataway, NJ 08854, USA
Telephone: (+1)-732-445-4859
Fax: (+1)-732-445-5472
Email: [email protected]
Url: http://www.cs.biu.ac.il/∼kashi
CURRENT POSITION:
Postdoctoral Researcher at RUTCOR – Rutgers Center for Operations Research, Rutgers University, New
Jersey, USA.
RESEARCH INTERESTS:
My main research interests are in the field of data mining and knowledge discovery with a focus on massive
data sets. This research is mainly concerned with the development of new methodologies and advanced
algorithms that scale to the huge bodies of data that have been gathered and stored on many kinds of database
systems. The interdisciplinary nature of this field involves techniques from databases, algorithms design and
analysis, statistics, data visualization, machine learning and artificial intelligence. I have worked on the
development of new automatic tools for finding implicit patterns (hypotheses) and display useful summaries
of quantitative data. I have also solved some of the limitations and shortcomings of traditional data mining
models (in areas such as clustering, classification, link analysis, etc.), and explored new application areas
enabled by the new mining capabilities.
EDUCATION:
1997 – 2003:
Bar-Ilan University (Ramat Gan, Israel).
Ph.D. in Computer Science
Field of Research: Data Mining and Analysis
Thesis subject: Quantitative Data Mining Using Data Visualization.
Advisors: Amihood Amir and Nathan Netanyahu
1995 – 1997:
Bar-Ilan University (Ramat Gan, Israel).
M.Sc. in Computer Science
Magna cum Laude
University’s Award for Excellence of best M.Sc. thesis
Field of Research: Data Mining
Thesis subject: A New and Versatile Method for Association Generation.
Advisors: Amihood Amir and Ronen Feldman
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1992 – 1995:
Bar-Ilan University (Ramat Gan, Israel).
B.Sc. in Computer Science and Mathematics
Magna cum Laude
FIELD OF INTERESTS:
• Algorithms Design and Analysis
• Databases, Data Mining and Knowledge Discovery
◦ Quantitative Queries
◦ Clustering Large Databases
◦ Mining Association Rules
◦ Approximation Algorithms
• Algorithms for Massive Data Sets
◦ Automatic Hypotheses Generation
◦ Streaming Algorithms
• Data and Information Visualization
• Pattern Matching
AWARDS AND FELLOWSHIPS:
• RUTCOR Postdoctoral Fellowship, 2003–2004.
• ICDM Student Travel Award provided by IBM Research, 2003.
• Travel supported by NSF grant CCR-9610170, Data Mining, Information Retrieval and Pattern Matching – Application-driven Algorithmic Research, 2002–2003
• ISF fellowship 282/01, On The Theory of Search and Applications, 2002–2003.
• AT&T Labs - Research Internship, Shannon Laboratory, Florham Park, NJ, USA, February 2002.
• Ph.D. and M.Sc. Fellowships, Department of Mathematics and Computer Science, Bar-Ilan University,
Ramat Gan, Israel, 1996–2002.
• Rector’s Award for Excellence of best M.Sc. thesis, Bar Ilan University, Ramat Gan, Israel, 1996.
• Dean’s Award for Undergraduate Top-10 List of Excellence, Bar Ilan University.
INDUSTRIAL BACKGROUND:
• Programming: C, C++.
• Programming Languages and Packages: HTML, PERL, JavaScript, MATLAB and SAS.
• Operating Systems: MS Windows, Unix.
• Profound knowledge and experience with developing and implementing algorithms, in particular:
◦ Algorithms that requires mathematical knowledge and some probabilistic analysis.
◦ Developing and implementing various Knowledge Discovery in Databases (KDD) algorithms.
• Automatic Hypotheses Generation: Developed and implemented new techniques for finding local and
non-linear interrelations in large multivariate quantitative databases. The proposed methodology generates and ranks hypotheses of subsets of data and attributes, according to the significance of their
relation in a given database. This enables to discover implicit but potentially useful patterns in the
data, which cannot be easily discovered by traditional analysis tools.
• High Dimensional (Projected) Clustering: Suggested and implemented an approach that makes it possible to automatically analyze high dimensional data for arbitrary low dimensional (projected) clusters.
This approach is robust as far as noise is concerned; it enables the discovery of projected clusters with
different topologies without any prior domain knowledge, and allows separating overlapping clusters.
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• Visualization of High Dimensional Data: Developed and implemented an approach to aid the automatic
process of analyzing high-dimensional data for distinguishable structures. The basic idea consists of
supporting the decisive steps of a further developed automated clustering process with visualization
techniques. This visualization technique allows an easy identification of important data characteristics,
which can be used for the calculation of the best projections and separators.
• Shape-Embedded-Histograms for Visual Data Mining: Designed and implemented a new visualization
technique, that using the available 2-D screen space to present more detailed information. This technique retain the intuitiveness of traditional techniques such as Bar Charts and Scatter Plots, while
allowing the visualization of large amounts of data in an effective way, and avoiding problems with
over plotting or occlusion.
ACADEMIC TEACHING:
Lectured and TA’ed graduate and undergraduate courses in the fields of Computer Science and Discrete
Mathematics, such as: Introduction to Algorithms, Algorithms I , Advanced Algorithms, Algorithms II, Data
Mining, Seminar on Data Mining, Seminar on Pattern Matching, Seminar on Algorithms, Projects Instructor, Introduction to Databases, Database Systems, Introduction to Computing, Data Structures, Advanced
Data Structures, and Complex Functions.
PUBLICATIONS:
A. Journal Papers
1. A. Amir, R. Kashi and N. S. Netanyahu, Analyzing Quantitative Databases: Image is Everything,
Submitted.
2. A. Amir, R. Feldman and R Kashi, A New and Versatile Method for Association Generation,
Journal of Information Systems (IS), 22(6/7): 333-347, 1997.
B. Conference Papers (refereed - with Proceedings)
1. A. Amir, R. Kashi, Daniel A. Keim, N. S. Netanyahu and M. Wawryniuk, Shape-EmbeddedHistograms for Visual Data Mining, In Proceedings of the VisSym - Joint Eurographics - IEEE
TCVG Symposium on Visualization (VisSym04), May 19-21, Konstanz, Germany, 2004.
2. A. Amir, R. Kashi, Daniel A. Keim, N. S. Netanyahu and M. Wawryniuk, Analyzing HighDimensional Data by Subspace Validity, In Proceedings of the 3rd IEEE International Conference
on Data Mining (ICDM), November 19-22, 2003, Melbourne, Florida, USA, pages 473-476.
3. A. Amir, R. Kashi and N. S. Netanyahu, Efficient Multidimensional Quantitative Hypotheses
Generation,In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM),
November 19-22, 2003, Melbourne, Florida, USA, pages 3-10.
4. A.Amir, R. Kashi and N. S. Netanyahu: Analyzing Quantitative Databases: Image is Everything,
In Proceedings of 27th International Conference on Very Large Data Bases (VLDB), September
11-14, 2001, Roma, Italy, pages 89-98.
5. A. Amir, R. Feldman and R. Kashi, A New and Versatile Method for Association Generation,
In Proceedings of the First European Symposium on Principles of Data Mining and Knowledge
Discovery (PKDD), Trondheim, Norway, June 24-27, 1997, pages 221-231. (Also in Lecture Notes
in Computer Science, Vol. 1263, Springer, 1997)
C. Work In Progress
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1. Optimal Support Set Selection in Data Analysis with Applications to Bioinformatics.
2. Efficient Subspace Validity Computation for Projected Clustering.
3. Exploratory Data Analysis of High-Dimensional Data by Pixel Validity Plots.
CONFERENCE, WORKSHOP AND SEMINAR PRESENTATIONS:
Conferences
1. The 3rd IEEE International Conference on Data Mining (ICDM), Melbourne, Florida, USA,
November 2003.
2. The 27th International Conference on Very Large Data Bases (VLDB), Roma, Italy, September
2001.
3. The First European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD),
Trondheim, Norway, June 1997.
Workshops
1. Second Haifa Winter Workshop on Computer Science and Statistics University of Haifa, Israel,
December 2003.
2. Haifa Winter Workshop on Computer Science and Statistics, University of Haifa, Israel, December
2001.
3. The 13th Israeli Symposium on Artificial Intelligence, Computer Vision and Neural Networks, Tel
Aviv University, Israel, February 1997.
Seminars
1. Department of Statistics, University of Connecticut, Storrs, CT, USA, April 2004.
2. RUTCOR Seminar, Rutgers University, Piscataway, NJ, USA, April 2004.
3. Computer Science Colloquium, Center for Information Science and Technology, Temple University,
Philadelphia, PA, USA, February 2004.
4. Research Seminar, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA, April 2002.
5. Computer Science Seminar, Rutgers University, Camden, NJ, USA, April 2002.
6. Research Seminar, AT&T Labs – Research, Shannon Laboratory, Florham Park, NJ, USA, February 2002.
7. Research Seminar, DIMACS Discrete Math – Theory of Computing Seminar, Rutgers University,
Piscataway, NJ, USA, February 2002.
8. Computer Science Colloquium, Bar-Ilan University, Israel, November 2001.
9. Computer Science Colloquium, Bar-Ilan University, Israel, May 1997.
PROFESSIONAL ACTIVITIES:
• Refereeing: IEEE TKDE – Transactions on Knowledge and Data Engineering, IEEE TNN – Transactions on Neural Networks, DKE – Journal on Data & Knowledge Engineering.
• Program committee: ICAI’04.
REFERENCES:
Available upon request.
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