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Venkatesh Ganti
University of Wisconsin-Madison
Computer Sciences Department
1210 West Dayton Street
Madison, WI 53706-1685
(608) 262-6629
Research
Interests
202 S. Randall Ave, #2
Madison, WI 53715
(608) 250-9357
[email protected]
http://www.cs.wisc.edu/~vganti
Design and development of scalable, interactive mining and monitoring systems for
next generation data analysis and decision support systems. My dissertation research
addresses the development of fast scalable algorithms for modeling the characteristics
of dynamically evolving datasets and detecting significant changes while tracking
trends. Several other aspects of decision support systems, such as approximate query
answering and collaborative filtering, also interest me.
Education The University of Wisconsin-Madison, since September 1997
Ph.D. Student
Advisor: Professor Raghu Ramakrishnan
Anticipated Graduation Date: July 2000
The University of Wisconsin-Madison, 1995-1997
Masters in Computer Science
(Overall GPA: 4.00)
The Indian Institute of Technology, Madras, India, 1991-1995
Bachelor of Technology in Computer Science
Thesis Topic: Adding real time constraints to a relational database system
Advisor: Professor Krithi Ramamritham
Honors
Microsoft Graduate Fellowship, since August 1999
Departmental Summer Research Scholarship, UW-Madison, June-August 1996
National Merit Scholarship from Government of India, 1991
State Merit Scholarship from Government of Andhra Pradesh, India, 1986-1994
Refereed Publications
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan. DEMON--Mining and Monitoring
Evolving Data. To appear in Proceedings of the Sixteenth International Conference on Data
Engineering, San Diego, California, 2000.
(Adjudged the best student paper.)
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan, Wei-Yin Loh. FOCUS--A Framework
for Measuring Changes in Data Characteristics. In Proceedings of the Eigteenth ACM SIGMODSIGACT-SIGART Symposium on Principles of Database Systems, Philadelphia, Pennsylvania,
1999. (Also submitted by invitation to the special issue for PODS 1999 of the Journal of Computer
and System Sciences.)
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan. CACTUS—Clustering Categorical Data
Using Summaries. In Proceedings of the Fifth ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, San Diego, California, 1999.
Johannes Gehrke, Venkatesh Ganti, Raghu Ramakrishnan, Wei-Yin Loh. BOAT--Optimistic
Decision Tree Construction. In Proceedings of the ACM SIGMOD Conference, Philadelphia,
Pennsylvania, 1999.
Venkatesh Ganti, Raghu Ramakrishnan, Johannes Gehrke, Allison Powell, James French.
Clustering Large Datasets in Arbitrary Metric Spaces. In Proceedings of the Fifteenth International
Conference on Data Engineering, Sydney, Australia, 1999.
Vishy Poosala, Venkatesh Ganti. Fast Approximate Answers to Aggregate Queries on a Data Cube.
In Proceedings of the 11th International Conference on Scientific and Statistical Database
Management, Cleveland, Ohio, July 1999, pages 24-33.
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan. Mining Very Large Databases. IEEE
Computer, Vol. 32, No. 9, August 1999, pages 38-45.
Vishy Poosala, Venkatesh Ganti, Yannis Ioannidis. Approximate Query Answering Using
Histograms. IEEE Data Engineering Bulletin, 22(4): 5-14
Johannes Gehrke, Raghu Ramakrishnan and Venkatesh Ganti. RainForest – A Framework for Fast
Decision Tree Construction of Large Datasets. In Proceedings of the Twenty-fourth International
Conference on Very Large Data Bases, New York, New York, 1998.
Rohan Aranha, Venkatesh Ganti, Srinivasa Narayanan, C.R. Muthukrishnan, S.T.S. Prasad, Krithi
Ramamritham. Implementation of a Real-Time Database System. Information Systems, 21 (1),
March 1996. (Preliminary version in Proceedings of the International Conference on Management
of Data, Pune, India, 1995.)
Patents
Method, Apparatus and Programmed Medium for Approximating the Data Cube and Obtaining
Approximate Answers to Queries in Relational Databases. Current status: Patent pending.
BOAT—Optimistic Decision Tree Construction. Current status: Patent pending.
Experience
Department of Computer Sciences, University of Wisconsin-Madison
September 96-present
Graduate research assistant with Prof. Raghu Ramakrishnan. Currently working on the DEMON
project. The objective of the DEMON project is to model user-specified subsets of dynamically
evolving datasets, and to detect significant changes and evolving trends.
Microsoft Research, Seattle, WA
Summer 1999
Was an intern in the Data Mining and Exploration (DMX) group. Worked with Dr. Heikki Mannila
on computing easily understandable description of large transactional databases.
Lucent Technologies, Bell Laboratories, NJ
Summer 1997
Worked in the AQUA (Approximate Query Answering) project with Vishy Poosala. Developed
efficient algorithms for constructing summary statistics that optimize main memory usage in a
prototypical system to give fast approximate answers.
Department of Computer Sciences, University of Wisconsin-Madison
August 1995-May 1996
Graduate teaching assistant for an introductory course on Numerical Methods assisting the
instructor in advising students and grading their work.
The Tata Institute of Fundamental Research, Bombay, India
Studied Probabilistic Methods and their Applications.
Summer 1994
Reviewing Activities
Journals:
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge Discovery
VLDB Journal
Machine Learning Journal
Others:
IEEE Computer
Non-academic Activities
Enjoy reading and playing badminton, squash.
References
Professor Raghu Ramakrishnan (Dissertation Advisor)
Department of Computer Sciences, 1210 W. Dayton St, Madison, WI 53706
Phone: (608) 262-9759; Email: [email protected]
Professor Jeffrey Naughton
Department of Computer Sciences, 1210 W. Dayton St, Madison, WI 53706
Phone: (608) 262-8737; Email: [email protected]
Dr. Heikki Mannila
Nokia Research Fellow
Phone: 358 40 749 9040; Email: [email protected]
Professor Olvi Mangasarian,
Department of Computer Sciences, 1210 W. Dayton St, Madison, WI 53706
Phone: (608) 262-6593; Email: [email protected]