Ron Kohavi, PhD

ronnyk@CS.Stanford.EDU
http://www.kohavi.com/resume.html

Work Experience

2005-	General Manager, Experimentation Platform, Microsoft.com (Seattle, WA).
2003-2005	Director, Data Mining and Personalization, Amazon.com (Seattle, WA). Manage multiple teams in a 65-person organization responsible for Amazon’s personalization, automation, consumer behavior / data mining, site experimentation, and automated e-mail. Introduced several features estimated to be worth several hundred million dollars in incremental revenue.
2002-2003	Vice President, Business Intelligence, Blue Martini Software (San Mateo, CA) Managed the Business Intelligence Sales Demo team and the Analytic Services team.  Helped drive Business Intelligence sales and marketing activities, created success stories, and helped the professional services organization in complex implementations as a center of excellence.
2000-2002	Senior director, Data Mining Applications, Blue Martini Software Led the engineering team, including two managers.  Responsible for the data collection, transfer (ETL), analysis, reporting, visualization, and campaign management in Blue Martini's products.
1998-2000	Director, Data Mining Applications, Blue Martini Software Designed and architected the data mining, reporting, and transfer (ETL) modules. Led the engineering team responsible for these components.
1997-1998	Manager, MineSet, Silicon Graphics Inc. (Mountain View, CA) Managed the MineSet data mining and visualization engineering team of 15, including another manager.
1996-1997	Manager, Analytical Data Mining group, MineSet, Silicon Graphics Inc. Managed a team of five engineers that designed and implemented the analytical (server-side) data mining engines of MineSet on top of MLC++.
1995-1996	Project lead, MineSet, Silicon Graphics Inc. Designed and implemented data mining algorithms for MineSet using MLC++.
1993-1995	Project lead, MLC++, Stanford University. Designed and Implemented the Machine Learning library in C++ for data mining. Supervised four students, including one 50% research assistant dedicated for the project. The library was adopted as the basis for the analytical engines in MineSet when I moved to SGI and later licensed by Blue Martini Software. It is used for research work at several universities. See http://www.sgi.com/tech/mlc/ for details.
1991 (summer)	Programmer, Verification Group, IBM Research Center, Haifa, Israel. Programmed a graphical user interface in C.
1987-1988	Manager (lieutenant), Israeli Defense Forces, Israel. Managed 12 people at a computer center in a large army base in Israel.
1985-1987	System Analyst, Israeli Defense Forces, Israel. Analyzed, designed, and programmed systems at a large army base in Israel.
1981-1984	Programmer, International Software, Tel-Aviv, Israel. Developed a database application generator, IRIS, with three other people (during high school). The program was sold commercially, including sales to the Israeli Defense Forces.

Education

1991-1995	Stanford University, Stanford, CA Ph.D. in Computer Science. Thesis: Wrappers for Performance Enhancement and Oblivious Decision Graphs Thesis advisors: Jerome Friedman, Nils Nilsson, and Yoav Shoham.
1988-1991	Technion, Haifa, Israel. B.A. in Computer Science, Summa Cum Laude.

Honors

2005	Irrelevant Features and the Subset Selection Problem and Wrappers for Feature Subset Selection are in the top-10 most cited papers in Artificial Intelligence Expert Systems and Machine Learning according to NEC's ResearchIndex. Bias Plus Variance Decomposition for Zero-One Loss Functions is in the top-100 most cited papers in Machine Learning.
1996	IEEE Tools With Artificial Intelligence Best Paper Award for the paper Data Mining using MLC++, a Machine Learning Library in C++ by Kohavi, Sommerfield, and Dougherty. The paper is in NEC's ResearchIndex top-100 cited papers in Machine Learning.
1992	Passed the Ph.D. Artificial Intelligence qualifying exam with distinction.
1989, 1990, 1991	Technion, President's award (top 5%) each year of degree.

Professional Activities

1. General Chair, KDD 2004

2. Four patents granted, several pending.

3. Program committee member, Knowledge Discovery and Data Mining conference (KDD), 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005

4. Program committee member, International Conference on Machine Learning, 1997, 1998, 1999, 2000, 2001, 2002, 2003

5. Member of Technical Advisory Board, mySimon, 1999-2000 (until they were bought by CNET)

6. Co-chair (with Jim Gray), Industrial Track, Knowledge Discovery and Data Mining (KDD), 1999

7. Co-chair (with Carla Brodley), KDD-CUP 2000 (Aug 2000)

8. Co-chair WEBKDD'2003, WEBKDD'2001, WEBKDD'2000

9. Co-editor (with Foster Provost), special issue of the International Journal Data Mining and Knowledge Discovery on e-commerce and data mining.   This special issue is also available as a hardcover book from Kluwer Academic Publishers; ISBN: 0792373030

10. Member of the editorial board, Data Mining and Knowledge Discovery journal,1997, 1998, 1999, 2000, 2001, 2002

11. Co-Editor (with Foster Provost), special issue on applications of machine learning (Volume 30, 1998), journal of Machine Learning

12. Member of the editorial board, journal of Machine Learning, 1997, 1998, 1999

Selected Invited Talks/Panels (reverse chronological order)

1. Emetrics 2004: Amazon's Data Mining and Personalization (June 2004)

2. CSLI's Seminar on Computational Learning and Adaptation on Real-world Insights from Mining Retail E-Commerce Data, May 22, 2003

3. Blue Martini Webinar.  Deriving Key Insights from Blue Martini Business Intelligence: Summary of key insights from using Business Intelligence against Debenhams and MEC sites. Approved by Debenhams and MEC, presented March 10, 2003. 

4. Mining Customer Data, Etail CRM Summit, 2002.  PDF slides.
The talk was heavily referenced in ComputerWorld (original)

5. Blue Martini article in the New York Times: Fine Tuning Customer Behavior.

6. Invited paper and talk at KDD 2001 industrial track: Mining E-commerce Data, the Good, the Bad, and the Ugly PDF paper, slides

7. Invited talk at SAS's M 2001, Oct 2-3, 2001.

8. E-commerce and Clickstream Mining Tutorial at the first SIAM International Conference on Data Mining, 4/2001

9. Data Mining and Visualization. Invited talk at the National Academy of Engineering US Frontiers of Engineers, Sept 2000. Available in book form ISBN: 0-309-07319-7

10. Crossing the Chasm: From Academic Machine Learning to Commercial Data Mining. The Fifteenth International Conference on Machine Learning, Madison, WA, July 24-27, 1998

Selected Publications (reverse chronological order)

1. Ron Kohavi, Llew Mason, Rajesh Parekh, Zijian Zheng, Lessons and Challenges from Mining Retail E-Commerce Data. PDF.
Machine Learning Journal, Special Issue on Data Mining Lessons Learned, 2004.

2. Ron Kohavi, Neal Rothleder, and Evangelos Simoudis, Emerging Trends in Business Analytics, Communications of the ACM, Volume 45, Number 8, Aug 2002, pages 45-48. PDF

3. Ron Kohavi and J. Ross Quinlan.  Decision-tree discovery.  In Will Klosgen and Jan M. Zytkow, editors, Handbook of Data Mining and Knowledge Discovery, chapter 16.1.3, pages 267-276. Oxford University Press, 2002.  Postscript, PDF.

4. Llew Mason, Zijian Zheng, Ron Kohavi, Brian Frasca, eMetrics Study, Dec 2001.  PDF

5. Zijian Zheng, Ron Kohavi, and Llew Mason, Real World Performance of Association Rule Algorithms, KDD 2001, short, long, slides. 

6. Suhail Ansari, Ron Kohavi, Llew Mason, and Zijian Zheng, Integrating E-Commerce and Data Mining: Architecture and Challenges, ICDM 2001, PDF

7. Kohavi Ron, Brodley Carla, Frasca Brian, Mason Llew, and Zheng Zijian, KDD-Cup 2000 Organizers' Report:  Peeling the Onion . SIGKDD Explorations Volume 2, issue 2, 2000.  PDF, powerpoint slides
Also translated to Japanese in Information Processing Society of Japan, Vol 42 No. 5

8. Kohavi Ron and Provost Foster, Applications of Data Mining to Electronic Commerce, Data Mining and Knowledge Discovery journal 5(1/2), 2001. Postscript, PDF

9. Eric Bauer and Ron Kohavi. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. The journal Machine Learning Vol 36, Nos. 1/2, July/August 1999, pages 105-139.  PDF.

10. Foster Provost and Ron Kohavi. On Applied Research in Machine Learning. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process Volume 30, Number 2/3, February/March 1998.  Postscript or HTML

11. Ron Kohavi and George John. Wrappers for Feature Subset Selection. Artificial Intellignce 97, 1997. NEC’s ResearchIndex one of the top 10 referenced papers in Artificial Intelligence Expert Systems. Postscript.

12. Ron Kohavi, Dan Sommerfield, and James Dougherty. Data Mining using MLC++, a Machine Learning Library in C++. International Journal on Artificial Intelligence Tools vol. 6, No. 4, 1997. (long version of the paper below with the same title). NEC's ResearchIndex one of the top 100 referenced paper in Machine Learning.   Postscript

13. Ron Kohavi and David Wolpert. Bias Plus Variance Decomposition for Zero-One Loss Functions. In Machine Learning: Proceedings of the Thirteenth International Conference, pages 275-283, July 1996.  NEC's ResearchIndex one of the top-100 referenced paper in Machine Learning. Postscript.

14. Ron Kohavi. Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid. In The Second International Conference on Knowledge Discovery and Data Mining, pages 202-207, August 1996