Ron Kohavi, PhD
I am a Microsoft Technical Fellow and corporate VP of the
and Experimentation at Microsoft's Cloud and AI group.
I was previously a Distinguished Engineer and GM, Partner Architect at Bing, General Manager for Microsoft's Experimentation Platform, director of data mining and personalization at
Amazon.com, and VP of Business Intelligence at Blue Martini Software.
Check out my
- Leading a team of about 110 data scientists and developers at
Microsoft's Analysis and Experimentation.
- The Harvard Businses Review article (10/2017) The Surprising
Power of Online Experiments describes much of my
work on expermentation
- While I have been working in industry
since 1995, I continue to publish sporadically.
My h-index, a measure of
productivity and impact of published work, is 57 according to
Google Scholar, with over 42,000 citations to my work. Hirsch,
who proposed the metric, suggested that an h-index of 10-12
is considered a useful guideline for tenure decisions at major
research universities; a value of about 18 could mean a full
professorship; 15-20 could mean a fellowship in the American
- MIT CODE (Conference On Digital Experimentation) invited talk
Pitfalls in Online Controlled Experiments and
20 minutes video.
- KDD 2015 Keynote, August 2015
Online Controlled Experiments: Lessons from Running A/B/n Tests for 12 years
- ACM Recommender Systems industry keynote, Sept 2012
Online Controlled Experiments: Introduction, Learnings, and Humbling
Statistics and Powerpoint PPTX.
- Three of my papers are in the top
1,000 most cited articles in computer science, including the article Wrappers for
Feature Subset Selection, which is in the top
- I was granted
- MineSet had powerful visualizations of models. Some examples (use
Evidence visualizer (Naive Bayes),
Decision Table visualizer,
Decision Tree visualizer
and powerful data visualizations including:
- I started the MLC++ project, Machine Learning library in C++, which formed the basis for
SGI's MineSet, then Blue Martini's analytics. Documentation
coding standards, MLC++ coding
standards, and utilities.
- Keynote at EC 10. A
similar keynote at the Analytics Revolution, 2010 was recorded: Online Controlled
Experiments: Listening to the Customers, not to the HiPPO (PDF) (PPTX)
- The paper
Experimentation at Microsoft, 2009, was recognized as top 30
Microsoft ThinkWeek paper and an early version of it won 3rd place at the
Third workshop on Data Mining Case Studies and Practice Prize, 2009.
- Scientific Advisor to Trusted Opinion, 2007-2008.
- Member of Technical Advisory Board, mySimon, 1999-2000 (until they were bought
- General Chair, KDD 2004.
Ronny Kohavi is a Microsoft Technical Fellow and corporate VP of Microsoft's Analysis and Experimentation at
Microsoft's Cloud and AI group. He was previously a Distinguished
Engineer and Partner Architect at Bing.
He joined Microsoft in 2005 and founded the
Experimentation Platform team in
2006. Prior to Microsoft, he was the director of data mining and
personalization at Amazon.com, and
the Vice President of Business Intelligence
at Blue Martini Software, which went public in 2000, and later acquired by Red Prairie.
Prior to joining Blue Martini, Kohavi managed
MineSet project, Silicon Graphics' award-winning product for data
mining and visualization. He joined Silicon Graphics after
getting a Ph.D. in Machine Learning
from Stanford University, where
he led the MLC++ project, the Machine Learning
library in C++ used in MineSet and at Blue Martini Software.
received his BA from the Technion,
Israel. He was the General Chair for KDD 2004, co-chair of KDD 99's
industrial track with Jim Gray, and co-chair of the KDD Cup 2000
with Carla Brodley. He was an invited speaker at the National
Academy of Engineering in 2000, a keynote speaker at PAKDD 2001, an
invited speaker at KDD 2001's industrial track, a keynote speaker at EC 10 (2010), a keynote
2012, a keynote speaker at
a keynote speaker at KDD 2015, and
and a keynote speaker at Conversion Hotel 2017.
He was an invited speaker at all five
MIT Code conferences
(Conference On Digital Experimentation) in 2014, 2015, 2016, 2017,
2018. His papers have over
citations and three of his papers are in the top
1,000 most-cited papers in Computer Science.
In 2016, he was named the 5th most
influential scholar in AI and the
influential scholar in Machine Learning.
He is currently co-authoring a book (due Q4 2019) titled
Trustworthy Online Controlled Experiments: A Practical
Guide to A/B Testing with Diane Tang and Ya Xu.
ronnyk@ live dot com