John Langford studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. from Carnegie Mellon University in 2002. Since then, he has worked at Yahoo!, Toyota Technological Institute, and IBM's Watson Research Center. He is also the primary author of the popular Machine Learning weblog, hunch.net and the principle developer of Vowpal Wabbit. Previous research projects include Isomap, Captcha, Learning Reductions, Cover Trees, and Contextual Bandit learning.
Blog: Machine Learning (Theory)
Tutorial: Real World Interactive Learning at ICML 2017.
Class: Machine Learning the Future at Cornell Tech in 2017.
Tutorial: Learning to Search at HLT-NAACL 2015 and ICML 2015.
Tutorial: Learning to Interact at NIPS 2013.
Class: Large Scale Machine Learning
Tutorial: Scaling up Machine Learning at KDD 2011.
Tutorial: Learning through Exploration Video at ICML 2010 and KDD 2010.
Workshop: Organizer Cores, Clusters, and Clouds at NIPS 2010.
Tutorial: Active Learning Video at ICML 2009.
Tutorial: Reductions in Machine Learning Video at ICML 2009
Workshop: Organizer Principles of Learning Problem Design at NIPS 2007
Tutorial: Learning Reductions IJCAI2005 and MLSS 2005
School: Organizer Machine Learning Summer School Chicago 2005
Workshop: Organizer (Ab)Use of Bounds NIPS 2004
Workshop: Organizer Machine Learning Reductions TTI-Chicago, 2003
Tutorial: Practical Prediction Theory for Classification ICML2003 and MLSS 2005
President-Elect of ICML in 2017
General Chair: ICML 2016
Program Chair: ICML 2012
New York ML Symposium co-organizer: 2008 2009 2010 2011 2012 2014 2015 2016 2017 2018
Area chair/Senior PC: ICML 2004 NIPS 2006 ICML 2007 ICML 2009 ICML 2010 KDD 2010 NIPS 2010 ICML 2011 and other since.
Program committee: ICML 2003, 2005 & 2008, SODA 2008, AAAI 2005, AAAI 2007ALT 2004, UAI 2007 AIStat 2005 COLT 2008 COLT 2009 and others
Reviewing: NIPS 2001, 2002, 2003, 2005 & 2007, AAAI 2002, MLJ, JMLR, JAIR, JCSS, TCS, JACM, and others
I have worked with many students over time. In all cases, I try to learn something from them as well.
Jacob Abernethy (Professor, University of Michigan)
Alekh Agarwal (Research Scientist, MSR-NYC)
Luis von Ahn (*) (Professor Carnegie Mellon)
Ashwinkumar Badanidiyuru (Research Scientist, Google)
Nina Balcan (*) (Professor Carnegie Mellon)
Arindam Banerjee (*)(Professor UMinnesota)
Kai-Wei Chang (Professor, UCLA)
Carl Burch (*) (Google)
Anna Choromanska (*) (Professor, NYU)
Christoph Dann (CMU)
Hal Daume (*) (Professor U Maryland & Researcher at MSR-NYC)
Varsha Dani (Research Professor U New Mexico)
Nick Hopper (*) (Professor UMinnesota)
Daniel Hsu (*) (Professor, Columbia)
Nan Jiang (Postdoc at MSR-NYC
Nikos Karampatziakis (*) (Microsoft)
Matti Kääriäinen (*) (Nokia?)
Sham Kakade (*) (University of Washington)
Adam Kalai (*) (Microsoft Research)
Akshay Krishnamurthy (Professor, University of Massachusetts, Amherst & Researcher at MSR-NYC)
Nicolas Lambert (Professor Stanford)
Lihong Li (Google)
Haipeng Luo (USC Professor)
Dipendra Misra(Cornell Tech)
Joseph O’Sullivan (ex-Google)
Pradeep Ravikumar (Professor UTAustin)
Ruslan Salakhutdinov (Professor Carnegie Mellon)
Matthias Seeger (*) (Amazon)
Vin de Silva (*) (Professor Pomona)
Alex Strehl (*) (Facebook)
Jennifer Wortman Vaughan (Microsoft Research)
Vandi Verma (*) (JPL)
Yevgeniy Vorobeychik (Vanderbilt University)
Eric Weiwiora (UCSD)
Bianca Zadrozny (*) (IBM)
Martin Zinkevich (Google Research)
(*) = on work which became part of their thesis