November 16th, 2014

Competitive Data Science, Kaggle, Kdd and other Sports

Category: Uncategorized
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Panelists:

This panel promises to be just brilliant and full of sparks!

Guocong Song https://www.kaggle.com/users/41275/guocong-song
Jose Guerrero https://www.kaggle.com/users/5642/jos-a-guerrero
Mark Landry https://github.com/mlandry22/kaggle/commits/master
Arno Candel http://www.slideshare.net/0xdata/h2o-distributed-deep-learning-by-arno-candel-071614

Bios:

Arno http://fortune.com/2014/08/03/meet-fortunes-2014-big-data-all-stars

Arno is a Physicist & Hacker at 0xdata. Prior to that, he was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named “2014 Big Data All-Star” by Fortune Magazine.

Mark Landry

Mark is a Principal Engineer, Software Development at Dell, where he provides modeling and analytical support to the company’s largest early-stage cross-department projects. A frequent Kaggle competitor since 2012, he has finished in the top 20 in six competitions. Experience gained from the platform’s diversity of problems, domains, and solutions has led to success in quickly understanding and modeling business problems at Dell and in health care. Mark is also active in Austin’s machine learning, R, and ACM SIGKDD communities.

Guocong Song

Guocong Song is a Principal Research/Software Engineer at Sharethis. His is top-ranked at Kaggle, where he achieved 5 wins out of 10 data science competitions up to 2014. Before moving to the field of machine learning and Internet, he had been dedicated to wireless communications for a decade. He received IEEE Stephen O. Rice Prize for the best original paper in 2010, and co-authored a book titled “Energy and Spectrum Efficient Wireless Network Design” that will be published by Cambridge University Press in 2014. He holds a Ph.D. in ECE from Georgia Tech and a B.S. from Tsinghua University.

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