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June Guo
Worked at Delphi Automotive
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June Guo

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+Carol Reiley and I are getting married! Engagement pictures in IEEE blog.  With robots. http://bit.ly/1mHZB2d 
Yes, you really can find love at an IEEE robotics conference
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June Guo

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Building a big data dream team

Today’s analytics specialists must have technical chops and be savvy enough to convey results.  Mix one part software engineer, one part big data architect, one part business analyst and one part computer systems analyst. Read more: http://dell.to/1fM5qpS.

What do you think? See any missing pieces to this approach?

#techpageone   #bigdata  
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June Guo

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Reading in liberal arts really helped me building my story. 
 
A story is up to 22 times more memorable than facts alone. In this video, Professor Jennifer Aaker shares key elements to keep in mind when crafting your company’s story: http://stnfd.biz/sOMJ2

1) Develop a goal: Why are you telling the story in the first place?

2) Grab attention: Why would the audience want to listen?

3) Engage: Why would the audience care?

4) Enable action: Why would the audience want to share the story?

On February 10th, Professor Aaker will share more storytelling insights with #StanfordGSBAlumni at “The Business of Change: A Conversation with GSB Dean Garth Saloner” event in San Francisco. Alumni, learn more and register here: http://stnfd.biz/sON37  #GSBboc
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VMX Project and gaming.  Want to use physical objects for gaming on your computer?  In this video we show us using hands for pong controllers using only a webcam.  Without our VMX webapp, you can build a new generation of games by integrating everyday objects into your games.  Requires no extra hardware besides laptop, unlike #leapmotion  ( #leapmotioncontroller  ) #kinect   sensors.

#gaming   #entertainment   #videogames   #computervision   #machine_learning  

Checkout full video on Kickstarter:
http://www.kickstarter.com/projects/visionai/vmx-project-computer-vision-for-everyone
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OverFeat classifier / feature extractor + New detection state of the art on ILSVRC13.
http://cilvr.nyu.edu/doku.php?id=software:overfeat:start

Today we are announcing the release of OverFeat, our convolutional net-based image classifier and feature extractor.
We are also releasing new record-breaking results on the ILSVRC13 detection task.

OverFeat is a C library (with source code) that runs pre-trained ConvNets. It can be used to extract dense features in images or to classify objects from the ImageNet 1K categories. It is provided under a non-commercial license, together with example programs, demos, wrapper scripts, and two ConvNets trained on the ImageNet 1K dataset.

This release includes two trained ConvNets: (1) A large network which is accurate but slow, (2) a smaller network, which is faster but slightly less accurate. The large/accurate network yields 14.71% top-5 error rate on the validation set of the ILSVRC13 classification task (ImageNet 1K) when using Krizhevsky's 10 views averaging. It yields 14.18% when the network is densely applied at multiple scales and flips and combining the outputs with a voting mechanism (which is not provided, but easy to implement). Finally it reaches 13.24% error when averaging 7 similar models (13.6% on the test set).  

The core of OverFeat is a C library provided with source code, but we also provide a Lua/Torch wrapper (with Python and Matlab wrappers coming soon). The scripts can be used to process a single image or a batch of images. The scripts can output the state of any layer in the ConvNet, including the output of the 1000-category classifier. Applying OverFeat to a large image will produce a map of feature vectors (or outputs) for regularly-spaced windows on the image. 

Concurrently, we are releasing a paper containing a new record for mean average precision (mAP) on the 2013 ImageNet detection task:
http://arxiv.org/abs/1312.6229

The paper describes the OverFeat system that participated in the ILSVRC13 and includes the following results:
- detection task: 24.3% mAP (post-competition). This establishes a new record (with UvA at 22.6%, NEC at 20.9%, and the pre-deadline version of OverFeat at 19.4%)
- localization task:  29.9% error (ranked 1st at the competition)
- classification task 13.6% top-5 error (ranked 5th at the competition)

If you use OverFeat in your research, please cite the paper mentioned above. The paper is submitted to ICLR14. 
The authors are Pierre Sermanet , David Eigen, +Xiang Zhang, Michael Mathieu. +Rob Fergus, +Yann LeCun.
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"Stanford engineers have always tackled the biggest challenges, and the past academic year was no exception." ~ Stanford School of Engineering Dean, Jim Plummer.

To read Dean Plummer's full letter: http://stanford.io/1cUad4d
Stanford engineers have always tackled the biggest challenges, and the past academic year was no exception. Stanford Engineering Dean Jim Plummer. Stanford faculty and students blazed new trails in energy, nanotechnology, bioengineering, education and many other fields.
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17 Equations That Changed the World

This is Ian Stewart's list, from his book by the same name.

What are some other choices?  I would nominate the quadratic equation, A =pi r², and Google page rank.
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June Guo changed his profile photo.

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Plan your day so that you can make big decisions in the morning; your willpower and focus are often depleted by the end of the work day. Read Professor Baba Shiv’s research on decision fatigue: http://stnfd.biz/sASNI and tips for making better decisions: http://stnfd.biz/sASO9 
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Elon Musk talks to Steve Jurvetson (MBA ’95) about Tesla Motors and why maximizing profits is not a smart idea. Watch now: http://stnfd.biz/sgG5a
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June Guo

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Lol! Happy new year! Everyone!
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    R & D, 2012 - 2013
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