About

Welcome 🙂 Greetings from Cecilia.

I’m currently a first year PhD student in Computer Science at UC Berkeley. YAY CAL weather 🙂 My research interests lay in Computer Vision, Computational Photography and Computer Graphics.

I did my undergraduate at Rice University with a BS in Electrical and Computer Engineering. I was a researcher in the Computer Vision lab at Rice University led by Prof. Ashok Veeraraghavan, working on a Computational Photography project based on Fourier Optics.

One of my favorite research projects is a virtual fitting system designed for online clothing shoppers. I have done other interesting course projects related to network communication, digital signal processing and embedded system. I like building real-life applications and systems, especially using a machines’ vision to learn a useful interpretation of the scene, of the people and of the world.

This website is for sharing: 1) knowledge and lessons acquired in related areas 2) my feelings as a researcher 3) exchanges of ideas and thoughts. So please feel free to comment!

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2 thoughts on “About

  1. Your tutorials on caffe very good. I am hoping more to come.
    Can you suggest me how do I start learning caffe? Other than the documentation on caffe’s website there is no other good source of learning it.

    • I’m not sure if you notice the ipython notebook examples given in caffe. Those are very hands-on examples that get you started on everything, including preparing data, build a network architecture, and doing inferences, etc. There is no easy way to get familiar with it other than trying examples yourself, and discover problems/bugs and google them to find solutions.

      Also caffe is just a framework and tool that helps you solve a problem suitable for deep learning. Any other tools like Theano, Tensorflow or Torch would achieve the same thing if you have a problem to solve in mind. The main point would be to know what problems you are targeting, and also a clear understanding of the fundamentals behind deep learning (e.g. back propagation, the effects of different functional layers)

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