Eric Christiansen

PhD candidate at UCSD in computer vision

I am a PhD candidate in the department of computer science and engineering at the University of California, San Diego, advised by Serge Belongie. My research interests include computer vision and machine learning. I also have a fatal attraction to sparse methods and functional programming languages.

Update: I'll be joining Google Research on March 31, 2014 as a software engineer.


While an undergrad at Swarthmore College, I worked in the summers with Gary Cottrell on a variety of cognitive science topics, thanks to whom I have an interest in machine vision and biologically inspired models. In 2008 I graduated with honors with a BA in math and a minor in computer science.

I began at UCSD in 2008, where my first focus was machine learning, working with Charles Elkan. During this time, I attended the Machine Learning Summer School at Cambridge University, where I presented a paper on theoretical machine learning.

In 2009, I switched my focus to computer vision with Serge Belongie, with whom I have since remained.

Work experience

To date, I have been in the PhD program for months, and 16 of those have been spent in internships. I find industry to be a refreshing change of pace.

2012 - 2013: Willow Garage

In June 2012 I started as a 3-month research intern at Willow Garage, working in robot perception. I liked it so much that I twice extended the internship, finally ending in March 2013. While there, I:

See this blog post for more information on the above.

While at Willow, I also organized two programs for the wider benefit of the company. First, I taught a twice-weekly CrossFit class, using equipment Willow purchased for the purpose. Second, when Willow had to lay off its kitchen staff, I organized company-wide catering, paid for by the employees. This inspired me to create Food for Thought at UCSD.

Summers of 2011 and 2010: Google

I had two 3-month internships at Google in the summers of 2011 and 2010. In 2011, I was at the LA and NYC offices, working on Google Goggles research and backend infrastructure. In 2010, I was at the Mountain View office, helping the webcrawler to detect and appropriately handle duplicate websites.

For earlier work experience, see my CV.


My primary area of research is in local descriptor methods, e.g. SIFT, which are used in computer vision to compare local regions of images. They are building blocks for many computer vision applications, including structure-from-motion and object detection and recognition.

From an internship I did at Google, I have an abiding interest in fast and/or approximate methods for nearest neighbor (NN) search. NN search is a fundamental technology underlying many machine learning techniques. In particular, I'm interested in the non-vector-space case, where methods such as projection-based locality sensitive hashing cannot be applied.

I am also strongly interested in sparse methods for image representation, including dictionary learning and compressive sensing. Such methods can be used to capture the relevant signal in images, without requiring human-designed features. In particular, I am interested in how such methods may be sped up, as it appears computational efficiency is the main blocker to wider adoption.

Publications, talks, and research code









Throughout my PhD, I've kept sane by working on a number of side-projects. The coding projects often involve my interest in functional programming, and the non-coding projects often concern human empowerment. Here are a few from 2013:






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eric at sparse dot st



4144 CSE Building (EBU3B)
UCSD Department of Computer Science and Engineering
9500 Gilman Drive, Dept. 0404
La Jolla, California 92093-0404