Shawn Lankton Online

In this three-part series I’ll give you a how-to for getting an interview, preparing for it, and dazzling the interviewers once you’re across the table. These are the main topics we’ll cover:

• Part 1: Branding Yourself and Making Making a “Wow” Resume
• Part 2: Preparing for Your Case Interview to Get Bulletproof
• Part 3: Talking about Your Experience and Sounding like a Bad-ass

Leaving academia and joining consulting firms is a something many PhD students (myself included) are getting interested in. Firms like McKinsey & Company, Boston Consulting Group (BCG), and Bain & Associates once hired mostly MBAs but are now branching out to hire MDs, lawyers, and PhDs.

I wanted to make a big impact with the skill I learned during my PhD. I got excited when I heard about consulting because it promises just that. In the next three parts, I’ll take you through the big lessons I learned while preparing and interviewing: How to get an interview, how to nail the case, and how to dazzle them with your experience.

Part 1: Branding Yourself and Making Making a “Wow” Resume gives you pointers to polish that scruffy science look off your C.V. and generally control your “personal brand” so that interviewers are impressed with you long before you walk in the door.

Part 2: Preparing for Your Case Interview to Get Bulletproof talks about how to approach the case and how to practice so that you can shine while others look dull. I’ll give you some simple exercises that will improve the structure and creativity of the “case” portion of your interview.

Part 3: Talking about Your Experience and Sounding like a Bad-ass covers an important and often overlooked portion of a consulting interview… talking about yourself! I know you have some amazing stories to tell. This sections shows how to make your stories say the right things about you.

Please enjoy!

Disclaimer: I recently went through the application and interview process with a top firm, came out with an offer, and signed it! In this series, I share my experience and give some ideas for people on a similar path. However, at the time of writing (July 2009), I do not have any inside information on how any company conducts their hiring. These are just my thoughts!

This is the first part of “A PhD’s Guide to Getting a Consulting Job,” because personal branding and resume building can help you the most! Having a great resume and a powerful personal brand is necessary to get in the door, and if you do it right, you may have the job before you even start talking.

First I’ll talk about how to build your brand, then I’ll share my tips on resume writing.    Click to continue →

So far in the “PhD’s Guide to Getting a Consulting Job,” we’ve covered how to work on your personal brand and write a resume that will get you an interview. Now, it’s time to get bulletproof for the interview itself.

Case interviews are an interview tool that consulting companies use to gauge how your analytical skills. Essentially, the case interview involves answering an open-ended question about a business problem. The interviewer gives some background on a (hypothetical) company that needs help. Then the candidate talks through an analysis and solution.

I’m going to show you how to rock a case interview.    Click to continue →

We have talked about how to get an interview and how to nail the case section. The last step in the “PhD’s Guide to Getting a Consulting Job” is discussing your personal experience in a way that is clear, compelling, and shows that you’ve got what it takes to be a consultant.    Click to continue →

Knowing about new research in my field helps keep my work informed and relevant. However, I rarely remember to log into IEEE Xplore, Springer, or Science Direct to see what’s new in top computer vision journals. Recently, I saw mention of using RSS to keep up with research on Productive Scholar.

It took a bit of searching, but eventually I found RSS feeds for many of the journals I’m interested in and loaded them into google reader. It is now quick to scroll through new abstracts as papers appear on-line prior to publication. Below are links to RSS feeds for some computer vision journals I’m keeping up with.

RSS Feeds for Computer Vision Journals

Finding RSS Feeds for Other Journals

It takes a bit of hunting sometimes, but I can’t imagine that a journal would not have RSS these days. IEEE Journals are easy to find, and I found that inezha.com was a good resource for finding some of the other ones I have listed.

Any good feeds I missed?
Other good ideas for keeping current?

Leave them in the comments.

Active contours are a method of image segmentation. They are well-loved for their accuracy, ease of implementation, and nice mathematical underpinnings. However, a full level-set implementation can be quite slow, especially when dealing with large data! Here are some tips to speed things up. By combining these ideas and solid programming techniques I’ve been able to get active contour trackers running at hundreds of frames per second!

Use Fast Level-Sets

Start by using a fast level-sets implementation that minimizes the number of required computations [code]. This will already save a huge number of computations per iteration and speed things up quite a bit!

Create better initializations.

The farther the initial contour is from its final position, the more computations must be done for the contour to converge. Hence, if you can start the contour in almost the right place, you’ll drastically reduce the time needed for segmentation. You can use prior knowledge, user input, or other segmentation techniques to create a rough guess that is close to the right answer. Another initialization that can leads to quick initialization is ‘bubbles’ on an evenly-spaced grid.

Use a multi-scale approach.

This is a way to quickly get good initializations using active contours. Say your data is MxN. Instead of segmenting the full data set, downsample the data so that you are dealing with an (M/8)x(N/8) volume. The segmentation should run much quicker on the smaller volume. Next, upsample the result back to MxN and use this as an initialization for the full data. The idea is that the time saved on the full segmentation by having a good estimate based on downsampled data will make up for the time needed to downsample, segment on the small data, and upsample.

Use approximate active contours.

Using an approximate solution for all or part of your segmentation can be helpful. As in 2 and 3, you can use an approximate active contour technique to quickly get close to the right answer. Then you can use an accurate level sets implementation to get the right answer quickly. Alternatively, the discrete methods can work quite well alone! James Malcolm proposed a nice method in “Fast Approximate Surface Evolution in Arbitrary Dimension” [code].

Use another technique entirely.

Active contours are “variational,” so they give nice, principled solutions with analytic geometry, etc. However, if you just want fast segmentations, other techniques such as thresholding/morphology, graph cuts, region growing, etc. can all be viable solutions.

Any other tips or links to good implementations? Leave them in the comments.

Active contour methods for image segmentation allow a contour to deform iteratively to partition an image into regions. Active contours are often implemented with level sets. The primary drawback, however, is that they are slow to compute. This post presents a technical report describing, in detail, the sparse field method (SFM) proposed by Ross Whitaker [pdf], which allows one to implement level set active contours very efficiently. The algorithm is described in detail, specific notes are given about implementation, and source code is provided.

Fast Level Sets Demo

The links below point to the technical report and a demo written in C++/MEX that can be run directly in MATLAB. The demo implements the Chan-Vese segmentation energy, but many energies can be minimized using the provided framework.

Sparse Field Method – Technical Report [pdf]
Sparse Field Method – Matlab Demo [zip]

To run the MATLAB demo, simply unzip the file and run:
>>sfm_chanvese_demo
at the command line. On the first run, this will compile the MEX code on your machine and then run the demo. If the MEX compile fails, please check your MEX setup. The demo is for a 2D image, but the codes work for 3D images as well.

My hope is that other researchers wishing to quickly implement Whitaker’s method can use this information to easily understand the intricacies of the algorithm which, in my opinion, were not presented clearly in Whitaker’s original paper. Personally, these codes have SUBSTANTIALLY sped up my segmentations, and are allowing me to make much faster progress towards completing my PhD!

Thanks to Ernst Schwartz and Andy for helping to find small bugs in the codes and documentation. (they’re fixed now!)

For more information regarding active contour, segmentation, and computer vision, check here: Computer Vision Posts

This week I made a presentation to my thesis committee at Georgia Tech to propose the content that will make up my Ph.D. dissertation. I’m happy to say that it went well and I’m on-track to graduate in September of 2009. The video below is an abridged version of the presentation I gave. It’s about 15 minutes long, and gives a general idea of the work I’ve been doing over the past three years as well as what I hope to accomplish before I finish. In a sentence, I propose a way to analyze image statistics locally that improves performance in several medical image processing applications.

On a side note, people interested in creating screen-casts of presentations on a Mac, should consider the program ScreenFlow, which worked great for me! This was also my first presentation created with Apple’s Keynote software, but I’m sure it won’t be the last.

Once you get through the process of writing your paper, getting it reviewed, making revisions, and finally getting that ACCEPT email in your inbox, the work isn’t done. You must prepare the figures in the paper so that they can be easily used by the IEEE to make a nice, high-quality, correctly laid-out, final print version of the article. This process could be quite complex and time consuming.

This post will cover some tips, tricks, and scripts to get your journal submission ready to go quickly and easily. The big challenges are:

• Getting all figures into single eps files
• Creating a list of figure captions

The good news is that both of these are delightfully quick if you’ve been using LaTeX to compose your article. Read on to find out how    Click to continue →

I return today from a week-long trip to Anchorage, Alaska. I spent the week enjoying the beautiful mountains, and the exciting science being presentented at the Conference for Computer Vision and Pattern Recognition (CVPR 2008) [here are some links to lots of papers from the conference]. This was my first trip to this conference, and I must say that I was impressed with the quality of the work presented. Below, I list some of my favorite papers and give a (very) brief overview:

   Click to continue →