New blog layout

I have decided to try a new layout for my blog. If you came here randomly or have been following my blog, either way, please let me know if you like this layout...

Using mercurial (hg) for local development

Decentralized Version Control (DVCS) is one of the most useful tools if you are working with multiple people on a project. I used to use version control (such as subversion and git) to not only keep track of code for my research, but also while drafting papers. At work, we use subversion (svn) to maintain our large codebase, but having local versioning is very helpful while developing features. For local version control, I have been using mercurial (hg), which by the way is quite easy to use, and personally I think feels like a mix of svn and git.

When you check out a copy of a codebase from your project's svn trunk, for example, and you decide you want to add a new feature, branching is the way to go and the quick learning curve of mercurial will help get you started.

When you initially add all your files to mercurial, it becomes the "default" branch. For every feature you are going to develop, you can create a new branch and tag it with a name (say "feature1"). You can switch to the "default" branch when there is new code pushed into the central svn repository and you need to do an "svn up"; you can add remove any new files for mercurial and then simply merge it back with your "feature 1" mercurial branch (of course resolving any conflicts). Once "feature1" is complete after all the code reviews and everything that gets needed done, just switch to the "default" mercurial branch, make sure it is updated, and merge your "feature1" branch with it. You can push in your added feature via svn to trunk, and close off the mercurial "feature1" branch.

The basic idea is simply to use named feature branches to work on your new features, and use the default branch to perform updates, propagating to your working branches, and when you are ready to check in your newly developed feature to codebase.

Using mercurial locally on top of a checked out svn repository has been working quite well for me. So I wanted to quickly share, without actually showing you the nitty gritty details, how you might go about using a similar versioning workflow. Maybe in a later post I will sketch out the details with the mercurial commands.

A retrospection on transition to industry

I have been working in the software industry for about a month now. So I thought this might be a good time to put down some of my thoughts in writing. To summarize, the transition to industry has been a very good learning experience so far.

Honestly, I am a complete newbie in industry. I did not even have an internship experience in industry before I took off from the Phd program. Although, I did have experience working on a large scale software project that helps scientists/researchers to perform large scale data analysis as part of an academic research experience, but working on software that gets deployed to production and, hopefully, reaps some profits in is a very different world. My experiences have been broadly in academia and in research in the past, and though my proficiency in various programming languages are more than adequate, after starting work in industry as a colleague with industry experts, I realized that there are many things for me yet to learn.

I have picked up a lot of things in a month, especially the myriad set of tools that are used to streamline the whole development to production process. I definitely have a much broader perspective on software now, and coupled with my background in research and academia, I feel more complete as a professional. My startup stint might have been the missing piece to my goal of becoming an accomplished computer scientist.

f8

I was at work when f8 started today in the morning. But I did get glimpses of the live presentation showing some of the new technologies that Facebook is planning on rolling out. I think they have some very interesting products in mind, such as Timeline, Ticker, and the concept of Open graph.

The proposed changes seem intriguing, but I wonder how this will affect the nature of social networks. There is the technology aspect of the products, but I am more concerned about the type of privacy features that are getting bundled with these ideas.

Code coverage plugin for eclipse

I needed a code coverage tool to check how good of a coverage my unit tests were doing. Looking around a little for a code coverage tool for eclipse, I came across ecobertura. I only played around a little with this, but from first looks, it seems quite handy. It gives you a nice highlighting of green for lines of code that got executed, and others in red. There are some other features that I did not get a chance to check out yet. One problem is that there is no way to toggle off the code coverage tool, probably something the author has in mind to add I am sure.

If you decide to use it, please let me know how it goes for you in the meantime.

Vim like editing for eclipse

I am using eclipse at work for development, and although eclipse provides many excellent functionalities, being a vi/vim person myself, I was missing the elegance of developing using a vim style editor. One of the senior engineers at work, being himself an avid vi/vim user as myself, pointed out an eclipse plugin for vim called vrapper. It is not complete vim, but has enough functionality to keep me happy. I have been using it for about a week now, and I am liking the eclipse functionality with a vim style editor.

You can install it from eclipse marketplace. If you are using one of the relatively newer eclipse IDEs, you should see a link from "help".

Computational Advertising class @ Stanford

Since I have decided to dabble into the problem of ad serving, I have been looking at this class at Stanford trying to get a sense of what is out there currently in the real world, and what are the lines of thought of researchers in the field. Here's the link if you are interested.

A brief detour

For those of you who might have stumbled upon my blog previously, you would probably remember me as a PhD student in Machine Learning in California. I have decided to take a detour from my PhD program for a stint as a Software Engineer. I am working in California, primarily focusing in applying Machine Learning in building high value scalable products for users, and with a goal in distributing them at a massive scale.

At the moment, I have decided to delve into the problem of Ad Serving. All of us engineers in this area are basically working on the problem of how to connect advertisers, publishers, and users in a manner that is profitable for all involved, and to do all that in an efficient manner.

How are our algorithms surviving in the stock market?

With the current high volatility of stock prices, I wonder how algorithms predicting stock prices are surviving. The ups and downs over the last few days have been pretty significant, and the robustness of algorithmic predictions, in general, is something that I am curious about. From a machine learning perspective, I am interested in knowing how such techniques are surviving in the current harsh world of stock market.

Anybody has any ideas on this matter?

Interesting TED talk on how algorithms are creeping into our lives

My post on "Prelude to postings again" has been over a month old. In the spirit of blogging, here is an interesting TED talk on how algorithms are becoming a part of our daily life. People in data mining might find this talk especially attractive.