Compressive sensing, again

I am currently reading a paper titled 'Random projections of smooth manifolds' by Baraniuk and Wakin. From a machine learning point of view, this paper is very interesting since it deals with dimensionality reduction, a very useful technique in machine learning research. The other great aspect of this paper is its close relation to compressive sensing (cs) ideas, a topic that I have been excited about for a while.

Graduate orientation...

Graduate orientation starts from tomorrow. It is a whole day session tomorrow, from eight in the morning till six in the evening. I am looking forward to the event tomorrow, since it is the official beginning of my new life as a phd student. There are numerous other orientation events throughout the beginning of next week, till classes begin at the end of the week. That reminds me, I need to explore more class options.

Unemployment vs PhD

This is from phdcomics, http://www.phdcomics.com/comics/archive.php?comicid=1215.


Makes one wonder whether it is worth getting a PhD or stay unemployed.

Computational tools for cooking

You don't need to be surprised reading the title, it does put Computational tools and cooking together in the same sentence. One of the perks of being a new graduate student is cooking for oneself, or rather, as in my case, learning to cook for oneself. It aptly fits in with my area of interest, machine learning, since I am learning to cook, identifying patterns in cooking from my training experience, and using those ideas to further refine my cooking to perfection.

However, I am not referring to myself by the term Computational tools in the title. Besides all the traditional tools necessary for cooking, my list involves a laptop, webcam, and internet. Honestly, I don't have a good idea of the quantity of spices to use or how to cook certain food items yet, so I take help of Google to find out recipes and figure out how much of each spices to use, if any. It is not as simple as searching for a recipe though; most times I don't have all the necessary ingredients mentioned in the recipes that I find, so I improvise by abstracting information by combining multiple recipes and some experimentation. This technique has served me adequately so far.

In the extreme case, which, very fortunately, has not happened yet, I have no idea of how to go about cooking something, skype and video chat seems like a great resource that will come in handy. Just call up someone who knows how to cook, and use the cam to show what disaster you have been up to and follow directions from that person. One great advantage of this telecooking technique is that you are not limited by geography, and can potentially go international; except that time zone might be a contributing factor.

Graph theory and probabilistic inference

Graphical models and probabilistic inference have become an intimate part of current machine learning research. I came across a paper by Professor Michael Jordan that gives a great introduction to probabilistic inference techniques based on graphs. Here is a link to the paper.

Getting into research

I have set up my workspace in the office, and have started going regularly. Because of summer, many of the senior phd students are not around but will probably be back by next week before classes start. So far, I have been spending a good amount of time reading a lot of the background work that my advisor and his collaborators have done, and current work they just completed. And of course, also thinking about my own research problem.

Keys to my office space

I got the keys to my office and access card to the cs building today. After picking up the keys and card, I went into the office to claim my desk. I got a nice, lighted space on the corner, with a set of lock and key drawers as well. I am feeling pretty excited having my own office space, a sanctuary to delve into deeper thoughts and understand the intricacies of computer science. It will be a while before I become a regular at my office, but I am looking forward to it already.