Tuesday, October 28, 2008

Back to books

Don't tell my wife, but I have bought some reading material since she returned to Singapore. Yeah, I'm a chronic book buyer (and sometimes book reader).

"A Random Walk Down Wall Street" is the first investment book that I read, and to this day, I constantly refer to it and base my investment philosophy of investing the bulk of my money in indices. The book is quite lengthy and goes into a diatribe of the history of investing (starting from the 16th century!). It explains financial theories such as modern portfolio theory and also includes practical advice regarding taxes and retirement funds such as IRA and 401(k)s.




I went ahead and bought some books which have been sitting in my Amazon cart for a few months now.


"The Intelligent Investor" is written by Benjamin Graham, a proponent of Value Investing. Warren Buffett calls this "the best book about investing ever written." Can't argue with the world's richest man (for now).

I browsed through "The Neatest Little Guide to Stock Market Investing" in a book store and it's a nice little package that explains financial jargon in layman terms.



While I was at it, I also got some coding books (ya'know, so that I can keep my job). Now that I think about it, I do tend to overspend on books. Ah well. These two books look interesting, and I can stand to be a better software engineer and less of a computer scientist, especially at my current position.



I will write reviews for these books after I finish reading them.

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Thursday, December 13, 2007

Why be an expert in Singapore? Seriously.

(Note: This was a blog entry which I wrote a while back before I completed my PhD program. I have made minor edits, but have left most of the writing intact. This entry highlights one compelling reason why I chose to work in the industry eventually. This led to a job search in Singapore, but ended up with me in US. But that's another story.)

My supervisor once voiced his concern when I started my PhD on whether I would be able to find a job upon graduation because I was not planning to study a more "popular" subject matter. Being fresh out of National Service and finally free to use my mind, I was eager to pursue my own interests and was not ready to kowtow to the perceived stifling of my academic life by such mundane economic reality. In fact, I was questioning his concern at the back of my mind - surely a computer science PhD would not have any difficulty in finding a decent IT job!

Due to stipulations of my fellowship, I am obliged to stay in Singapore for two years to work after graduation, but I was free to work anywhere, as long as it is related to computer science. So in my last year of my candidature, I started my job hunt in earnest. I prepared my resume and seeked out the usual suspects - I applied for industry and teaching positions first, and procured several job offers, each giving a fairly decent monthly salary as a first job. But, well, let's just say I was not tripping over my own feet to accept any of these job offers.

Incidentally, my wife started her job approximately the same time as I entered graduate school, and is thus rather suited to serve as a frame of reference for comparing job compensations between working after a Bachelors or doing a PhD before entering the workforce. After four years of working, she was moving up the career ladder, and was earning 25% more per month than the job offers I was getting. I like to point out that my wife is a Bachelors degree holder in a government job - so we are not talking about a really fast-tracked Masters in a blooming industry. Many a times I would hear about a supposedly "excellent", or "great" job offer from a local research institution or school, and would be informed that I was going to be paid a quarter less than my wife. The icing on the cake, the cream de le creme, the best part of it all, is that I would be drawing Bachelor's pay until my PhD degree is actually conferred. FYI, I was officially conferred my PhD degree in September after submitting my thesis in January. Let's just say I was not amused at this point in time.

The school does have much more attractive compensation, with a higher monthly salary as a postdoctoral researcher (although they do not pay annual bonus), a much more comprehensive healthcare plan, and the freedom to engage in any area of research. It seems like a no-brainer to stay in school if possible after a PhD in Singapore. However, postdoctoral research positions are contract-based for one to two years, and it does entail a need to secure a more permanent job after the contract ends. That means a tenure-track position, which is hard to obtain unless you are a super-star (but if so, you should be gunning for a position in an ivy league university).

I am disappointed at the job options of a PhD graduate in Singapore, and found them clearly lacking any attraction whatsoever. I find solace in achieving mastery of a subject matter after four years of research and study, but financially, it is a little disappointing.

Read and weep - Student to Professor: The Road to Tenure-Track [Princetonreview.com]

In a related thread, what makes sense in Singapore? People management.
People management is better rewarded than technical expertise. This differentiation between experts and middle-level managers in terms of rewards is an Asian phenomenon. Here, those who present the work seem to get the credit for it, regardless of who actually performs it. We live in a place and time where articulation is often mistaken for accomplishments.

- Are you too smart for your own good?. Manoj Thulasidas. Today newspaper. Aug 25 2007.

Fast forward to today, and I am enjoying showing up at Google for work. I honestly enjoy the challenge and it pays well enough for me not to worry about money. I wished Singapore could have given me such opportunities there, but in all seriousness, I will never return to Singapore for a technical job.

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Thursday, July 26, 2007

ICML 2007

The papers for ICML 2007 are now available online. As a game AI researcher, interesting papers include:

1. Learning to Solve Game Trees. David Stern, Ralf Herbrich and Thore Graepel.

Formulating a probabilistic model for nodes in a game-tree and performing best-first AND/OR search. Interesting trend in integrating statistical machine learning techniques into game-tree search techniques. See also authors' prior paper on Bayesian pattern matching in computer Go.


2. Combining Online and Offline Knowledge in UCT. Sylvain Gelly and David Silver.

UCT - Upper confidence Tree search is a promising game-tree search technique for games with high branching factor. This paper sheds some light on what does and does not work when using UCT.

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