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Personal statment
Here's my first try on my personal statemtn for grad schools. It needs a lot of work; I have about a week in which to give it that work. At this point I don't think I'll be customizing it for 8 different schools; that's a bit crazy. How could this be better? Um, yeah.
Four years ago, as I was selecting an undergraduate institution, I had very little idea of what I wanted. To call my criteria for schools 'arbitrary' would have been generous; in fact, I may have done better with a dartboard. That spring, it crystallized for me exactly what I needed - far too late, of course; applications had been due months previously. Fortunately, I lucked out, and matriculated at Harvey Mudd with every intention of going on into Artificial Intelligence.
Of course, selecting the field and getting into it were two very different matters. Two years later, after finally completing Mudd's core curriculum, I had a summer research fellowship in Machine Learning. I had hardly been introduced to the field, yet there I was with only a computer and a vague description of the problems that I was supposed to be investigating. Weeks and months passed, and even though I wrote code that nominally did what it was meant to - unsupervised clustering of discrete-valued data - I had very little idea of the ultimate goal. Numbers went in and numbers came out, and other numbers would say how similar the sets were, but that was all that I ever saw in them: numbers. By the end of the summer I'd realized my mistake: I'd jumped straight into a highly theoretical research field with no foreknowledge of what it involved, and realized quickly that what it involved was not for me. Foolishly, I set aside the idea of research altogether, and spent the next year preparing myself for the day-to-day drudgery of the software industry.
A summer internship writing document management software (about as thrilling as it sounds) opened my eyes to how much of a mistake that was. I knew that I wanted to do something more than anonymous coding with my life, but there were no obvious options for what that could be. That was until the second project in Professor Sweedyk's Computer Graphics class, where I was assigned to build a ray tracer. Within the first week of the project, I was fascinated by just how high the quality of the images I produced could be, even with relatively straightforward code changes. Two weeks through the four-week assignment I had finished the base requirements, and rather than focus on other classes, I kept thinking and reading about further possible improvements I might make. How many geometric primitives could I include? Beyond primitives, how could I effectively use polygon meshes to make realistic models? What could I do to soften up those polygons' sharp corners? Beond simple texture mapping, how could I liven up large, flat surfaces? I had to set the project aside in favor of other classwork, but I found myself looking forward to breaks, when I would have nothing more pressing to do and I could go back to the ray tracer and simply see what more I could do.
Even after I had to leave the ray tracer behind, I still tended to be preoccupied with it. However, it was only when I found myself explaining ray tracing to a curious chemist at two in the morning that I noticed that I'd discovered a potential long-term academic interest. I realized, though, that much like four years previously I'd reached a critical decision too late to really act upon it. With only a single full semester remaining, I hardly had time to get any more of the undergrad research experience that is so often touted as a prerequisite for graduate work. Even my experience with other approaches to similar rendering is still limited at best, but I'm looking forward to any opportunity to explore others.
It feels like it just kinda dies at the end, and I'm not sure how to wrap it up. Anything?
click
Four years ago, as I was selecting an undergraduate institution, I had very little idea of what I wanted. To call my criteria for schools 'arbitrary' would have been generous; in fact, I may have done better with a dartboard. That spring, it crystallized for me exactly what I needed - far too late, of course; applications had been due months previously. Fortunately, I lucked out, and matriculated at Harvey Mudd with every intention of going on into Artificial Intelligence.
Of course, selecting the field and getting into it were two very different matters. Two years later, after finally completing Mudd's core curriculum, I had a summer research fellowship in Machine Learning. I had hardly been introduced to the field, yet there I was with only a computer and a vague description of the problems that I was supposed to be investigating. Weeks and months passed, and even though I wrote code that nominally did what it was meant to - unsupervised clustering of discrete-valued data - I had very little idea of the ultimate goal. Numbers went in and numbers came out, and other numbers would say how similar the sets were, but that was all that I ever saw in them: numbers. By the end of the summer I'd realized my mistake: I'd jumped straight into a highly theoretical research field with no foreknowledge of what it involved, and realized quickly that what it involved was not for me. Foolishly, I set aside the idea of research altogether, and spent the next year preparing myself for the day-to-day drudgery of the software industry.
A summer internship writing document management software (about as thrilling as it sounds) opened my eyes to how much of a mistake that was. I knew that I wanted to do something more than anonymous coding with my life, but there were no obvious options for what that could be. That was until the second project in Professor Sweedyk's Computer Graphics class, where I was assigned to build a ray tracer. Within the first week of the project, I was fascinated by just how high the quality of the images I produced could be, even with relatively straightforward code changes. Two weeks through the four-week assignment I had finished the base requirements, and rather than focus on other classes, I kept thinking and reading about further possible improvements I might make. How many geometric primitives could I include? Beyond primitives, how could I effectively use polygon meshes to make realistic models? What could I do to soften up those polygons' sharp corners? Beond simple texture mapping, how could I liven up large, flat surfaces? I had to set the project aside in favor of other classwork, but I found myself looking forward to breaks, when I would have nothing more pressing to do and I could go back to the ray tracer and simply see what more I could do.
Even after I had to leave the ray tracer behind, I still tended to be preoccupied with it. However, it was only when I found myself explaining ray tracing to a curious chemist at two in the morning that I noticed that I'd discovered a potential long-term academic interest. I realized, though, that much like four years previously I'd reached a critical decision too late to really act upon it. With only a single full semester remaining, I hardly had time to get any more of the undergrad research experience that is so often touted as a prerequisite for graduate work. Even my experience with other approaches to similar rendering is still limited at best, but I'm looking forward to any opportunity to explore others.
It feels like it just kinda dies at the end, and I'm not sure how to wrap it up. Anything?
click
no subject
My first impressions, taking the statement apart from anything I know about you, is that you have no idea what you're doing.
Why should I be convinced you're more commited to graphics than you were for AI?
As everyone else said, the customization is going to help. Make sure that for customization, you find specific groups at schools that you would be a good fit for. Ultimately, very few students are just "let in", in my experience. Most students are picked up by a particulat group or professor,
Also, if you have a specific project idea of any sort, let them know! The best thing to do in grad school is to immediately be matched with a prof who thinks he/she can benefit from your ideas, and this starts the grant grubbing process early enough that you may get lucky enough to be funded by research early. (some schools have teaching reqs. anyways, but, generally, unless you have infinity time and patience, the less teaching you have to do the better.)
Also, with the ML bit, emphasize a bit more about how even though you knew nothing about the field, didn't even like it, you still managed to produce results (with the idea of "so, think what I could do with training in something cool!"). Clustering, in particular, impresses people far more than it has any business doing so. (at least, that was my experience. It was my worthless project for Thom that got me in everywhere I got in).
IM me if you want to talk about this more.. I've gotten involved with the selection process here, so I know a little of what at least we're looking for. (its too bad you're no longer into ML -> it'd be great to have you here!)