My name is Robert. I am an undergraduate at the University of Cambridge studying Physical Natural Sciences (Physics, Computer Science, Mathematics, Materials Science).
I am interested in finding intuitive quantitative explanations for complex phenomena, even if those intuitive explanations concede a lack of determinism. It is always nice to find order in complexity, but I think we should accept that it may very well be complex all the way down. I’m a logical person who holds rationalism dear. I have traditionally considered physics and mathematics to be my fortes, but I a have been actively expanding that to computer science and finance.
The best and worst thing about computers is that they do exactly what you tell them to – they are flawless in theory, if they are coded by a theoretically perfect person, but I’d take buggy code over by-hand calculations any day.
Whenever I need a computer to help me, I first turn to python. I have been using python (primarily for scientific purposes) for about 5 years, and its initial charm has never really faded. With its pseudocode-like syntax and extremely clear ethos (type
import this into a python console), it lets you get your ideas into a computer with relative ease. Of course you aren’t going to get C-like speeds natively, but nowadays there are options like cython or Numba which can alleviate this to a certain extent. I’ve built a couple of full-stack projects using python (Flask), and of course CSS/HTML/JS, but if I’m honest I don’t get as much of a kick out of frontend work as I do with pure python.
However, I concede that R has a better statistics ecosystem – there is an R implementation for basically any useful theorem/procedure in statistics. I have used R for a number of data science projects, and I do appreciate its intuitive data processing. As long as there are people who are willing to re-implement R packages in python, I think R’s small lead on the statistical front won’t last forever.
I have also frequently turned to ‘knowledge based computing’, a nice euphemism for asking Mathematica what the answer is. I’ve dabbled in C++, and would love to become more proficient. I also taught myself Java, partially because that’s what Cambridge will teach in their first year for computer science, and partially because pain builds character. Yes it’s fast, and the JVM is seriously impressive, but writing Java doesn’t really me happy.
Finance is inherently interesting to me, because it represents a quantification of the beliefs and wills of so many individuals (point particles if you like) – and in my mind, quantification is the first step to prediction and/or understanding. Of course, a nice side effect of prediction is that you can make money from it.
I have spent a lot of time building predictive systems based on machine/deep learning. I’m not naïve: I know that whatever I do has probably being done far better by a quant fund somewhere. But regardless, I do think it’s interesting to test the limits of statistical inference in such a complex and dynamic system.
One day I’d like to be able to use my knowledge of physics and mathematical methods to be able to properly find order in financial markets, or failing that, to quantify the chaos and understand how to mitigate its effects.
I try not to limit my reading to specific subjects – I think it can be incredibly valuable to continuously attempt to read about subjects that are new to you, that way you know what you don’t know. I think this also applies to a lesser degree with fiction.
Below are some of my favourite books (or short stories); I suppose there is an overrepresentation of science fiction, but I can’t apologise for being fascinated by it
On top of the very general ‘physics and maths’, here are some specific interests (in no particular order):
Either doing supervision work, reading about finance, or building software projects!
October 2018 Started at Cambridge! I spent the previous months trying to learn as much as I could about finance and statistics in the meanwhile, because I knew that I’d probably be mostly occupied with physics and maths at uni.
Finished national service!
Passed out from the Medical Response Force conversion course, which was 7 weeks of very tough training. MRF is a unit of specially trained combat medics who deal with chemical, biological and radiological threats. It is part of the Special Operations Taskforce, so we are expected to be operationally ready at all times. I am now a specialist, as well as a trained military driver.
Enlisted into the army. In Singapore, there are two years of compulsory national service (NS) for citizens or permanent residents (that’s me). Basic Military Training was until September.
Accepted to study the Physical Natural Sciences at Christ’s College Cambridge, with two years deferred entry (entry in October 2018)
Graduated from high school, having completed my International Baccalaureate with my three higher level subjects being maths, chemistry, and physics, and extended essay in maths – I scored 45 points overall.