Finance permeates everything in modern society (just like computers); having a sound understanding of its principles, limitations, and potential, is paramount, as individual and organizations stand to gain and lose a great deal, sometimes not even from their own mismanagement. To me, finance embodies rigorous treatment of risk and payoff under uncertainty, through applying advanced statistical methods on lots and lots of data. Having just finished my MEng in Computer Science with a focus on machine learning, most of my experience is with (un)supervised learning, data mining, and working with various forms of data, such as search or advertising data, ontologies, images, DNA sequences etc. My background, as well as my personal interest in becoming an educated participant in the open market, makes this a very appealing field of study.
From the perspective of an engineer, I could qualitatively appreciate the simpler or more mechanical ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. However, the important issues being debated, like the evidence against the random walk hypothesis, or Mandelbrot criticizing the assumptions of portfolio theory - these are presented in literature beyond my current mathematical reach. Hence my first reason for applying to the MCF programme: I wish to learn about core concepts in modern finance, the current models in use, and their limitations, so that I can reason about them. Secondly, during my time here, I want to develop my understanding of nonparametric methods in modelling time-series data and general random processes. I'm particularly interested in Markov models and neural nets, due to the ubiquity of their applications in general data science and their lack of assumptions beyond their structure. Perhaps such an approach could offer novel solutions, or complement conventional models when the usual assumptions break down.
While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research consisted of self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method suggested originally, and then propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.
I firmly believe in lifelong learning, and that having an understanding across multiple disciplines provides the necessary insight and expertise to comprehend and ultimately solve today's most difficult problems. I seek out challenging environments and new perspectives to change the way I think; that is when I make the most important mistakes, learn the most useful lessons, and develop fastest both personally and professionally, and so I actively work towards putting myself in such situations every opportunity I get. I think the combination of both Stanford and the MCF programme is a good fit at this stage in my career, and I look forward to learning and contributing with the very best this autumn.