|Li Quan Khoofirstname.lastname@example.org | email@example.com|
Through investment, we collectively determine the survival of ideas, and consequently, for better or worse, it reflects our priorities for improving the world, be it in technology, infrastructure, or healthcare. To this end, I believe the role of finance is to make the process of investment as practical and as informed as possible, for when managed correctly, wealth is transformative - it is opportunity itself.
I'm a computer scientist with a focus on machine learning, so I work with data-driven methods that improve decision-making. I started looking deeper into finance when I realized a subset of scenarios lend themselves naturally to being modelled in this way, which may provide additional robustness against a constantly evolving market - in fact, MIT's Professor Poggio emphasized the importance of generalizability for any financial forecasting method in 9.520 Statistical Learning Theory. My aim is to capture the relationship between prices of securities more explicitly, beyond relatively simple statistical descriptions, through the application of several methods (which I detail in the 250-word essay) from the deep neural net repertoire, which has had tremendous impact in signals analysis. In doing so, I hope to improve asset management, and financial modelling tasks in the time or frequency domains.
I've decided to pursue the MFin degree because my line of inquiry lies firmly within the domain of finance, therefore it is a definite prerequisite to working in any professional capacity within the discipline, and to see the above ideas realized into practical business applications. As to why I chose MIT's MFin degree, an aspect of the MIT experience could be described as a diverse set of people and ideas, united in advancing professional practice, and improving the world (and each other) in the process. Professor Lo teased in 15.401 that it would challenge our preconceptions and completely change the way we think, and that's exactly what I'm looking for.
Most of my prior experience was in research and development, both in university and in industry, with a couple of side projects in software development and hackathons. After my first exposure to an industrial research setting with Microsoft Research, I joined DigitalMR to salvage a feasibility study on image sentiment analysis running behind schedule - a project which I felt was the high point in my professional history. I had to first convince the project stakeholders to forgo the overly-optimistic method suggested originally, and then draw upon recent literature to propose and follow through with a new model, which would yield the most useful results in the time remaining (it did), while advising management throughout the project about commercialization.
I share MIT's belief in lifelong learning, and I firmly believe that having an understanding across multiple disciplines yields the necessary insight and expertise to understand and ultimately solve today's most difficult problems. I think the combination of both MIT and the Master of Finance programme is the best fit at this stage in my career, and I look forward to learning and contributing with the very best this summer.