At one time, I thought that it was nearly impossible for a ‘normal human’ to get into universities like MIT, Stanford, and Harvard. However, of late I have been inclined to think that not only many ‘normal’ students are applying but they are also getting admissions.
Bonus Articles
Although it is not necessary for students to turn to a paper writing service or a personal statement writing service for guidance to write a successful personal statement, many successful students do at least get their essays proofread by an expert. Moreover, these students were kind enough to share their applications with future aspirants from all over the world; the personal statement.
Before you go on and read the sample Ph.D. personal statement of this student (which I know you will without reading all this :P) I want to highlight the 3 key points that you must cover inside your Ph.D. statement of purpose.
- Why are you choosing this particular area of research – clearly state why you are interested in pursuing a Ph.D. in this area. Convey your reasoning and motivation by giving concrete examples of relevant projects, research work, etc. that you have done in this area.
- Why do you have chosen to apply to this particular university and department? Is there any reason why you are applying to this program, such as special research facilities/equipment or faculty, etc. that appeal to you?
- Short and Long term Career objectives – at this point you may not have a clear idea of where you want your career to be after completing your PhD, however, you should at least have some loose ideas that you must write down in your statement of purpose.
Here is the personal statement of the MIT PhD Student.
In today’s world, few have the chance to complete high school, even fewer get to college, and only a handful are fortunate enough to pursue graduate studies. Born and raised in a developing country, I have witnessed firsthand the disparity between the haves and the have-nots. Over the past five years, my academic and professional endeavors are an attempt at fulfilling the responsibility which comes with my good fortune of being in the “haves”. In what follows, I discuss how these experiences have shaped my interests and led me to pursue an academic career in Operations Research.
Related Personal Statements
My scholastic voyage began with two research internships I had at Cornell during the summers of 2004 and 2005. I worked at the Laboratory for Elementary-Particle Physics (LEPP) studying particle trajectories under electro-magnetic fields. My work resulted in a research paper titled “Emittance & Phase-space Distributions of Electron Bunches in Energy Recovery LINAC”, which I presented at a LEPP seminar. In the summer of 2006 I declared my undergraduate major in Engineering Physics. This degree fulfilled my need for mathematical rigor and my interest in the natural laws governing particle interactions. I subsequently began research at Cornell’s Laboratory for Plasma Studies studying the effects of thin-wire etch techniques on uniform plasma expansion and developed software to analyze multi-wire experiments.
My first exposure to Operations Research was a course I took in linear-optimization during the fall 2006. After studying the Simplex method, I drew closer to the subject, a seemingly simple algorithm boasting an impressive reputation for solving most real-world problems in polynomial time. As the semester progressed, I was able to draw beautiful parallels to familiar concepts from Physics; linear-programming bore an astonishing resemblance to Lagrangian multipliers used to find extrema of constrained functions, and duality seemed to be to linear-programming what Fourier transforms are to signal-processing.
During my junior year, I began pursuing a concentration in Operations Research alongside my coursework in Physics. In the summer of 2007, hoping to experience firsthand the application of the field’s tools in finance, I began an internship as an analyst with BlackRock’s Financial Modeling Group. There, I developed a time-series smoothing application for risk-analytics and implemented a Kalman-filter to validate time-varying volatilities estimates. The highlight of this experience came at summer’s end when I presented my work to the entire modeling group. This very positive first presentation experience in a professional setting kindled my interest for teaching. Returning as a senior that fall, I became an Academic Excellence Workshop facilitator for Calculus I, and subsequently for Linear Algebra. These weekly courses supplemented students understanding of course material by having them practice on a challenging set of problems. Overall, teaching students and helping them solve problems was a unique experience I was fortunate to have.
Following graduation, and upon receiving a fellowship for academic excellence and graduate teaching-assistantships for courses in “Monte Carlo Simulation” and “Spreadsheet-based modeling”, I decided to pursue my Masters degree in Operations Research at Cornell. That summer I worked as a quantitative developer at Milcord LLC, a geospatial intelligence and knowledge management solutions company in Boston. My work involved developing belief-network models to predict geographic changes in insurgency, and prototyping a dynamic risk-avoidance, GPS using a variant of Dijkstra’s algorithm. This experience helped me understand just how much industry relies on academia for insights into solving complex problems, and prompted me to pursue graduate coursework in “Discrete Models” and “Service Systems Modeling” back at Cornell. The first course was built extensively on the foundations of linear-optimization, with a specific emphasis on graph theory, traveling salesmen problems, and a variety of network algorithms. The second course developed, through lectures and individual case-studies, the applications of optimization and queuing theory to radiation-therapy, ambulance deployment, and call-center staffing.
Beyond coursework, perhaps the single most defining experience I had as a graduate student was a semester-long group project sponsored by Iowa farm-owner, Clay Mitchell, and supervised by Prof. H. Topaloglu. Historically speaking, the farming industry has been constrained by domestic animals’ use as a source of power. Still, recent advancements in technology have opened the doors to a sea of innovation and possibilities. Our research aimed at assessing the feasibility of a farm-yield optimization by redistributing eroded top-soil from low-lying areas. We began by framing the problem as a series of “soil pick-up and drop-off” requirements satisfied in some optimal manner. Our first breakthrough was realizing that by assuming prior knowledge of these requirements, the problem simplified into finding a minimum-cost traversable path. Further, by discretizing the farmland into soil “supply” and “demand” regions, the task of generating profit-maximizing requirements assumes the structure of a classic assignment problem.
I took the initiative to develop an integer-program which solved for Manhattan-paths that fulfilled soil-redistribution requirements for this project. As it turns out, solving this formulation optimally was intractable for any reasonably sized grid and requirement set, and thus I proposed an iterative method for solving the problem. Subsequently, we used simulations to develop criteria that guaranteed no substantial loss in optimality. The highlight of working on such a novel project was being able to dissect formulations to understand the sensitivity of our solutions to perturbations in key parameters and constraints. Our project won 2nd place in the Silent Hoist & Crane Company competition for best Masters-of-Engineering project, and received special coverage in the “Cornell Daily Sun” newspaper.
Recent decades have seen a fundamental shift in the market landscape, with e-commerce businesses like Vistaprint taking full advantage of the internet. Being particularly intrigued by its value proposition and marketing strategy, I began my first full-time position with Vistaprint’s Customer Analytics department in July of 2009. Thus far, my work has entailed using an assortment of clustering and predictive modeling techniques to develop preference models and create a performance monitoring system for key business metrics. Here, not only have I acquired a diverse set of analytical skills, I have also developed an admiration for research in its ability to tackle complex problems arising in practice. This, along with my teaching experiences, has inspired me to pursue a PhD in Operations Research.
Through my coursework and internship experiences, I have developed interests in stochastic modeling and data-mining. Specifically, I am interested in pricing and capacity allocation problems arising in revenue-management, as well as dynamic lot-sizing models that govern order-replenishment in supply-chains. Because decision problems in these settings involve complex networks, multiple periods, and a large number of products, I have also taken a keen interest in stochastic approximation methods.
I believe MIT’s Operations Management program is particularly well-aligned with my interests, and find Professor Farias and Perakis’s research in pricing and revenue management particularly appealing. In “Optimal Bidding in Online Auctions”, G. Perakis et al. adopt a dynamic programming framework to derive exact optimal bidding solutions for both single and multiple items in an online auction. The authors test their results on real data from eBay’s website and show how the optimal solution outperforms static heuristics, which have become industry standard. Today, competition in the marketplace is compelling companies to become increasingly creative in persuading their customers to buy. Apart from the familiar limited time and price hurdled offers, online auctions have become an integral part of e-commerce business in the last decade. This paper demonstrates how OR is both changing the way we think about such problems, and continuously improving existing solutions to drive efficiency and profitability. As an aspiring academician, I look forward to contributing to this effort.
The interdisciplinary nature of OM at MIT Sloan stems from its faculty and students; in turn, this enables the program to span the spectrum of cutting-edge applied and theoretical research in OR. Given the range of professional experiences I have had over the years, being a PhD student in this department would be ideal for my academic career. I strongly believe that this program will fulfill my passion for applying mathematical theory to solving complex problems, but will also allow me to leverage my analytical skills and industry experience to contribute to the program’s intellectual diversity.