SoP ver6、個人エピソード挿入

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StanfordとUSFの教授に研究室見学のアポイントのメールを送って2日経過したけど、返信が今だにこない。。どうしたものか。どちらにしろ大学見学はするしな。。突撃かな

内容が悪かった?メールにしては文量が多すぎた?手紙で送るべきだった?などなど、考えることはつきない。おそらくもう返信はこないだろうが、もう数日待ってみようと思う。後はMITには手紙で送ってみようかなと


今日は、以前SoP 引きつける志望動機書とは?で記載したように、SoPの編集を行った。具体的にはTEDストーリーをまねて、データ解析を志すようになった個人的エピソードを混ぜた。若干作った部分はあるが。

ところで、Conclusionのしまりが悪いので、「SoP conclusion」で調べるとWhat should be the flow of thoughts in a statement of purpose (SOP) for graduate admissions?のQuoraに下記の記載があった。以下抜粋

As a faculty member who’s been working on admissions for nearly a decade, and my other colleagues from other schools will confirm this, the SOP is the LEAST useful and impactful part of your application

You can demonstrate your interest and knowledge by picking one or two professors and contacting them beforehand and speaking a bit about their research (it’s best if they’re in the same sub-discipline) in the statement and how it connects to other aspects of your education.

As an example, compare these two one-liners:
・I am fascinated by supersymmetric theory and how it can answer questions about quantum gravity.

・I am fascinated by how the recent discovery of the Higgs boson has made important implications for the possible discovery of supersymmetric particles at the LHC and how this discovery has made the upcoming run of the LHC a critical test for both supersymmetry and for theories beyond the Standard Model more generally.

Most applications have the first as their level specificity and while it’s not an application killer, but it’s pretty blah.

SoPが殆ど考慮されないという言葉も意外だったが、教授とのコンタクトメールで、完全に前者のようにバズワードを並べただけの薄っぺらい研究への関心を書いていたことに気付かされた。今後アポイントをとる時はもっとその教授の論文を読み込まないといけないと痛感した。

が、問題は分野が専門過ぎてかつ、今の自分の職種(医学領域)とは異なるため完全な論文の理解が厳しいということ。。。。統計の独学を進めるしかないのかな。。

ちなみに現在のSoPは下記(バージョン6,741words)

Personal Statement

  Enhancing people’s wellbeing through individualized healthcare data analysis is the ultimate goal I aim for in my professional career. By accumulating and analyzing a vast number individual healthcare data, I believe that optimal and personalized healthcare information feedback is possible. I seek to attain this goal through working as a data scientist with a medical background.
  During medical school, I taught myself computer programming and developed an information-sharing and community-making web service for medical students. Since then, I have been fascinated with programming because we can create something new from nothing. I kept teaching myself programming, such as C++, JavaScript, Ruby and others. I also developed a hardware called Flixy, which aims to raise patients’ drug adherence. It synchronizes a drug case with a mobile app via Bluetooth, and automatically records drug intake of the patient (web site: http://flixy.co/). I also taught myself R language, and showed my competency through an internship in a health insurance company. I analyzed the health checkup data and medical expenditure data, and improved the efficacy of the company’s prevention program. From these experiences, I came to believe that IT is strongly potential to improve the quality of healthcare, and I myself want to engage in this field.
  After university, I started working as a clinical physician. Working as a physician was rewarding, and I took pride in providing recommended treatments supported by medical paper. I researched paper every time when I encountered clinical problems, and I came to believe without a doubt that having medical paper behind is the most significant matter to make best clinical decisions. One day I encountered a case of young male with intraventricular thrombus having developed acute cardioembolic stroke. The role of immediate anticoagulation in this case is controversial. I researched papers as usual and found some. As I read these papers critically, however, I noticed that randomized control trials described in these papers were not applicable to the patient at hand. This patient was significantly younger than the studied population, and his race was different. Moreover, his past medical history did not meet inclusion criteria of these studies. I got perplexed. How on earth can I decide optimal treatments for this patient? Medical papers I believed so far did not back up me in this case. I am still not sure whether I provided optimal treatments for this patient. Since then, I experienced similar cases many times. Physicians cannot always provide the personally-tailored, optimal and evidence-based treatment for each patient—and I found this very upsetting. Recommended treatments based on randomized control trials are mostly one-size-fits-all models that perform well for the average patient. However, these lack external validity such as race, age or preexisting conditions in many cases.
  On that occasion, I watched TV program featuring big data driven medicine. It showed an example of big data driven system which predicts infection in babies in neonatal ICU. Introduced medical revolutions using big data analysis really amazed me. I have also read that big data analysis, combined with the machine learning methodologies, enable a personalized, actionable predictions. Therefore, I came to strongly believe that big data analysis plays a significant role in reducing medical uncertainty. Furthermore, my acquiring more profound knowledge on data analysis is imperative in achieving my goal, and the significance of a person with medical background engaging in data analysis field is remarkable.
  I got deeply interested in Stanford program because there are several laboratories that share similar interests with me, such as labs of professors Wong, Hastie, and Tibishirani. Furthermore, this program offers not only general statistics courses, but also domain-specialized course, which is unique to this graduate school. The courses “Analytics for Big Data”, and “Data Driven Medicine” are especially of my interest. Furthermore, the internationally high reputation of the department attracts diverse and competent students, and I believe studying in this environment will be very stimulating and exciting.
After completing my degree, I plan engage in healthcare IT industry, and create a system which automatically evaluates disease risk from patient data. This system calculates the disease risks, its optimal treatment and predictive prognosis from patient data such as age, sex, physical data and preexisting conditions. It would also extract similar cases from previously accumulated data and predict what physicians would want to evaluate in the new case.
  I look forward to joining Stanford University as a graduate student at your esteemed department, which would be a great step towards my goal.

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