面接対策_原稿

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いよいよ明日(10/11)サンフランシスコに行き、StanfordとUSF訪問するということで、面接対策の準備で原稿をEvernoteに書いている。

以前、面接対策に記載した質問内容に対して答えを用意する感じ。

一般的な質問(なぜこの大学?なんでData scientist?)に関しては、SoPで書いていたのでSoPから引用するのみで事足りた。そう考えると、SoPは質問内容をある程度網羅しており出来としてはいいのかもしれない。一番大変なのは、自分のやりたい研究内容と、相手の研究内容の把握だ。

統計に関しては独学で少しずつ勉強しているとはいえ、最新の論文を読めるかと言われると、全然完全に読めない。「パラメトリック法」とかも最初分からなかったし、途中で数式が出てくるとお手上げになってしまうので。

もし聞かれたら、大まかな内容のみ話して、統計手法の詳細はこれから今勉強中ですと言って逃げようと思う。そもそも今の時点で完璧に理解していたら、大学院に行く意味なんてないから、その辺は向こうもわかってくれるであろう。。。


暫定完成は下記

Interest of Prof’s research

described the method of sampling an analytic cohort from EHR data and analyzing them appropriately (June, 2015)
– describe a nested case-control design to sample appropriate controls and an analytic approach using regression splines.
– The irregular and longitudinal nature of the data can make analyzing EHRs challenging

developed the Mobilize Center to advance human movement research and improve mobility
– There are no integration and analysis of this large quantity of mobility data in Japan.

Why this department?

– In the current medical field, we are still uncertain about tailored, optimal treatment, prognosis prediction, and the disease risk factors of each patient so that personalized healthcare is not being put into practice and patients are not getting the best treatment. Recommended treatments based on randomized control trials exist however they perform well just for the average patient and lack external validity in many cases because of the differences of ethnicity, age, past medical history, and so on. I found this very upsetting
– Big data analysis, combined with machine learning methodologies, enables personalized, actionable predictions. It can play a crucial role in reducing medical uncertainty
– enhancing people’s well being through individualized healthcare data analysis.

why do you believe this program is right for you?
– laboratories there engaging in research that aligns with my interests.
– specialized courses in analytics for big data and data driven medicine
– the opportunity to do large-scale computing is rarely found in Japan
– Stanford students’ access to Amazon’s EC2 cloud platform
– internationally high reputation with top-notch students

Tell me about yourself
who you are
– kazutaka yoshinaga, 26 y/o
– doctor at Keio University hospital, Japan

skill
– knowledge on medicine (will specialize in radiology)
– programming skill (C++, Swift, Ruby, R, etc)
– what you have done that is outstanding

– created software application called “Flixy” which aims to raise patients drug adherence
– synchronizes a drug case with a mobile app via Bluetooth, automatically recording the patients’ drug intake.
– not become too modest

What are your career goals
– to engage the healthcare IT industry and create a system that automatically evaluates disease risk from patient data.
– This system would extract similar cases from accumulated data and predict what physicians would want to evaluate in a new case. A tool that could calculate disease risks, optimal treatment, and prognosis from patient data such as age, sex, physical data, and preexisting conditions would revolutionize evidence-based care.

what are your weaknesses?
– say something that will not come across as entirely bad
– (例)Sometimes I speak my mind before I think something completely through
– (例)I tend to focus on details, at least initially, so it takes me longer to get a broader perspective

What are your strong points?;don’t list too many (2-3)
patience
– belonged to department of anatomy
– Investigated how the functional cellular community of cerebral cortex develops

sociable
– Engineer Internship at company which creates electronic medical record
– enjoyed working together as a team of that company, which comprised of people from different cultural backgrounds, including Japan, France, the United States, Ukraine, Poland, and other countries

Why do you think you are the right candidate for our program?
– Unlike other fields, the medical field must show not only correlations but also cause-and-effect relationships, and expert medical knowledge is required to reveal confounding factors between two variables.
– Therefore, medical professionals engaging in the data analysis field would be remarkably valuable.




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