Wonder why your Ph.D./Master's application is being rejected?

Here are some insider reasons (from a committee member) that result in such "Love Letters" 👇

- A Short Thread

1) First, you should know that sometimes, an independent reviewer is required (especially in the US) to review applications even after PIs must have selected their candidates. So, even if any bias slips through the PI, the independent reviewer will pick it up.
2) Having said this, there are three major issues that application reviewers/committees often find with your application. These three components make your applications almost challenging to assess.
3) NUMBER ONE - Incomplete Applications

This occurs when you do not upload the requested documents needed to evaluate your file. Think of a candidate that did not send a GRE score when the institution clearly requires it for admission.
4) NUMBER TWO - Error laden Essays

This is particularly interesting because based on reports from this resource person, it is not particularly about how little your experience is but how you communicate it error-free or at least extensively minimized. This gives headache!
5) "Too many errors and running sentences pisses me off and I begin to wonder if this applicant really took this application seriously or just submitted his or her application as part of many mass submissions just to make up the numbers", says this resources person. Take note!!
6) NUMBER THREE - Generic recommendation letters

This often presents itself as bland and generic. This kind of letter does not attempt to share practical engagements or activities carried out by the student in collaboration or independently of an advisor.
7) Filled with many "She is intelligent, intuitive, and hard-working". Okay? so can you put this in perspective? Can you give examples of situations where the student exhibited these qualities as claimed?

Now you know! Fix this ASAP.

#BigDaddyTweets #phdchat

More from Science

It was great to talk about reproducible workflows for @riotscienceclub @riotscience_wlv. You can watch the recording below, but if you don't want to listen to me talk for 40 minutes, I thought I would summarise my talk in a thread:


My inspiration was making open science accessible. I wanted to outline the mistakes I've made along the way so people would feel empowered to give it a go. Increased accountability is seen as a barrier to adopting open science practices as an ECR

It also comes across as all or nothing. You are either fully open science or your research won't get anywhere. However, that can be quite intimidating, so I wanted to emphasise this incremental approach to adapting your workflow

There are two sides to why you should work towards reproducibility. The first is communal. It's going to help the field if you or someone else can reproduce your whole pipeline.


There is also the selfish element of it's just going to help you do your work. If you can't remember what your work means after a lunch break, you're not going to remember months or years down the line

You May Also Like

Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details):
https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.