Writing Cold Emails

Writing emails to a stranger could be daunting, but it's a great way to build connections, explore opportunities, and even advance your career.

How do we write an effective cold email?

Check out 🧵 below for some ideas.
*Content*

Your email should contain the following four elements.

• Greeting: "Hello"
• Introduction: "My name is Inigo Montoya."
• Context/Connection: "You killed my father."
• Call for action: "Prepare to die."
*Greeting*

Understand basic email etiquette. Do not use Miss / Mrs. particularly if you know the recipient has a PhD. Respect their expertise.

Source:
*Introduction*

A simple one would work. My name is <NAME>. I am a <POSITION> from <AFFILIATION>. I am writing to <WHAT DO YOU WANT>
*Context*

Provide SPECIFIC context, personal connection, background information in your email. Do your homework!
*Call for action*

Spell out what you expect the recipient to do after reading the email. Make it ACTIONABLE (e.g., set up a short meeting, answer a question, or prepare to die).
*Consistent format*

This pitfall is quite common in inquiry emails from prospective students. Ex:

"I am fascinated by your work <paper A> and <paper B>." where paper titles are of inconsistent font type/sizes. This almost surely indicates that you are sending massive emails.
*Your name*

Make sure that you full name "in English" appears as the sender. Don't use your favorite anime character's name or some unmemorable ID (e.g., A90291053@school.edu).
*Make it clear and structured*

Emails are not just plain texts. Make the main points stand out using bold/italic fonts. Itemize your talking points to make the email easier to read.
*Quality*

For prospective students looking for advisors, remember that your email is a WRITING SAMPLE. If your email is not clear or has lots of errors, it could actually hurt your case. Revise the emails before you send them.

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More from @jbhuang0604

27 Jul
How to make steady progress in my research?

I worked so damn hard but "IT JUST DOESN'T WORK!"😤
How can I unblock myself quickly and make good progress toward the goals?

Below I compiled a list of tips that I found useful. 👇
*Imagine success*

Forget about all the technical difficulties for a moment. Imagine you finish your project successfully, would you find the outcome exciting?

If not, drop the project. Yep, just drop it. Free up your time to work on important problems.
*Work backward 🔙(1/2)*

Say your project involves three steps: A➡️B➡️C.

First, assume that you have perfect output of B and work on the step C.

Next, assume that you have perfect output A and work on the step B and so on.

In the end, you will have a fully working method.
Read 14 tweets
23 Jul
How to do research with my mentors effectively?

I get this question frequently in my open office hours. I am still learning as well but I hope sharing my ✌💰 may be helpful to some.

Key idea ➡️ **Help them help you!**

How? Check out the thread 🧵
*Frequent update*

Setting up weekly meeting with your mentors is great. But, do NOT stay silent during the week. Nothing is more frustrating to learn that the student got stuck 20 mins after the meeting last week in a meeting.

Your mentors want you to succeed! Help them do so!
*Manage meetings*

Before: send results/agenda whenever they are available. Give your mentors time to digest them.

In the meeting: progress update. Reserve the last 10 mins to discuss next steps.

After: Send a summary and an actionable plan to keep everyone on the same page.
Read 9 tweets
20 Jul
Example 1

Trajectory:
• Learning LR-HR -> challenge: large patch space -> learning mixture of models -> learning 1D profile -> high-level feature

Relationship:
• Contrastive concept: External vs. Internal (no learning)

Source: cv-foundation.org/openaccess/con…
Example 2

Trajectory:
• Applications of vision-based methods for assessment.
• Highlight the closest related work.

Relationship:
• Building upon the methodology... BUT, use deep learning.

Source: Deep Paper Gestalt arxiv.org/abs/1812.08775
Example 3

Trajectory:
• Video completion methods and their use for view synthesis.

Relationship:
• Contrastive concept: \emph{screen space} vs. \emph{3D space}.

Source: arxiv.org/abs/2011.12950
Read 5 tweets
19 Jul
Writing Related Work

I enjoy reading/writing the related work section of a paper. It helps organize prior research and put the contributions of the work in proper context.

But HOW? Check the thread below👇
*Divide and conquer*

No one likes to read 1-2 pages full of texts. Identify a couple of important “topics” relevant to your research. Add paragraph titles (\paragraph{}) so that it’s easy to navigate.
*Topic*

For each topic, write about
1) the TRAJECTORY of the research progress as a story and
2) the RELATIONSHIP of prior art and this paper.
Read 12 tweets
25 May
Sharing tips on preparing your presentation slides

Just attend many thesis presentations and qual exams at the end of the semester. I compiled some common pitfalls here and hopefully would be helpful to some.

Check out the thread 🧵below!
*Outline*

I am surprised to see so many talks starting with the OUTLINE.

No one, literally no one, will be excited by the: "I will first introduce the problem, then I discuss related work, next I present our method, I show some results, and conclude the talk".
*Be concise*

Do not treat your slides as a script.

Rule of thumbs for my students preparing a talk:
• Never write full sentences (unless quoting)
• Always write one-liners
• No more three lines of texts per slides
Read 14 tweets
11 May
Understanding ML/CV papers 📰

• Ground truth label:
Some guy says so.

• Learning from unlabeled data:
Learning from carefully curated ImageNet and pretend that we don't know the labels.

• Parameter empirically determined:
Tried many paras and this has the best number.
• Interpretable classification:
Showing some cherry-picked blurry heat maps.

• Code and data available upon acceptance:
Accept this paper first, then we will consider releasing them when we finish the follow-up paper.
• User study:
My labmates think our results look better.

• Analysis-by-synthesis:
Tuning the model until it looks good.

• To the best of our knowledge, we are the first...:
Did not see this on Twitter
Read 4 tweets

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