Issue #19: The nonlinearity of a Ph.D

Reflecting on a recent milestone

May 31, 2024

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In this issue…

I’ll talk about a milestone I reached this week. Rather than tell you what it was, here’s a screenshot:

I finally published my first first-author paper!

This paper is the product of 3 years of wandering in the dark and 1 year of genuine research. In many ways, it’s a microcosm of my growth as a Ph.D student and what I believe to be most Ph.Ds.

Let me explain why.

Walking in the dark

I started the project that would become this paper as soon as I started my Ph.D. I had joined a special program in my university to let me get a head start on my research before the academic year started.

My professor thought of the idea and gave it to me to execute. As a first-year student, this is not unusual. I may have had a Master’s, but I was totally new to doing research. A way to ease into it is to be given the idea.

But in the three years that followed, I struggled to write and motivate the paper. I was just given the idea for the project; it was up to me to fill in the gaps to convince others that it was a good one. Back then, I couldn’t even convince myself it was research worthy.

And it definitely came through in my writing. It was rejected from two journals prior. My boss tried to console me, but I didn’t need to be consoled; from my perspective, the rejection was inevitable.

To be frank, I was ashamed of this project. It was a direct reflection of my confidence and ability as a researcher. It was bad because I was a bad researcher.

At least that’s how I felt.

In retrospect, I have come to understand that that this part of my Ph.D is part of a long, difficult process. I was writing terrible manuscripts because I had no idea how to do statistical research. I was merely mirroring structure and material from papers I thought were relevant.

Genuine research

In those 3 years, I read a lot of papers. I saw how papers were structured. I saw what needed to be done to demonstrate an idea. I saw a lot of stuff I didn’t understand at first, but started to get an inkling after my 50th look at the paper.

I needed these 3 years to learn the basic mathematical ideas and the norms of my field. I thought that the first year of my Ph.D was me getting to the starting line, but I was so wrong. The first years of courses and transitioning to research bring you to the starting line.

I can’t put a defined date on this, but there came a moment where I was able to look at my crappy draft and see it in a new light. I knew my paper had to have a certain structure, and I needed to fill in those gaps. Back then, I didn’t know what kinds of analyses I needed to do, but after reading so many papers, the pattern became clear. I didn’t need to ask my supervisor what to do, I had my own intuitive feeling for what needed to be done.

It took me a year to gather all of the results for my paper, but I was gathering these results with purpose. Almost like magic, I was able to do what I felt was bonafide independent research.

But it’s not magic. For those who haven’t done a Ph.D but want to, here’s my advice to you. The idea of doing independent research will seem impossible to you at first. It will feel impossible at first because there are norms you don’t know about. You don’t know what’s been done, what’s working, and what’s left to be done. In Cal Newport’s words, you’re not yet at the “bleeding edge” of your field.

The solution to this is reading, reading, and more reading. It’s easy to say this now that I’m on the other side, but you have to trust in the process of literature review. That may seem like a lot for an entire field, but as you read more, it’s more likely that you’ll encounter more and more specific problems. For very specific problems, there’s only a finite number of papers that might exist for it.

Be like bamboo

Growing as a Ph.D student is like bamboo. Bamboo doesn’t immediately sprout out of the ground. It takes years for it to develop its root system before it utterly explodes in growth.

The early part of a Ph.D feels slow and cumbersome because you need to catch up to the bleeding edge of your specific research problem. You’ll feel like all you encounter is confusion and more problems, it really really sucks to live through that.

But once you have the fundamentals, it becomes much easier to spot the holes in your field and figure out solutions. To quote Cal Newport again, real research is rarely a mind blowing innovation that no one could have imagined; it’s seeing what the current ideas are and trying to take a single step in a direction that others haven’t thought of before. One step past the bleeding edge.

This was a long one, thanks for sharing in this experience with me.

See you next week.

Christian

A Snapshot of Very Normal

😵‍💫 What am I working on right now?

Working on part 2 of the nonparametric mini-series

🧐 What am I enjoying right now?

Book — I decided to dive into the chapter-by-chapter summary of Antifragile: Things That Gain from Disorder by Nassim Nicholas Taleb. Nassim’s famous barbell strategy has remarkable parallels to my YouTube strategy and gives me confidence I’m on the right path.
Thing — I got a camera! You’ll be seeing my ugly mug a bit more, not just on YouTube but in other pieces of content I have in the works.

📺 What are my recent videos?

How we use randomness to improve our lives: a video on how random treatment assignment let us make the leap from correlation to causation.

📦 Other stuff

I wrote guided solutions to problems to Andrew Gelman’s Bayesian Data Analysis. It’s for advanced self-learners teaching themselves Bayesian statistics

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