In pursuit of a PhD and figuring things along the way
A couple months ago, while measuring some samples with an oscilloscope, I witnessed a never-seen-before phenomenon.
As is completely obvious, the results were undoubtedly a part of some bigger picture.
Is this a signal from the universe? Some kind of a message?
Is that you, Batman?
So this particular week in solar cells (some weeks ago), I finally managed to make my own batch of solar cells – after which I started to consider myself qualified enough – in the capacity of an independent solar cell researcher ^_^
I would like to, first, apologize for the misleading title.
Wikipedia defines “machine learning” as:
Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
But that is not what this post is about.
You see, as a junior scientist about to embark on your career path, one thing you should do is learn to use different machines. Some machines will make something for you, whereas others will tell you that those machines are probably not working, and despite all your efforts, it will be some time before you start getting good samples or results out of any thing in your lab.
So if you work in a lab, you’ll now have a very good idea of who rules there: It’s them. Now and forward, in your time as a researcher, you’ll go into many labs, meet a lot of people, and learn a lot of new stuff… but nothing and no one will judge you like those silent machines sitting smugly atop work benches of your lab.
Many a times, I have started working with one, thinking, oh this is going to be one of the easier ones. Never has that attitude worked for me. It’s like you cannot trust any single one of them; even the ones that you think you know, won’t think twice when it is as tempting as shattering your trust in them.
They don’t care if you are enthusiastic about research and have chosen this field as your career with such joy. They will happily make you think that you have nice typical graphs* in your presentation that you are about to show in your group meeting. They want you to suffer, to taste embarrassment, like they want to see how long you are going to last in this field that you have been so passionate about …
… Until you have proven yourself worthy of research.
As a result, one of the few things I have always found myself doing in the beginning is to start gaining favor of the machines that are in my lab. To build good rapport with them. And it is not easy, not with every machine. Even after you feel you two are going along well, they will still test you, or just throw you under the bus when they feel like it**.
If it all sounds depressing to you, let me tell you there is hope. Somehow, after you have stood beside them long hours and worked with them at odd times***, they will start to slowly accept you and let you enter the ranks of researchers. It’s blood, sweat and tears to gain that kind of trust, but you can gain the status of meh-you’ll-do in the eyes of the machines.
But don’t ever expect them to start liking you, because that’s ridiculous, they probably don’t have a heart.
* Well, okay, they may have been looking a little weird to you as well, but early on, you wouldn’t know that, thinking this is probably how they are supposed to be.
** Which tends to happen most on Fridays, followed next in probability by Mondays.
*** Condition of high-levels-of-consistency needs to be met with these requirements of long-hours and odd-times.
Recently, the hotplate I had been using was glitching a lot (it was fine when I was not using it this much). And then I saw this in my social media newsfeed and this seemed to hit right on point:
Currently, I think we have both started to understand each other more, so we have been getting along better. Now, I have nothing but honest praise for the hot plate.
Dear hotplate, you are the best! 🙂
(I took the image from this link but could not find whose brilliant theory this actually is).
When I am a little frustrated, I blog about it. I have found that it clears your head and is quite an effective release mechanism.
And recently, I have found another way for times when I am more frustrated – a LOT frustrated:
Wash glassware.
Just grab a brush, pour a dollop of soap and take it ALL out on the stupid organic stuff sticking in there that just won’t go away. Bonus points for you if you can get it sparkly clean (suitable for a dish-washing soap advertisement) and a gold medal (albeit imaginary) if you can make that water “sheet” over your glassware during your distilled water rinse (nothing more spiritually satisfying than that).
Washing glassware is my research-bane: it’s the rate limiting step to my progress – where I’m most likely to procrastinate when starting a new experiment. To a normal human mind, washing some glass bottles and beakers could look like a mundane and quick step, but it can be quite tiring and time-consuming depending on how finicky you are about your glassware (and of course, we scientists-of-the-wet-labs, have specific procedures for washing our dishes.)
(Also, if you would like to read in depth about the many things that can get your beakers sparkly clean (except for cracks and scratches, where the only remedy might be to get new sparkly-clean glassware), I highly recommend this link).
So if I can have really clean bottles in my cabinet and methodically washed glass slides in my drawer, I have one less excuse to laze around and can start working on my next steps. It’s like killing two birds with one stone and so this is where I can channel the frustration of my failed-lab-experiments.
Now, from my recent frustrations, I have accumulated a fair number of glass bottles and washed slides and I am good for a couple of upcoming series that I should be running.
After I run out, I can think about learning the art of strategic frustration so that I can always have some clean glassware at hand in future, or when needed (not sure how that would work, but if I do, that can be for another blog post).
For the last four months, my PhD has accumulated into wonderful experiences of “okay so that doesn’t work”.
Which is good, because every time one thing doesn’t work, there’s one thing less to discover that doesn’t work, getting you one step closer to the thing that does work.
So you have this recipe/procedure/protocol that you need to optimize, or get the best out of. You start off with rather good energies, thinking the most it will take you is, what, three weeks? It seems pretty simple. You’ll easily find the best way to do it in that much of time.
As you try on and on, you realize there are so many factors and parameters that are affecting the whole process. And to get to the best possible option, you will need to twiddle with all of those (ALL of those, one at a time. And then of course you have to make sure that once you are checking one factor, something else doesn’t meddle in and give you some kind of false positive or negative).
But…
… Do you even have all the options jotted down? There might be more. There must be something you are missing.
… What if option G was the best recipe? It did give some results but you probably went ahead trying H, I, J, K, L because you may discover something better (there might be no “better”, but you’ll need to find that out for yourself, now, won’t you?).
… Would “better” pass off as “best”? What even is meant by “best-possible recipe/procedure/protocol”? Is there a “better” than what you are considering “best”?
After everything, you end up with lots of data, most of which is just proof of how, mostly, it doesn’t work (if you are lucky, it may be otherwise). Obviously, there’s always a chance that nothing is going to work. And it might be some time before you realize that you need to change the whole game plan.
I mean, a PhD is like life in real time. So you want to learn about life, go get a PhD: It will teach you things in a couple years that you might take decades to learn otherwise.
