If you die in science, you die for real

In 2011, I made a choice that would completely change my life: I decided to become a scientist. It was a very weird period, I was doubtful and delusional. The thing that I didn’t realize, however, is that the feeling of doubt and delusion would never go away, the better I tried.

The change itself was actually reinvigorating. Since when I first had a talk with the like-minded, I felt an urge, an euphoria, that I still feel when I go up a mountain to work at an observatory. I absolutely love to observe the sky, and the sensation seems to become ever stronger the more I do. The twilight is the beginning of a new night, of new opportunities and a travel that might end at new discoveries and excitement. For the first time in my entire life, I really feel like I belong somewhere.

And this is why… I am afraid. Almost every day, while sailing through the internet or visiting Twitter, I end up reading a post about how academia is broken; that there are too many students or postdocs trying to get an academic job and there are not many being offered; that working in academia is frustrating and it pays badly. I am afraid I might end up leaving science, and coming back to the same frustrations I had when I was looking for a job in corporations.

Since I made the decision to become a scientist, I knew that it wouldn’t be easy. To be completely honest, I was in the “follow your passion” mindset. I had confidence that everything would be fine if I did my best. Some say that we are afraid of what we don’t know, so it could be that I’m afraid now because I don’t know the exact level of difficulty of being a scientist or just because my future is uncertain.

Money is not a bigdeal for me: I was born in a simple family, and I can live comfortably with just a couple of bucks to buy me food and pay for the internet. But I know that many people want to construct their families, and have a nice house to raise their kids, pay for good education for them. So it’s understandable why a career in academia is problematic on that point. A scientist will only be able to have these good things when they are on a professorship track, and it can take a couple of decades to achieve that.

One can argue that I can be an astronomer or a scientist, even without being in academia: I could, for example, be working on data science, since it is big thing right now with corporations; or I could be a writer, working in science outreach. So there is that: looking for something outside academia. Of course, the chances and opportunities would depend mostly on luck, but also on what you have “worked” on during your graduate courses (the quotation marks are there because some companies don’t consider research as “working”). And this is where all my frustrations with corporations come from: not seeing the value in science.

When I read these inspirational and informative posts about looking for jobs outside academia, it’s a bit unsettling to read about isolated cases. I mean, maybe John was lucky enough to find a position as data scientist in an awesome company, and maybe Mary hit the ballpark when she founded her own business; but what about all the other people who left academia and are stuck at uninteresting jobs, just as almost all of my friends who went straight from undergrad to corporations? What do they have to say? Do they exist? We don’t have numbers on it, or at least I never saw them. As an astronomer obsessed with statistics, I find it hard to believe that getting a satisfying job outside academia is an easier task. We should be honest about the issue.

Outside of academia, I know very few people who actually enjoy their jobs as much as I do with astronomy, and all of them have a larger income than I do. They have cars, live in nice places, post selfies on Facebook when they’re traveling, but they hate to wake up in the morning and having to go to work. For them, the weekend is a blessing, and the weekdays are a curse. This is exactly what I want to avoid. Finding a satisfying job is hard, anywhere; there is no magic pill that will solve this quest.

I was talking to a friend this week, who is a professor, and he said things were even worse, in Brazil, a few years ago (around the 90’s and 2000’s). When he was in my position, a graduate student, in the same institution, he had absolutely no prospects of finding a job. Research in our country was sparse and fellowships were rare. It is just now that we are getting on our feet with science. Additionally, most of Brazilian research is done exclusively in academia, far away from companies. So, as you can see, at the current generation of scientists, there are two prospects: 1) In public universities, there are many positions being created as the result of investments and outright retirement of the old professors; for instance, at IAG/USP, most professors are either very old or very young, because of the recession gap from the 90’s to the 2000’s. 2) As the local culture of scientific jobs changes, there will [hopefully] be a broader integration between research and companies, which will open up opportunities outside academia.

Sometimes I think that being a scientist is like being an artist: it’s a very elusive position, one that few can get into; one that not everyone recognizes its importance; one that is full of ups and downs; and most importantly: one that takes a lot from you, and it will probably not financially pay-off your efforts. But, damn, it’s awfully satisfying.

Maybe I should stop focusing too much on the objectives, it’s not like a “if you die in science, you die for real” kind of situation. Perhaps I should just enjoy the ride, whatever the destination. To be honest, it’s been like that since the beginning: for instance, I never chose my exact field of study (apart from focusing more on stellar astrophysics, which I find very enticing), and that’s the reason why I’ve wondered through stellar evolution, formation of stars, interstellar medium and now solar twins and spectroscopy. Also, if you asked me 5 years ago, I would never have said that I wanted to be an exchange student in Netherlands. Things just happen, and our inclinations change. Maybe the randomness of life is what makes it worth living.

Featured image: “Science by Jurne, Huer by Enron” by Steve Rotman


If you die in science, you die for real

Pysics notebook one

Since I started studying Python programming, I’ve been creating some sample codes, here and there, that solve physics problems. Most of them are exercises from books, the internet and occasionally from school assignments. I have a lot of fun creating them, even though they can be quite difficult to compute sometimes. Because of that, I started a very laid back project called Pysics, which is basically publicizing a compilation of these exercises, so that they can be used by other students.

Initially I was creating a program on GitHub, but since I got to know this amazing tool called IPython Notebook, I realized that this should be the best manifestation of Pysics, without question.

So, here it is, the Pysics notebook one. You can also download the ipynb file here. It basically contains four exercises: plotting the electric field produced by point-size electric charges, calculating the time-evolution of a coupled triple pendulum, computing Bessel functions and simulating the diffraction pattern of a telescope.

I plan on working on one or two more notebooks, which I will release someday (not sure when, for my graduate school starts in just a few weeks and I will be busy with the preparations). But, for now, I hope this notebook can be useful and inspiring to you.

Featured image: the diffraction pattern produced by a point-like source of light as seen on a telescope.


Pysics notebook one

CREPE: a global optimization tool

It’s summer. And in contrary to many people here in Brazil, I am spending time at beaches or beautiful places, for various reasons. On the other hand, I started an interesting coding project: it’s called CREPE, which stands for CRoss-Entropy Parameter Estimation. It is a code written in python designed specifically to be global optimization tool, which is something very handy for scientists. The program is freely available on GitHub. Please be aware that it is far from being a release version, there are still many things that I want to implement.

But what the heck is cross-entropy, global optimization or parameter estimation? – one might ask. Well, in science, many times we create hypotheses when trying to understand a system, a signal or just the mechanisms of how things work. And the most objective way of testing a hypothesis is formulating it mathematically: let’s call it a test function. The test function might be dependent of various variables. For instance, when studying how the temperature of a solid plate behaves when one of its tips is heated, we can observe that the temperature depends on the position of the analyzed point, the energy output of the heat source, the material, how the heat is exchanged, time, and so on. These are all variables, and in the most complex phenomena, we do not know exactly what are the values that these variables assume.

On that same example, we might not know some of these variables, let’s say: the energy output of the source. If we know the temperature of each point on the plate, the other variables and how the heat is changed, we can estimate the energy output of the source (or how it varies with time). There are many tools for that and it can be calculated analytically. However, in many problems we do not know every variable (let’s call them the parameters), the observations might be imbued in noise and uncertainties, our hypothesis is sometimes completely empirical (which means there isn’t a beautifully closed set of equations with well defined variables), and the calculation can be outright too difficult or time-consuming to solve analytically. And that’s when we go to computers and global optimization tools. Some scientists (especially purist physicists) say this is “playing dirty” or “an appeal to ignorance.”

Global optimization is a clever way applied mathematicians created to, well, optimize a function or set of functions in order for it to assume a form that reproduces what we observe (the data). Back to the previous example, a global optimization would take all the information that we know, including our test function, a reasonable guess and optimize the value of the energy output of the heat source, in other words, look for the value that best reproduces our measurements of temperature. Global optimization is so powerful, that it can, in theory, estimate as many variables as you wish. But how well it will estimate and how much time it will take strongly depends on the computation power, the initial guess of the user, how good is the hypothesis, and how good is the data. You know, when you have an equation like y = x + 2, and if y = 4, solve for x? When you solve for x, it’s basically doing an optimization, which is finding the best value of x that best reproduces y.

Now, for the concept of cross-entropy, this is actually very complicated, and to be honest I didn’t quite understand, but you’re welcome to see these Wikipedia pages. The creator of the cross-entropy (CE) method is Reuven Rubinstein, and there is an official webpage for it which contains a very handy tutorial about the method. But notice that this kind of tool isn’t something that we learn (neither are even mentioned) about in classrooms, which is somewhat sad, because global optimization is such a powerful tool, and not that difficult to program it yourself. But with great power come responsibilities.

CE converges to a solution extremely quickly. It’s crazy fast. The problem is that it converges so fast, that it might get stuck into a non-optimal solution (what we call a local minimum). It doesn’t mean that it is a hit and miss program, but the results must be checked very carefully. Also, CE does not guarantee that your test function or the hypotheses are good, neither does it for the data. So, the job of the user of CE is to benchmark the test function(s) and take into account the quality of the data during the analysis. A good way to avoid mishaps is to plot everything, it really help us to make a good initial guess and check the validity of the results.

CREPE is very simple at the moment. So far, it works with optimization of parameters with single-variate Gaussian uncertainties, and there’s still some tweaks I plan to do and many things to add. I’ve been basing the program on this paper.


Inside the codes that can be downloaded on GitHub, you’ll find a folder called examples. The first example I want to show is curve_fit.py. In astronomy and other fields of experimental physics, sometimes we measure extremely faint signals that are imbued in noise. curve_fit.py generates mock data, and you have control of what kind of signal (the function f(x)) it is and the noise sigma (which translates into the amount of noise). In that example I used a sinusoidal mock signal, which describes various phenomena, such as the movement of a spring after it is pressed or pulled, and the parameters that we have to find are a (the spring constant) and b (the phase – or initial position). Here is a plot of a mock data, with noise sigma = 1.0:

Plot of a sinusoidal (y = sin(ax + b)) mock signal with noise generated by CREPE.


Now this is a very noisy data. In fact, by looking at the plot, we can see that the noise levels are approximately half of the amplitude of the signal! This is terrible data, but if that’s the only data available, the only thing we can do is trying to get the best out of it. Suppose we were given this data and we don’t know what are the true values of the parameters a and b. CREPE can estimate them for us, if we feed it with at least two very important items: a guess and a performance function. As the name implies, the guess is a set of intervals that we think a and b should be inside (or close by). The performance function has one job: to evaluate how well the guesses of a and b are able to reproduce the data. There are many ways to evaluate that, in fact, I plan on adding some “standard” performance functions to the program so that the user doesn’t need to bother with it. What CREPE does is to create many samples of a and b (based on the user’s initial guesses), and just trying them on the performance function. Depending how well a certain set of samples do, the program selects the best ones and generate new samples based on them. And then it iterates until the results can’t get any better. Basically, the program “learns” how to reproduce the data, and because of that, methods like these are called machine learning.

After a handful of iterations, CREPE spits out the values of a and b that it “thinks” are the best ones, along with their uncertainties (although I’m still working on how to define these uncertainties). Based on the previous mock data, these are the results I got: a = 2.027 ± 0.007, b = 3.711 ± 0.104.

CREPE’s results in reproducing the data. The red curve corresponds to the signal without noise (true signal).


But how close are they to the true values of a and b? Well, a (the spring constant) get pretty close: a_true = 2.0. But b (the initial phase) doesn’t: b_true = 10.0. Why is that? Well, I think that the quality of the data was very influential. So, let’s try to find a and b using a signal with less noise, shall we? This time, noise sigma = 0.5:

Mock signal with less noise (sigma = 0.5).


Looks a bit better than the previous signal! And now, using CREPE’s parameter estimation capabilities, we get a = 1.994 ± 0.002 and b = 10.061 ± 0.012, much closer to the true values of a and b.

Fitted signal to the new data.

But scientists are not always just fitting curves and functions to data. Sometimes, there are more complex calculations involving empirical models of how we think the universe plays. The program model_fit.py is a very simple example of how to work with a model in CREPE. One of the biggest accomplishments in astronomy was decomposing the light coming from objects in the sky (a field of study known as spectroscopy), and discovering that they have “lines” in them. These lines are features generated by the emission or absorption of light by chemical elements in very specific wavelengths. In this example, we simulate a spectrum, with 3 emission lines. In reality, the spread and intensity of these lines depend on a number of variables, but in this example, we consider that only the abundance of the elements and the rotation of the object play a role in the observed spectrum.

If we were given the data (again, very noisy) and, from the literature, we have the empirical model of that region of the spectrum for that specific celestial object, we can use CREPE to estimate the elemental abundances and the rotation of the object. The next figure illustrates the results from the model fitting example:

Final result of the spectrum modelling example.

And again, not a perfect fit, but it comes close. Right now, I’m working on improving CREPE in order for it to get better results even if you have noisy data. One of the biggest advantages of CE is that it can attain good results much faster than other methods (there are a number of them in python – pick your poison!), so I think there is some potential for this program. Also, I think it’s important for scientists to make public the tools that they create, so they can be checked and used by other scientists, and python is perfect for that. Many astronomers are stuck with tools created on IDL, a very expensive programming environment (although extremely well-developed), which makes collaboration and sharing much more difficult. Luckily, python has been growing in the scientific community (especially in astronomy), so we are heading to a bountiful future on that front.

Well, this post was longer than I expected, and I barely touched the potential that I envision for this project. If you have any ideas or want to make contributions to it, just drop me a message or a pull request on GitHub!



CREPE: a global optimization tool

Are we being too romantic with physics?

Featured image: Could you point me out to the next step of this problem?

This week was a very hectic one, one of the reasons being because I did two exams on Electromagnetism I, probably the most dreaded course in every physics graduation program. If you’re not familiar with it, electromagnetism is the advanced study of the interactions between charges, electric fields and magnetic fields, as well as their applications. It involves a lot of vector calculus and special math techniques (the ones learned on courses of mathematical physics or mathematical methods for physicists), and it also require a lot of, let’s say, cunning in order to formulate the problems into mathematical equations and solve them. And this is where things get problematic for me, and for many other students.

A few days ago, I stumbled upon this article on Medium, which speaks of three points tied together: Walter Lewin and his involvement in sexual harassment on MIT, the way physics is [wrongly?] taught in universities and how it affects the learning process of minorities, especially females. I understand how the gender issue has been such an important problem discussed among scientists, but what really struck me while reading was the author’s vision on how we can improve the students’ learning in a physics program by doing one thing: teaching them problem-solving skills. According to the author, that would also help close the gap of gender and minorities on physics, whacking two moles with just one swing. And this article started me thinking: why aren’t we generally taught the techniques of solving physics problems? Actually, there’s a fun expression in Portuguese for that: it’s the jump of the cat. Why aren’t we taught “the jump of the cat” in physics problems?

Preeya Phadnis makes a very important point on that: physics teachers argue that they shouldn’t teach the “jump of the cat” before the students try and battle with the exercises for themselves, because that would take away their chance of having an eureka moment, the climax of solving a very difficult physics problem. Another argument for not teaching the skill is that letting the students figure things out “builds character”, strengthen their wit. Now, analyzing my behavior since I started studying physics, I’d say that I have been guilty of counseling my colleagues (and even myself) into doing things by themselves and just battle until you win the fight against the problems. I am a strong advocate of discovering things by ourselves, because that’s how I’ve been hacking my way into astronomy research: most of the things I know were learned outside of classrooms.

But here is the problem, and I agree with the author: people come from different conditions, have different backgrounds and do not have the same skills. And the way teachers minister their classes act as gatekeeping, especially when they say things like:

  • “Physics is supposed to be difficult: deal with it”
  • “If you want something easy, go study [presumptuously insert a another program here]”
  • “You’re not working hard enough”
  • “Some people do not have what it takes to be a physicist”
  • “Some people will never learn [insert course name here]”

I don’t think teachers should be gatekeepers. That’s not their job. They’re supposed to do exactly the opposite: to open doors. You see, it’s not asking the teacher to be more lenient on the evaluation or just ask easy questions, but it would be helpful if they could actually encourage the students instead of putting them off of physics. No, I don’t think physics is for few people. If they don’t have the necessary skills to solve a physics problem, why not teach them? Let’s go back to electromagnetism: if you’re not familiar with it, you should know that solving a problem of EM can take a long time – depending on how it’s set, it can take several hours or even days for the student to develop a solution, and the smallest mistake will screw everything up – and things can get worse if you don’t have the right skills. Achieving an eureka is a good sensation, but also a too romantic of a vision on physics. And when your work is not even recognized, why bother? What was it worth for? Yeah, you solved a problem that was already solved a hundred years ago, big deal. Looking at the bright side, at least you developed the problem-solving skill a little bit – but at what cost? The biggest problem is that some people will not even learn, since the towel was already thrown midway through the problem because they thought they did not have what it takes to solve it.

I once heard that I am a lazy physicist, that I don’t want to solve the most intricate and difficult problems because they seem to take too much work. From a teacher. While that might partially be true, it’s not about laziness, it’s because I have different skills (such as programming, using numerical methods, working with schematic plots in order to advance in the problem), but I wasn’t allowed to use them to solve those problems. Instead, I was forced to develop a skill – solving difficult problems on paper – by myself, because that’s how, supposedly, physics should be [or was] done [before me].

This week is also the last of the semester at the university, and some students were doing their final exams while others were already on vacation. However, when a few of us gathered in front of the university library, I was very sad to know that one of my friends from the physics courses is leaving the program. She is a girl and a former research colleague, and her reason for doing so is because she doesn’t think she has the right skills to be a successful [astro-]physicist, and she also felt very demotivated to continue taking the courses. I wonder how much of her despondency was caused by discouragement from her peers, teachers and the general culture of physics school. I also learned that many of my colleagues from the electromagnetism class gave up on it midway through the course because they couldn’t keep up with it. Additionally, there are a couple of other students that have been hanging up on even more basic courses (such as vector calculus and basic physics) – for years –  and they are just now managing to arrive at the more advanced courses. It’s quite depressing.

Let’s get one thing out of the way, though, and this is a point correctly made by a professor that I know: here in Brazil, and very frequently at my home university, most people who enter the physics program are there because that’s the only option, their “plan B”. These people think that, by entering on that program, they can easily do a transfer to an engineering program [1] after the first year. What they don’t realize is that it is not allowed to transfer from physics to engineering, only the other way around. When these fresh students realize that, there is a massive leave from the program, which usually occurs after the first and second semesters. I remember that, when I entered the physics program (as a diploma carrier), there were something like 50 or 60 freshmen. Right now, at the 6th semester, a very optimistic estimation would be around 10 students. What I mean by all this, is that there will always be a very high leave rate for the first year of a physics program here, but I would assume that everyone that stays there after that is either interested on physics or have absolutely nothing better to do besides carrying on the courses. If there is a reason for these people to stick to the physics program after the trial of the first year, there must be also a reason for they to decide to leave after 5 or 6 semesters. Could it be just a sudden realization that “physics is not for me” [2] or were they ever so slowly forced to think that throughout the years?

To be honest, I was also very discouraged and demotivated from the EM course. But I still kept on, because… I don’t really know, maybe I had external motivations to do so. It’s such an interesting part of physics, and yet, they managed to turn it into a torture. The classes were 2-hour long (without break) whiteboard grinding sessions, with lots of intricate derivations of crazy equations, and very few connections with practical physics. Maybe there are physicists who enjoy this approach, but I’m not one of them – I prefer to take a more pragmatic approach. I like to say that I street fight physics, going down and dirty with it. It’s not beautiful, in fact it is sometimes inelegant or ugly, but it’s the way I do. I wish that we could have a more diverse physics teaching, a celebration of the different skills among scientists, and that the ones we don’t have could be taught instead of used to filter us at university.

While this ideal environment is not achieved at the universities (and I don’t think it will be done in a timely manner), I highly recommend other striving physics students to do the following: just hack the system – find ways and shortcuts to do the problem-solving, learn them, make annotations and study them. The problem is that, with this approach, you’ll miss out on the actual physics (the things that happen outside the mathematical equations). Or maybe it will help? If you learn the problem-solving skills fast enough, perhaps you’ll have time to study the implications of the equations. But again, this might be highly dependent on one’s background and conditions. Also, we could do without the boring 2-hour long pointless classes, right?


[1] Why do people leave physics to do engineering? For 2 reasons. First: engineering pays better. Second (which is probably tied to the first): engineering is their first option, and their grade on the selection exam wasn’t good enough to make it into the highly competitive vacancies.

[2] One might wonder if that is what happened to me when I decided to leave engineering and go study astrophysics. Well, it’s not because I thought engineering wasn’t for me, it’s more complicated than that. I do think I have the skills to be an engineer, but the thing is that I actually did not find it joyful. It was boring (at the context I was in). There are other additional reasons that contributed to my decision, but I will leave them for another post.

Are we being too romantic with physics?

Much flashback, very defeat

This is somewhat of a continuation of the posts I wrote some weeks ago about the teaching methods of a certain professor here at my home university. As a follow up on that, I can say that things have been acceptable, at least with the teacher and his classes. However, this week I noticed a trait that seem to prevail among the students and sometimes even with the staff around here: defeatism. I have to be honest, though, that I consider myself a defeatist in a few points of my life, for instance, when I defected from a career as an engineer and decided to pursue another one as an astrophysicist, the reason being because I felt deluded by the job prospects around here and excluded by companies for being an introvert. Also, you might not know this because I did not write about it here in the blog, but I canceled my enrollment on the Quantum Physics course this term (more on that here), because I quickly got fed up with the classes (more specifically, they were annoying the hell out of me and actually dampening the progress on my research). So there you go, I am not free of the defeatism that permeate the halls of this place.

I realized how bad things were when we got to solve a theoretical problem as a group. On the morning we decided to unite and try to bring that beast to its knees, I was surprised to see that one of my colleagues’ first action was to say “okay, I’ma look if this problem is already solved on one of these books”, while opening dozens of pdf files of books on electromagnetism, both in Portuguese and English. My other colleague tried to understand at least the ideas of the problem and get some kind of hunch on it. But he quickly gave up and started looking for a complete solution on the internet. They were defeated even before starting to write a freaking line of equations. And this is coming from a guy who prefers to write codes than equations!

Now, don’t get me wrong on this. I also look for solutions on the internet and books, for instance when I’m stuck on something I can’t solve or when I have a much farther objective and I’m just trying to skip a thorn on my way. However, on the situation I mentioned, I would assume that the main objective is, specifically, to learn how to solve the problem. They were simply skipping to the end (the problem already completely solved) without putting their necks out to the hardships of mathematical and physical thinking. What kind of physicist do that? Now I understand why some of these students don’t advance on their undergraduate research (a complaint constantly heard around here): it’s because they are trained to look for ready answers instead of building their own. I have to admit though that at some points, when they were looking on some lecture notes, they provided us with some very important insights, so kudos on that, and I wonder how much time it would take us to arrive at these insights by ourselves (probably a few years).

Sometimes I do grimly complain that the teachers usually solve the easiest problems on classes and leave the most treacherous ones for us students to solve, but this is a hollow rant. This is actually a good thing, if the objective is to practice the work as a physicist: the easy problems are already solved and we are way over them, there are new and unsolved things to do, and that’s our job. But I shudder to think how on Earth are these previously mentioned students (soon or late to be physicists) to solve unsolved problems if there is no ready answer, if they quickly throw the towel without at least lifting a finger. Oh well, maybe they have different objectives than mine. I realize that many students there actually fell without parachutes on Physics, and are striving to obtain a diploma without a clue of what they will do with their lives after college. Maybe they are not striving at all, and consider being a student their day jobs.

I know these things, because I’ve been there. Once I was a day-job student who simply carried things on with my belly without a clear prospect of an after-college life. That’s why I was completely helpless when I got my engineering diploma, and ended up throwing my towel a few months later. There was no joy, no wonder, a lot of disappointments and my shattered self image. I felt completely defeated. When I turned to science, I was well-received and found back the wonders once had when I was just a little lad. Now, I fear for these students might be trailing the same path I once threaded a few years ago.

Featured image: a cartoon I found hung on the wall of an art studio at Tromsø, Norway. I don’t know the creator. If you do, please let me know so I can credit the image.

Much flashback, very defeat

Ugh… FINE, I’ll play along

In the last post (which was written almost two weeks ago – geez, I need to get back to writing more here), I ranted quite vigorously about one of the teachers at my university and how crappy his classes and assignments were. Well, so this is kind of an update on that, and it has a good and a bad note. However, I don’t want to rant as much now, because I am in a good mood, and I admit that, indeed, I overreacted. Maybe.

You see, education in Brazil is old-fashioned. Even before going abroad and studying in Netherlands, I had already got pretty mad at how things were carried out here, so it shouldn’t be a surprise to me to find everything the same way it was before (which I wrote about in another previous post). The thing is: I stand up for what I believe, so much that I wrote a direct and signed letter of suggestions to the beforementioned teacher. The other thing is: it seems to have had an effect. Classes have been a bit more interesting, and the dull assignments are less plentiful, giving some space to more thought provoking ones. I’m also trying to do some changes on my part, for instance, by being more active on that online platform the teacher decided to use. Things are not perfect, as expected, but you know: baby steps.

It led me to realize that the teaching/learning process is not only about the teacher or the student, it’s about both of them. While I was ranting about the classes being crappy, I did not mention that I am a terrible student. If none of them try to be a little bit better themselves, there is no motivation for the other party to try to get better too. Well, so yeah, that was the good news. Now, the bad news is that I didn’t realize that I would bump into an even worse class.

So here is a little bit of backstory: I took a course on Quantum Physics 1 when I studied in Netherlands, and I thought I could ask for credits on the Quantum Physics (which is some kind of introductory and redundant course on modern physics – I seriously don’t know what the balls it is doing in our curriculum) class at my home university. However, the institute’s board decided to give me the credits on Quantum Mechanics 1, said to be the best equivalent to the course I took abroad. The weird thing is that this QM1 course has the QP course as a prerequisite, and the board did not give me the credits on it (because I lacked enough credits for both of them), even though they did for the more advanced course. Later I discovered that it was only one of the members that voted against me having both, but whatever – no grudges, his reasons were fair. But, man, couldn’t they cut me some slack?

So, bottomline, I had to enroll in the Quantum Physics course. Which is dutifully taught by the oldest professor at the institute. And, you guessed right, his teaching methods are just as equally old, so much that his are the only classes to be compulsory. Yep, they are crappy and I cannot simply stay and study at home, because I will fail the course even if I get a 100% score on the tests. But enough ranting, this post is not about it, because I have already ranted to the former coordinator of the Physics department (he is actually quite cool with being honest and open with the students). What he, and some other students that I talked with, said is that I should stop complaining and simply accept things as they are, because there is absolutely nothing that I can do about it. No chance, nada. I have to take the QP course, and go to the classes.

And you know what? Fine. Just: fine. What the fucking ever. If there is nothing else to be done, so be it. If this is what it takes for me to be an astrophysicist, then I will play along. It’s not that old coot that will stop me from pursuing my career, and I kinda feel there will be a lot more people like him in the future that I will have to deal with, so better practice now, right? *sigh*

Ugh… FINE, I’ll play along