peterhalburt33

Wish I had time for a longer comment, but this advice rings very true for me. Reflecting back on grad school and the transition to professional life, you have to realize that your role changes every couple of years and that the things that got you to one stage won’t get you to the next. Many people end up stuck in a local maximum (lacking vision) which partly explains the Peter principle.

I was just about to say, I was slightly disappointed to find that this wasn’t an article about a novel technique to identify fast moving water (akin to the fact that we can identify the temperature water by the sound it makes when it is poured).

Not Math books, but David Griffiths Electrodynamics and Quantum Mechanics are such a pleasure to read for their style of writing.

Wait, what? The last thing we want is more Elizabeth Holmes types…

As a mathematician who works with many engineers and computer scientists, I wanted to expand on one of the points under the “Getting a Job” section. While it is certainly true that a mathematics education provides a great background for understanding other STEM fields, I would caution math Ph.D students who expect these jobs to be open to them because of their STEM connection: the onus is completely on you to bridge the gap between what you do and the field you want to work in. While it may be true that someone will hire you for your critical thinking skills (though I will personally say that I have never seen this happen), it is more likely that your deep specialization in a tangentially connected field (coupled with not being involved in the culture/conferences of the community you wish to enter) will be an impediment to entering a new field: you expect to be paid like a Ph.D., but will potentially require years of training to get up to speed.

As an example, I remember the advice of “just go into data science” being handed out like candy to students interested in industry around the time I was in grad school (10 years ago). To be sure, there was a period where a STEM background + interest could get you in the door, but that time is over. These days you are competing with many equally brilliant students who have taken multiple courses and done research in this area, and it is highly unlikely that an employer will take a chance retraining an e.g. algebraic geometer with no precious data science experience to suit their needs.

All this to say, if you have an interest in another area, you must know the players and their work in that area while simultaneously knowing your area in math. It is not easy by any means, you are essentially signing up for twice as much work learning your field and theirs, but the rewards are great - as a connector between two fields, you have precious expertise that is very employable across a broad range of industries (my first job out of grad school was essentially providing advice on research programs helping connect different STEM communities to government funding agencies, but I was able to use my connections from that job to get back into research).

It aggravates me too, however, considering that the definition of a tensor according to mathematicians is an element of the tensor product of two vector spaces (or whatever other objects you can tensor together), and according to physicists would be an object which transforms like a tensor, I’m somewhat sympathetic. Neither definition sheds any light on what a tensor is to anyone who doesn’t already understand what a tensor is, and I’m convinced that the moment one understands what tensors are they lose the ability to explain what tensors are.

I will say IMO (and experience) in professional math that while there is perhaps more of a chance for an outsider to have an impact, Mathematics is hardly free from bias towards insiders: it can manifest itself as subtly as using notation as a shibboleth (e.g. it’s somewhat easy to tell which community an author comes from through their notation and terminology, and equally easy to harbor resentment towards those outside your field) all the way to active “prove I’m the most clever in the room” syndrome during seminars. I’d like to think that a more collaborative atmosphere is prevailing now due to the rise of interdisciplinary and applied math, but people are people everywhere and as Sayre stated “Academic politics is the most vicious and bitter form of politics, because the stakes are so low.”

I would also mention David Graeber’s excellent “Bullshit Jobs”

To your point, how can one measure 1% improvement in a meaningful way for abstract activities (e.g., learning)? The premise is that 1% improvement each day will yield large gains through the power of compounding, but if you aren’t sure you are making a quantified 1% improvement over your previous improved state things go awry quickly: if you only make 1% improvement over your original state each day then by the end of the year you have only improved by a factor of 4.65x, rather than your projected 37.8x improvement. Point being, if you want to take advantage of the sensitivity and power of exponential growth, you better be sure that you have a good way of quantifying your growth.

I know this is probably meant to be more inspirational than quantifiable, but then why even insert numbers unless to mislead people about the amount of effort it takes to improve? Either way you have to put in the same amount of effort to achieve the improvement, whether you break it up into 1% chunks or not there’s no free lunch.

The first time I attempted Rudin I gave up after the second chapter. I had been through Abbot, and still didn’t truly understand Analysis (or the style of analysts). After letting it sit for a few months, I picked Rudin back up and it instantly became my favorite text. I have no clue why this change happened so suddenly, but as a convert I will point out that he is a master at including exactly the right amount of detail: he does not leave details out, but neither does he commit the equally (if not more) harmful sin of over explaining a concept. This leaves every concept explained as concisely and simply as possible, but no more, and is extremely helpful as a person with ADHD. The book is worth it’s weight in gold: one can read a chapter of Rudin in a day and have a better understanding of the concept than reading twice or three times as much material in a competing analysis text.

I’d agree, but most first year students aren’t ready to work at the level of abstraction required for a rigorous LA course (i.e., more than just matrices and Gaussian Elimination etc.)

The equations you quoted result from minimizing the square of the norm of the residual of Ax-b over all inputs x, so in a sense least squares is just calculus…

I’d push back slightly on the idea that a PhD isn’t an optimal route to industry careers.

Due to the variety of experiences and knowledge I acquired in grad school, I became conversant in a few different fields, which allowed me to cross disciplines to a very nicely compensated career path WITHOUT meeting many of the relevant criteria that new grads might be subject to.

So sure, maybe a PhD isn’t optimal if one values money and is certain that they are on a lucrative career path, however, it has opened doors for me to enter a career path where individual enrichment is a large focus since the prestige of the company is built upon the accomplishments of individual employees.

If nothing else, I got to spend five years enriching myself, studying what I love and getting paid (modestly) to do it. While there are certain aspects of academia that are off putting, grad students are mostly shielded from politics and get to spend time focused on learning their field and becoming experts with no serious expectation of a useful deliverable at the end. You might argue that the thesis is the deliverable, however, since one is working at the boundary of knowledge, there’s often very little oversight and one can justify chasing down exploratory research directions that turn out to be dead ends. My experience in industry is that it is more a zero sum game, where you have to carve out opportunities to enrich yourself and hours and productivity are tracked much more closely.

Agreed this isn’t airtight, but if you accept the premise that real numbers are specified by their sequence decimal digits: .499…+.499… has 9 as its first digit after the decimal, 9 as its second and so on - the only other number that has this decimal expansion is .999… infinitely repeating, so these must be the same numbers.