How have lack of travel and increased social isolation affected mathematics?

In many experimental sciences, a lot of research projects now require an effort that a few authors cannot handle. There are some discussions if this means that instead of being creative, people are merely scaling up, which is probably true in some cases, but often strong empirical results that enable "real-world" progress require such efforts. But that's not Math. I work (partly) in a field of theoretical CS which is Math-heavy and attracts mathematicians and there I also feel the indirect effects of Covid-19. As a junior in the field, I need to rely on the feedback of more senior people, and these people move fastest on the whiteboard/with pen and paper, in physical meetings. The good thing about these collaborations is that we juniors can provide fresh ideas and the seniors can shoot down the bad ones quickly (before a long, formal review process) and make sure the good ones are executed with the necessary rigor.

(Note: I find the comment of "your" reviewer strange as well.)

Many current problems are simply too complex to be solved by a single person, often because it requires expertise from very different fields. High energy physics, biology and neuroscience come to mind. There is not a single person who understands all aspects of CERN. Likewise there isn’t a single person who understands how to 1. Build a large scale Electron microscope to acquire high res images of an entire brain 2. Analyze hundreds of terabytes of data to make these images usable and 3. Analyze the results which would require highly specific domain knowledge of neural circuits or other biological features of the system you are looking at. My experience is opposite to yours. You are lost as a lone researcher in many fields and the best papers these days are written by enormous collaborations.

Others have mentioned the size of projects, which might be true.

Another factor is the incentive structure. There's immense pressure to obtain grants (due to indirect fund income) and focus on h factors increases this as well. The best case strategy given both of those incentives is to have large groups of people who each submit as many grants and papers as possible, and put each other on each submission. That way, even with a high grant failure rate each will bring some money in, and self-citations to a paper will be multiplied by the number of authors.

Background in pure math here. Generally researchers learn about what problems are fashionable as they talk to others in the field. As a PhD student, your advisor should give you ideas for problems to work on. Solutions to old problems tend to open up new lines of inquiry. It's a potentially infinite process, with the only limit being the capacity of the human mind.

The odd perfect number problem has been unsolved for thousands of years:

https://en.wikipedia.org/wiki/Perfect_number

Why would anyone want to know the answer to this problem? The truth is that the motivation for most pure math research is purely aesthetic. You could also ask arts or english departments "How can you apply your paintings or novels?!?". Some mathematicians (mostly geometers and topologists) are inspired by problems in physics, but most aren't. In truth you never know what structure or theorem might have some future application; number theory was totally "useless" until modern cryptography made (some of) it useful.

For just a tiny taste of one small area of modern math, I dare you to click on any of the links here:

https://en.wikipedia.org/wiki/Floer_homology

We are flush with structures to investigate. The idea that "most discoveries have been made" is nonsensical in a domain where the discoveries to be made are literally infinite. To give a hint as to the infinite nature of mathematical inquiry, you probably are familiar with the idea of a function mapping a number to another number. A good deal of modern math is involved with much higher order functions; we can have functions that map functions to numbers, functions to functions, functions to spaces, and so on and so on. And then we can consider functions between those functions (and so on). Category theory is an attempt to give a framework to some of these "meta" relations. It should be obvious there is no limit to these structures and no limit to the number of problems one could pose about them.

I only really know about the company-sci touching parts, but —

Mathematics didn’t have a strong notion of proof until the late 1800s/early 1900s. Contradictory “proofs” in calculus created a need for a more rigorous system of reasoning. (It took until the 1950s to formalize what an ‘infinitesimal’ is — but those were used in early calculus prior to limits.)

That gave us electronic computers, as the effort to reduce proofs to things that could be mechanized bore fruit. Every program is a proof!

Since then, the idea of proof and structuring mathematics has been radically redefined — introducing things like category theory and type theory as alternatives to set theory, while studying the impact of certain axioms and developing tools around “reverse mathematics”, which is fitting a set of axioms to the theorems you want to be true.

Modern mathematics is working on the study of logic topology, extending our reasoning tools to deal with complexities around proving equality in the hopes we can automatically verify mathematics. These tools overlap heavily with AI research.

The applications here are what you might guess: new AI methods, data analysis methods, verification of software, etc. The DOD has paid a fair bit to support that research into software verification, for instance. The NSF and others fund “big data” analysis.

I’m less familiar with other parts of mathematics, but —

The study of knot theory has impacts for physical sciences, from particle physics (anyons) to fluid dynamics. In particular, there’s some work to be done in higher order knot theory and computational knot theory (ie, how to be efficient). MSFT is building a quantum computer based on this — and has suggested that higher order knots might not require as expensive of hardware.

In number theory, we still don’t know much about almost all real numbers. Things like Chaitins constant exist — and such uncomputable, normal numbers form the bulk of the reals — but we don’t really know how to get our hands on them. In less exotic research, elliptic curves are used in cryptography. There’s some work I don’t quite understand in building out a homomorphic encryption — where we can operate on encrypted data.

In many areas, we’re still working through the algebra-geometry correspondence, which we got hints of 400 years ago but only formalized once topology and category theory were invented — and still are building tooling around.

And there’s lots of areas I have no idea about — but I assume are being similarly productive.

Ones that I know of, but can’t comment on: fractals, differential equations, bifurcation theory, and chaos theory.

Note: that's in an economics journal, "The Quarterly Journal of Economics". Not sure what, if anything, that has to do with math.

Indeed - Check out the amazing https://mathseminars.org/

My reaction to the various pictures, for what it's worth, was "what a beautiful place - perhaps it needs more blackboards, but I would love to do maths there".

You could also fit a large assortment of gourmet meats into a stack of shoeboxes, but it's preferable to display them in a way that whets the observer's appetite.

I'm not sure how big Oberwolfach's library is, but the first pic looks more like a (somewhat fancy) journals/new acquisitions display than like primary storage.