[dpflan]

I believe a use-case(s) receiving attention is drug design, protein design, chemical design, etc.

Here is a summer school by the London Geometry and Machine Learning group where research topics are shared and discussed. - https://www.logml.ai/

Here is another group, a weekly reading group on graphs and geometry: https://portal.valencelabs.com/logg

[heyitsguay]

As someone who did an applied math PhD before drifting towards ML, it's worth pointing out that these applied math groups typically talk about applications, but the real question is whether they are actually used for the stated application in practice due to outperforming methods that use less pretty math. Typically (in every case i have seen) the answer is "no", and the mathematicians don't even really care about solving the applied problems nor fully understand what it would mean to do so. It's just a source of grant-justifiable abstract problems.

I would love to be proven wrong though!

[dpflan]

Indeed, the ivory tower has nice chats and ideas and is a cool place to hang out, but does application actually occur.

[funnygiraffe]

Thanks. That's certainly very interesting. Albeit it seems to me that the number of jobs doing geometric and topological ML/AI work in the drug or protein design space would be quite limited, because any discovery ultimately has to be validated through a wet lab process (or perhaps phase 1-3 clinical trials for drugs) which is expensive and time-consuming. However, I'm very uninformed and perhaps there is indeed a sizable job market here.

[dpflan]

I think the job market in general for this kind of stuff is "small"; but you can find jobs. Look at Isomoprhic Labs for example. There are new AI/ML companies that have emerged in recent years, helped by success of things like AlphaFold. I think your question is really: does this research actually creates tangible results? If it did, it would be able to create more jobs to support it by virtue of being economically successfully and therefore growing?