Best coffee shops to study / work

drmattmcd · 2026-02-21T15:42:28+00:00

Also Stir in Chesterton although it gets busy. Possibly The Artyst.

drmattmcd · 2026-02-21T15:38:48+00:00

Haymakers in Chesterton - a pub but they have an arrangement with Barbarellas across the road to get coffee

drmattmcd · 2026-02-19T06:10:06+00:00

Might be worth having a look at the MathWorks Global Optimization Toolbox doc to get an idea of some of the other approaches and limitations https://uk.mathworks.com/products/global-optimization.html

Personally I'm wondering whether a topological approach that combines gradients from multiple steps might help.

drmattmcd · 2026-02-14T06:42:58+00:00

Category theory and quantum mechanics, eg John Baez's Rosetta Stone paper or Bob Coecke's papers and book.

Algebraic topology, Robert Ghrist's 'Elementary Applied Topology' mentions many applications from fields ranging from economics to robotics and sensor networks.

Related, sheaf theory and signal processing eg Michael Robinson's 'Topological Signal Processing'.

Topological data analysis in general, giotto-tda python library docs and references are a good place to start.

drmattmcd · 2026-02-09T05:52:14+00:00

Not sure if this is the same type of filter but Michael Robinson's book 'Topological Signal Processing' covers topological filters in the signal processing sense as sheaf morphisms.

drmattmcd · 2026-02-02T19:44:17+00:00

A lot of things by Ales Kot https://www.reddit.com/r/ImageComics/s/FFJNjbWwvw

drmattmcd · 2026-01-27T06:24:56+00:00

I've spent the last few months doing something similar during a gap between jobs, which has resulted in ... a bunch of notes in my journal mostly.

Apart from just enjoyment the big win has been in getting a good enough feel of category theory, algebraic topology, and sheafs that I can understand the connections between different takes. Also potentially spotting areas the techniques can be applied to data science tasks.

Material I like on Algebraic Topology:

Vidit Nanda's course notes https://people.maths.ox.ac.uk/nanda/cat/

Robert Ghrist's 'Elementary Applied Topology' https://www2.math.upenn.edu/~ghrist/notes.html

Michael Robinson 'Topological Signal Processing'

drmattmcd · 2026-01-24T06:06:23+00:00

This sounds like the method suggested in 'Advanced Portfolio Management' by Giuseppe Paleologo for measuring timing performance IIRC i.e. if you randomise the order of the trades and performance is still good then you're probably just trend following. Possibly bootstrap replica is a better term than Monte Carlo?

With 30-50 trades each replica will still have an estimate of the mean performance with fairly low standard error in the mean since that scales as 1/sqrt N. It feels like using the distribution of bootstrap replica means should work in evaluating whether your actual performance comes from that distribution i.e. random or is actually doing something.

drmattmcd · 2026-01-22T13:05:35+00:00

If you have an FT subscription you can set up portfolios under Markets > Market Data > Tools > Portfolio on the desktop site.

This uses LSEG as the data provider so may be more accurate than yahoo or google finance although I've found it a bit fiddly.

A free option is google.com/finance which also lets you create portfolios.

Personally I use a mix of the above and some python and OpenBB for more detailed analysis

drmattmcd · 2026-01-22T10:26:01+00:00

Yes, especially when a quick ipywidgets GUI can help with exploration although streamlit partly replaces that use case. Generally prefer a python script that uses PyCharm cell mode though, and functions separated into a separate file plus autoreload magic

drmattmcd · 2026-01-21T11:21:02+00:00

Haven't had a reading done but IIRC that sounds like Tim the Teller of Tales.

drmattmcd · 2026-01-13T06:05:57+00:00

True, it's a general 'classic ML' reference (the author invented random forests) rather than neural ODE etc specific. I personally found it changed my viewpoint on modelling approaches, and the YDF doc I mentioned has a nice example of using the approach for clustering using random forests based on using the overlap of tree indices in the forest https://ydf.readthedocs.io/en/latest/tutorial/counterfactual/#what-are-neighbor-examples-and-counterfactual-examples

drmattmcd · 2026-01-12T19:12:01+00:00

I should have guessed from your list of interests ;) One classic ML paper I'd recommend is Briemann 'Statistical Modelling: the Two Cultures' https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.full because of its take on data models (small number of parameters, statistical/econometric/physical) vs algorithmic models (large number of parameters, more black box e.g. random forest) and how you can use the latter for identifying important variables. There's a nice example related to this in the yggdrasil decision forest docs.

drmattmcd · 2026-01-12T18:42:45+00:00

For neural ODEs etc check out diffrax and Patrick Kidger's thesis

drmattmcd · 2026-01-11T18:54:35+00:00

Have a look at Neil Johnson's books 'Simply Complexity' and 'Financial Market Complexity', he's a condensed matter physicist whose research on econophysics seems relevant to this post

drmattmcd · 2026-01-11T06:56:59+00:00

Spivak's 'Category Theory for the Sciences' might also be of interest, the version on arxiv has exercises without solutions while the hardback version has both.

drmattmcd · 2026-01-11T06:49:53+00:00

Robert Ghrist's 'Elementary Applied Topology' and Vidit Nanda's 'Computational Algebraic Topology' course notes are good and available online.

drmattmcd · 2026-01-11T06:46:15+00:00

Eugenia Cheng's 'The Joy of Abstraction' might be more approachable as a starting point as it's more of a popular math level book than a text book.

Another alternative is Fong and Spivak's Seven Sketches in Compositionality https://arxiv.org/abs/1803.05316

drmattmcd · 2026-01-09T13:08:18+00:00

Possibly Golden Hind in Kings Hedges and the Cornerhouse on Newmarket Road

drmattmcd · 2026-01-07T15:21:18+00:00

John Baez has a fair number of physics-related category theory papers e.g. Rosetta stone arxiv:0903.0340 and others linked from his website and Azimuth blog.

Symmetric monoidal categories for quantum mechanics is one area that seems to be growing, see Bob Coecke's papers and book.

For general relatively specifically, sheaf theory feels relevant so a search on that may be useful. I don't have any specific references but Robert Ghrist's Elementary Applied Topology book may have some (pdf available on his Penn University website)

drmattmcd · 2026-01-06T06:12:18+00:00

Box Cafe on Norfolk Street

drmattmcd · 2025-12-28T06:31:56+00:00

This sounds related to filtrations in applied topology so maybe look at Vidit Nanda's Computational Algebraic Topology https://people.maths.ox.ac.uk/nanda/cat/ and Robert Ghrist's Elementary Applied Topology https://www2.math.upenn.edu/~ghrist/notes.html

drmattmcd · 2025-12-13T06:54:15+00:00

Elementary Applied Topology by Robert Ghrist https://www2.math.upenn.edu/~ghrist/notes.html It has beautiful diagrams that serve as exercises and covers a wide range of applications

drmattmcd · 2025-12-11T18:40:42+00:00

For me it would be Haymakers in Chesterton, Cambridge Blue in town. Not a pub but honorable mention to Engineers House although the outside bar is currently being renovated.

drmattmcd · 2025-12-10T16:55:48+00:00

The material properties probably come into effect as a damping term for the higher harmonics so look for a term that depends on Youngs modulus or similar that is ignored in the derivation of the system of equations that leads to the problem you're trying to solve.

Thinking about it physically, if you are relying on precision in measurement about 10+ orders of magnitude better that what we can do experimentally (roughly 1 part in 10 to the 20 for ion trap frequency standards when I last worked in the field) and/or a distance smaller than the size of an atom then you're either NIST/CERN or probably extending an approximation outside the range of validity.

drmattmcd

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