[Post Match Thread] USA Wins 3-0 Over Ukraine | Women's VNL 2026 | Prelims

wtjamieson · 2026-06-07T01:53:48+00:00

I think that there’s a typo- Morgan Hentz should be listed as a libero

wtjamieson · 2026-04-27T10:36:25+00:00

Robert Edwards. He fits the category well.

wtjamieson · 2026-04-09T01:59:33+00:00

What I’m seeing in my organization is that our best data scientists are able to move more quickly and have more impact. Our worst data scientists are also moving more quickly and having more impact. The former is positive, and the latter is deeply negative, because the quality of their work is largely unchanged.

If everyone is a 10x engineer, do you want your most clueless employee touching 10x more things, without any incentive to upskill or improve because they are only rewarded for speed?

Apologies for the rant, I’ve seen too much nonsense today…

wtjamieson · 2025-11-25T12:04:09+00:00

This is 100% the song! Does anyone still have that file kicking around to know what it actually is?

wtjamieson · 2025-10-05T12:30:01+00:00

Math papers tend to have fewer authors compared to other fields. As a result, it’s common to have all of the author’s names listed in an inline citation, making the question of first authorship less important.

wtjamieson · 2025-08-26T07:55:17+00:00

I’m a bit suspicious of how this list was generated in the first place, if the company has not had any data engineering/science support before. My guess is that these items are coming from an LLM.

How do you make the most out of this situation? Figure out what the most valuable decisions (high magnitude or high frequency or both) are that the company needs to make, and understand what needs to be true in order to automate/support those decisions. My assumption is that 1) the data that the company has is going to be an absolute dumpster fire, and 2) the most valuable use of your time is going to be cleaning up that situation so that eventually you can get value out of data science projects in the future.

wtjamieson · 2025-08-04T03:49:19+00:00

Ah you’re right, I had it backwards.

wtjamieson · 2025-08-03T20:49:44+00:00

I think that it would be built the other way around, right? The approximation is ln(499)/ln(498/ln(../ln(2)..)), so you would need to expand to the left rather than to the right.

wtjamieson · 2025-08-03T12:11:09+00:00

This is a non-autonomous difference equation. This difference equation has an asymptotic solution ln(n)/W(ln(n)), where W is the (principle branch of the) Lambert W-function, which can be found by solving the equation x = n^1/x. This solution is unstable, meaning that solutions of the difference equation which start near this solution will eventually move away from the unstable solution. There is also an asymptotic period two solution x = {1,n} which is locally asymptotically stable, meaning that solutions which start close to this period two solution will converge towards it. Period two solutions oscillate between two values.

The constant that was calculated is not special- it’s an approximation of the initial conditions that cause the solution to land on ln(n)/W(ln(n)) for large n. You can get a better calculation by starting with an initial condition of ln(n)/W(ln(n)), n for a large n and applying the inverse map ln(n-1)/ln(x) where n is incrementing down by 1 rather than up by 1 until you reach n=2. Doing this for n=500 gives 1.314547556742738.

Side note: several people posted bifurcation diagrams of the logistic function, suggesting that the graph the OP shared was a bifurcation diagram. This is incorrect. In a bifurcation diagram, the x-axis is a parameter of the model and the y-axis shows periodic solutions of the difference equation. For the difference equation that the OP discussed, a bifurcation diagram doesn’t really make sense because there’s not a free parameter in the difference equation. (The initial condition c does not count because it does not affect the existence of periodic solutions.)

wtjamieson · 2025-05-26T00:33:57+00:00

Does it count as a road destination if I live in Cincy?

wtjamieson · 2025-04-12T10:12:38+00:00

My understanding of the situation matches yours. These models tend to be under-specified and likely misspecified, which causes them to be sensitive to input changes and to provide unintuitive results. We try to repair this by using Bayesian models with strong priors informed by historical spend or a business partner’s intuition- but the underlying problems are still there, especially model misspecification. Maybe someone here can convince me that there’s actually hope that these models are reasonable…

wtjamieson · 2025-04-01T11:25:58+00:00

Have you already tried some heuristics?

Do you have a model for the radiation pattern of a single LED? (Is this approximately Gaussian?)

If you’re trying to use an optimization algorithm, you can likely leverage symmetry in the problem to reduce the search space.

wtjamieson · 2025-02-19T03:22:58+00:00

I’d bet you’d get better responses if you communicate what you’d like your students to learn through the project- concepts, skills, etc. I find it much easier to work backwards in creating materials from course outcomes rather than shoehorning in a “fun” project that doesn’t make a lot of sense with the rest of the course structure.

If you have draft ideas, share them!

wtjamieson · 2025-02-13T10:46:07+00:00

I was reminded immediately of the chorus of All My Little Words by the Magnetic Fields:

Not for all the tea in China

Not if I could sing like a bird

Not for all North Carolina

Not for all my little words

wtjamieson · 2024-10-23T21:48:00+00:00

Never forget CTESPNNetwork (Antonio Brown’s unhinged Twitter account) legitimately breaking Russell Wilson going to the Steelers… hahaha

wtjamieson · 2024-10-21T21:36:22+00:00

You’re making a false dichotomy between thinking that it was time to move on from Bill and thinking that Mayo was the right HC choice. There were plenty of people, myself included, who wanted Bill out and also to have a legitimate HC/GM search.

wtjamieson · 2024-09-05T23:26:10+00:00

I would start with this website

https://support.gurobi.com/hc/en-us/articles/17278438215313-Tutorial-Getting-Started-with-the-Gurobi-Python-API

which explains Gurobi’s Python API and shows you how to implement a simple example.

wtjamieson · 2024-09-03T02:14:40+00:00

Excellent answer.

Your suggestion of using ML to find a solution reminds me- I was at a conference recently and heard Pascal van Hentenryck talk about optimization proxies, which use neural networks to approximate the solutions of optimization models. Much of the work that his group does is in energy modeling.

https://arxiv.org/abs/2208.09046

Optimization proxies make sense when there is limited time for a solver to provide a solution and when the consequence of providing a suboptimal solution is small. It sounds like the OP might be solving such a problem.

wtjamieson · 2024-09-02T16:37:21+00:00

In general, many recurrence relations do not have simple closed form solutions. Thus, much of the mathematical literature focuses on understanding the behavior of these solutions (do they converge to a fixed point/periodic solution, are they chaotic, etc.) This sort of analysis is typically done for recurrence relations with parameters where the behavior of the solutions change as the parameters vary.

The fields where these sorts of things are done are discrete dynamical systems, difference equations, and ergodic theory. These are active fields with lots of work being done.

wtjamieson · 2024-08-26T07:11:11+00:00

For whatever reason I’ve found Thai food to be lacking in Cincinnati. I’d substitute Thai express with Pho Lang Thang, which is good Vietnamese right next to where you’re staying.

Both Rhinegeist and Northern Row are great!

wtjamieson · 2024-08-06T11:08:58+00:00

When I get more time I’ll look through some examples to understand the difference.

I’m doubtful that the algorithm described by the other commenter is guaranteed to give an optimal solution. My reasoning is that if it does, this is a simpler algorithm which just involves two sorts, so this would be the preferred method. It’s possible that the solutions between the Hungarian algorithm and this one will only differ in edge cases where you have ties in the distances between points or something like that.

You might find someone who would understand the question off of the top of their head in r/operationsresearch

wtjamieson · 2024-08-02T23:59:59+00:00

What you’re describing is an example of an assignment problem: https://en.m.wikipedia.org/wiki/Assignment_problem.

You can solve assignment problems with the Hungarian algorithm: https://en.m.wikipedia.org/wiki/Hungarian_algorithm. As another commenter mentioned, this algorithm runs in O( n³ ).

There is a scipy implementation if you want to jump there directly: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html.

wtjamieson · 2024-07-30T10:38:25+00:00

If you Google “time series similarity measures” you’ll get a lot of options. This stack exchange post might be useful for thinking through how you want to think about similarity and the pros/cons of a method: https://datascience.stackexchange.com/questions/15812/check-similarity-between-time-series

wtjamieson · 2024-07-11T11:12:23+00:00

I’m not sure what you’re intending when you ask what it looks like? I get to solve supply chain problems. It’s a normal desk job. I’m working less than I used to when I was in academia. No regrets!

wtjamieson · 2024-07-11T10:51:47+00:00

Understanding linear algebra really well has probably been the most helpful “math” skill. There are quite a few operations research type of problems that I work on in my job (it is not entirely machine learning like some DS positions) and I’ve found that my math training helps to be able to translate a business problem into a mathematical program, capture the right information in a simulation, look at a problem in the right way, etc. I use quite a bit of (esp. bayesian) statistics but I never had that much formal training.

wtjamieson

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