Sevvandi Kandanaarachchi

29 Followers
37 Following
21 Posts
Applied Mathematician working on statistical machine learning and data science research. Loves unusual data
pronounsshe/her
websitehttps://sevvandi.netlify.app
githubhttps://github.com/sevvandi
scholarhttps://scholar.google.com/citations?user=XIC1yhMAAAAJ&hl=en

Railways of Australia.

#rayshader adventures, an #rstats tale

... and now the proof of the Newton identity

\[ s_{n,k}(x) s_{n,k+2}(x) \leq s_{n,k+1}(x)^2 \]

is formalized! (If one is interested, one can find the proof at https://github.com/teorth/symmetric_project/blob/master/SymmetricProject/newton.lean .)

Copilot showed an uncanny ability to anticipate some of the steps of the proof, perhaps because it "knew" about the standard proof of this identity which I was following somewhat closely. The process is now faster than it was before, though again there was an unexpected speedbump. The standard proof of the Newton identity proceeds by reduction to the case \(k=0, n=2\), which turned out to be straightforward. What was unexpectedly tedious was the verification of the "base case"

\[ s_{0,0}(x) s_{0,2}(x) \leq s_{0,1}(x)^2 \]

which expands after a "routine" unpacking of the definitions to the AM-GM inequality

\[ x_1 x_2 \leq (\frac{x_1+x_2}{2})^2. \]

Somewhat annoyingly, I had to perform this unpacking in fine detail, for instance by tediously verifying that the 1-element subsets of {0,1} were {0} and {1}. Possibly I was missing out on some of Lean's automated tools for this (perhaps by making some trivial little lemmas to feed into Lean's simplifier). In any case, it is done. Next step is to establish Maclaurin's inequality, which I anticipate to be a quick corollary of the Newton identity.

symmetric_project/SymmetricProject/newton.lean at master · teorth/symmetric_project

Contribute to teorth/symmetric_project development by creating an account on GitHub.

GitHub

I had the pleasure of giving a keynote at the NHS-R/NHS.pycom 2023 conference this week and talked about building open source software in both #rstats and #Python.

If you're interested in learning more (and want to dive into #overviewR and #overviewpy - the packages that make your exploratory data analysis easier!) - here are my slides: https://bit.ly/talk-building-bridges

#rladies #pyladies

📣 [Community Call] R in Government

With Luíza Andrade, Karly Harker, Ahmadou Dicko @dickoa and Pablo Tiscornia @pablote

🕓 Tuesday, 31 October 2023 16:00 UTC -

We invite you to learn about the challenges and lessons learned from our panelists and attendees in their efforts to make their government data, processes, and analyses more open and reproducible.

📌 More info + join the event here: https://ropensci.org/commcalls/oct2023-government/

R in Government · Community Call

In this community call, our panelists will share their experiences and examples of projects with R at different levels of government and in different countries. We invite you to learn about the challenges and lessons learned from our panelists and attendees in their efforts to make their government data, processes, and analyses more open and reproducible. See below for speaker bios and resources.

Today I got text asking to update details with the bank. It gave a 1800 number. But the number was wrong. Yet another scam. #BankScam
Wildflowers at Yabbierangu,
Clifford Possum Tjapaltjarri (1932-2002), 1991
Source: Wikiart
#fractals
Slowly starting to read more toots and look at who to follow. Enjoying different posts about R and other #stats stuff.

Stephen Fry ‘Shocked’ to Discover #AI Stole His Voice From ‘Harry Potter’ Audiobooks and Replicated It Without Consent, Says His Agents ‘Went Ballistic'

(a human stole his voice but ya know...)

https://variety.com/2023/film/news/stephen-fry-ai-stole-voice-harry-potter-audiobooks-1235727795/

Stephen Fry Says AI Stole His Voice From Harry Potter Audiobooks

Stephen Fry recently revealed at the CogX Festival (via Forbes) that his voice from the “Harry Potter” audiobooks was taken by AI software and replicated without his consent, much to th…

Variety

Moving to a smaller instance. A re-#introduction.

I research #MachineLearning for Scientific Discovery. #ml4science #ai4science
I advocate for #OpenSource and #OpenScience when possible. A lot of my effort goes to solving problems in #LifeScience #Genomics and #RadioAstronomy.

Read our book on Mathematics for Machine Learning at https://mml-book.com

I cook to relax.

Mathematics for Machine Learning

Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press.

Mathematics for Machine Learning
And so it begins! #PositConf2023