Paul Harrison

165 Followers
73 Following
211 Posts
Bioinformatician with the Monash Genomics and Bioinformatics Platform, Monash University, Melbourne, Australia. I aim to control my false statement rate.
Websitehttps://logarithmic.net/pfh
X (deprecated)https://twitter.com/paulfharrison
Bluesky (maybe?)https://bsky.app/profile/paulfharrison.bsky.social

After several months of back and forth, I got my university to install Linux on a PC.

They also installed CrowdStrike.

Very sad to hear of the passing of Anita Morris. Anita was a friend I've known for many years, and this is rather sudden. Anita was a creative and determined person with a core of strength I have always respected.

I gather the funeral will be on the 10th of February. There are more details on the W.D. Rose website.

535.491โ€ฆ^i = 1

I'm not sure why I find this annoying.

Visitor by the balcony.

๐Ÿ”ฌ ๐—›๐—ฒ๐—น๐—ฝ ๐—œ๐—บ๐—ฝ๐—ฟ๐—ผ๐˜ƒ๐—ฒ ๐˜๐—ต๐—ฒ ๐—ฅ๐—ฒ๐—ฝ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐—ถ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐˜† ๐—ผ๐—ณ ๐—•๐—ถ๐—ผ๐—ถ๐—ป๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐˜๐—ถ๐—ฐ๐˜€ ๐—ง๐—ผ๐—ผ๐—น๐˜€ ๐—ถ๐—ป ๐—•๐—ถ๐—ผ๐—บ๐—ฒ๐—ฑ๐—ถ๐—ฐ๐—ฎ๐—น ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ๐˜€ + ๐—ช๐—ถ๐—ป ๐—ฎ ๐—ฉ๐—ผ๐˜‚๐—ฐ๐—ต๐—ฒ๐—ฟ! ๐ŸŽŸ๏ธ

My wonderful PhD student (not on fedi) is runnning a survey about researcher views about pathway enrichment analysis.

If you are a life scientist who has ever done a pathway analysis this survey is for you.

Survey Link๐Ÿ”— [https://lnkd.in/gHNMSD89]

๐Ÿ“Š Deakin University ethics approval (ref:2024/HE000066).

First 150 participants get a $10 AUD gift voucher.

#Genomics #Bioinformatics #Biology

LinkedIn

This link will take you to a page thatโ€™s not on LinkedIn

Happy little accidents...

I'm really liking this course on generative diffusion models. They seem to have boiled many years of confusing development of ideas down to a simple approach.

https://diffusion.csail.mit.edu/

Generative AI with Stochastic Differential Equations - IAP 2025

Missing Semester

The optimizer tries to find the lowest energy. This closely resembles "maximum likelihood" or "maximum a posteriori" estimation in statistics. We might hope this finds the most representative estimate. Clearly, here it does not! The estimate is smoother than most samples from the distribution.

Also the optimizer has not found the lowest energy state, which would be all-dark or all-light. It might take a very long time to reach one of these optima!

Second, sampling from the distribution with a Langevin Dynamics simulation. The algorithm is almost identical to gradient descent with momentum, but we add just the right amount of noise to the momentum at each step.