A guaranteed way to ensure madness and 10-hour weekend workdays:
1-Reconfigure login sites often
2-Require frequent passcode re-authentication w rapidly expiring passcodes
3-Change tabs weekly
4-Make forms picky about input formatting
5-Give no confirmation upon form submission

#facultylife

The journey continues. UT Austin and Oden tomorrow and Friday. Here with Dima Makarov :)
(Discussing Bach, Richter, Gould, Yuja Wang, and maybe some single molecule 😊)

One of my favorites by Voltaire in describing Canada
“quelques arpents de neige, habités par des barbares, des ours et des castors” 🙃

Thanks to the CTBP for the wonderful invitation for a seminar at Rice! 🙏
Joining me are Tolya Kolomeisky and Oleg Igoshin.

Hello everyone,

Tomorrow I’ll be giving a chalk talk on our new eLife:
“REPOP: bacterial population quantification from plate counts”

elifesciences.org/reviewed-pre...

Looking forward to seeing you!!

#eLife #datascience #biophysics #bioinformatics #Bayesian #REPOP

Thanks for the link to your paper!

Can we learn motion models from post-processed tracks? 🧐
Not really 😢

Emission noise accounts for ~99% of the likelihood.

TLDR: What you think is anomalous diffusion… might just be noise. 🤷‍♂️🤷‍♂️🤷‍♂️🤷‍♂️

🔗 Read more in our latest preprint: arxiv.org/abs/2507.05599

#Biophysics

6/6
If you plate, you need REPOP.

Software -- github.com/PessoaP/REPOP
Preprint -- elifesciences.org/reviewed-pre...

Special thanks to the Lab Members @pedropessoaphd.bsky.social, Carol Lu and Stanimir Tashev

As well as Rory Kruithoff and @dpshepherd.bsky.social
#Biophysics #QuantitativeBiology

5/6
This is why we built REPOP, an #opensource tool to REconstruct POpulations from Plates.

Straightforward to use and with tutorials available on #GitHub

github.com/PessoaP/REPOP

With all the #Bayesian rigor and #PyTorch speed

4/6
As we show in the paper, this
- Overestimatese variability
- Can miss real structure in your population: Subpopulations and/or multimodality as biological differences across samples,

3/6
This assumes:
– No randomness in how many bacteria end up on the plate
– No randomness in the original swab

In reality, every step is noisy.

2/6
Plate counting is a simple:

You dilute a sample, plate a small volume, and count colonies.

Say you dilute by 200×, and count 50 colonies.
Easy just multiply 50 × 200 = 10k bacteria, right?

NOT QUITE...

Hello all,

If you do #PlateCounting, you may want to take a look at our new eLife @elife.bsky.social

If you don't, I still encourage you to join for an interesting discussion.

Follow the thread 🧵

elifesciences.org/reviewed-pre...

#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP

3/3
In it, we simulate some general physical systems that violate the HMM's assumptions and demonstrate contradictory results that can arise. Surprisingly, the problems with HMM analysis only grow with better data acquisition (higher data acquisition rate and/or reduced noise).

2/3
HMM are classic in time series analysis, but they can yield confusing, seemingly contradictory results. In particular, when applying HMMs to physical systems where two key HMM assumptions, that state spaces are discrete, and that transitions are instantaneous, don't apply.

Have you ever applied a Hidden Markov Model (HMM)?
Does the results seems contradictory?

If so, you are going to love our new preprint:

arxiv.org/abs/2506.05707

So… I was googling myself and made quite a discovery

🎧 There's an AI-generated podcast of my #TimeSeriesForecasting paper 🤔🤔
www.youtube.com/watch?v=3BNz...

Not sure whether to feel flattered, creeped out, or alarmed.

That is the future I guess 🤷‍♂️🤷‍♂️

To read the full paper: doi.org/10.1088/2632...

The Pressé Lab is happy to join the #biophysics #imaging & #singlemolecule scientific communities here.

Hopefully high SNR messaging from this account about low SNR data :)

#science #machinelearning #datamodeling