Pedro Pessoa, PhD
@pedropessoaphd.bsky.social
Postdoctoral Researcher at Arizona State University
For a look at my research papers , tutorials and other scientific texts see my website https://pessoap.github.io/
For a look at my research papers , tutorials and other scientific texts see my website https://pessoap.github.io/
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
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
September 8, 2025 at 8:01 PM
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
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
6/6
✅ Simulate complex, non-Markovian biological dynamics
✅ Train conditional normalizing flows to approximate intractable likelihoods
✅ Perform full #Bayesian inference on anything you can simulate.
arxiv.org/abs/2506.09374
✅ Simulate complex, non-Markovian biological dynamics
✅ Train conditional normalizing flows to approximate intractable likelihoods
✅ Perform full #Bayesian inference on anything you can simulate.
arxiv.org/abs/2506.09374
June 19, 2025 at 10:25 PM
6/6
✅ Simulate complex, non-Markovian biological dynamics
✅ Train conditional normalizing flows to approximate intractable likelihoods
✅ Perform full #Bayesian inference on anything you can simulate.
arxiv.org/abs/2506.09374
✅ Simulate complex, non-Markovian biological dynamics
✅ Train conditional normalizing flows to approximate intractable likelihoods
✅ Perform full #Bayesian inference on anything you can simulate.
arxiv.org/abs/2506.09374
3/6
Because of that clock, division times aren’t memoryless -- they’re not exponential.
This breaks standard models of gene expression, that is:
NO Master Equations
NO Fokker-Planck equations
We had rethink how we do inference.
Because of that clock, division times aren’t memoryless -- they’re not exponential.
This breaks standard models of gene expression, that is:
NO Master Equations
NO Fokker-Planck equations
We had rethink how we do inference.
June 19, 2025 at 10:25 PM
3/6
Because of that clock, division times aren’t memoryless -- they’re not exponential.
This breaks standard models of gene expression, that is:
NO Master Equations
NO Fokker-Planck equations
We had rethink how we do inference.
Because of that clock, division times aren’t memoryless -- they’re not exponential.
This breaks standard models of gene expression, that is:
NO Master Equations
NO Fokker-Planck equations
We had rethink how we do inference.
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...
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...
April 7, 2025 at 6:14 PM
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...
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...
But how do we know how accurate our estimate of π really is? 🤔
There’s a way to do it right: Combining it with Bayesian inference. Instead of just getting a rough guess, we can properly quantify uncertainty.
That is what I have written in my blog today. Check it out
There’s a way to do it right: Combining it with Bayesian inference. Instead of just getting a rough guess, we can properly quantify uncertainty.
That is what I have written in my blog today. Check it out
March 14, 2025 at 7:50 PM
But how do we know how accurate our estimate of π really is? 🤔
There’s a way to do it right: Combining it with Bayesian inference. Instead of just getting a rough guess, we can properly quantify uncertainty.
That is what I have written in my blog today. Check it out
There’s a way to do it right: Combining it with Bayesian inference. Instead of just getting a rough guess, we can properly quantify uncertainty.
That is what I have written in my blog today. Check it out
Often in social media, questions like "find the next number in the sequence" are posed as indicators of intelligence. This approach to demonstrate cognitive prowess is dubious (to say it mildly).
Follow the🧵
#ProblemSolving #Mathematics #Logic #DataScience #Mathematics #Science #STEM #Education
Follow the🧵
#ProblemSolving #Mathematics #Logic #DataScience #Mathematics #Science #STEM #Education
January 26, 2025 at 7:31 PM
Often in social media, questions like "find the next number in the sequence" are posed as indicators of intelligence. This approach to demonstrate cognitive prowess is dubious (to say it mildly).
Follow the🧵
#ProblemSolving #Mathematics #Logic #DataScience #Mathematics #Science #STEM #Education
Follow the🧵
#ProblemSolving #Mathematics #Logic #DataScience #Mathematics #Science #STEM #Education