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/
Reposted by Pedro Pessoa, PhD
News story our APS fellowship.
A testament to the great people we work with (and the unwavering rigor of Bayesian approaches, impervious to hype) 😊 🙏
news.asu.edu/b/20251021-a...
A testament to the great people we work with (and the unwavering rigor of Bayesian approaches, impervious to hype) 😊 🙏
news.asu.edu/b/20251021-a...
ASU chemistry and physics professor elected to prestigious fellowship | ASU News
Steve Pressé, professor in Arizona State University’s School of Molecular Sciences and Department of Physics, has been elected as a 2025 American Physical Society Fellow for his leadership and excepti...
news.asu.edu
October 23, 2025 at 5:16 AM
News story our APS fellowship.
A testament to the great people we work with (and the unwavering rigor of Bayesian approaches, impervious to hype) 😊 🙏
news.asu.edu/b/20251021-a...
A testament to the great people we work with (and the unwavering rigor of Bayesian approaches, impervious to hype) 😊 🙏
news.asu.edu/b/20251021-a...
For full paper:
doi.org/10.7554/eLif...
doi.org/10.7554/eLif...
doi.org
September 15, 2025 at 6:12 AM
For full paper:
doi.org/10.7554/eLif...
doi.org/10.7554/eLif...
In our work, we introduce REPOP, a Bayesian computational framework that more accurately quantifies bacterial populations from plate counts by modeling the experimental noise introduced through dilution and plating.
September 8, 2025 at 8:01 PM
In our work, we introduce REPOP, a Bayesian computational framework that more accurately quantifies bacterial populations from plate counts by modeling the experimental noise introduced through dilution and plating.
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
Reposted by Pedro Pessoa, PhD
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
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
REPOP: bacterial population quantification from plate counts
elifesciences.org
August 6, 2025 at 10:30 PM
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
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
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...
🎧 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...
Mamba time series forecasting with uncertainty quantification
YouTube video by Xiaol.x
www.youtube.com
August 5, 2025 at 2:56 AM
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...
🎧 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...
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
5/6
We apply this to yeast expressing GFP under the glc3 promoter.
🌱 At first glance, high fluorescence seems like gene activation. But when you model protein inheritance across divisions...
Most cells are actually inactive — just glowing their ancestors GFP.
We apply this to yeast expressing GFP under the glc3 promoter.
🌱 At first glance, high fluorescence seems like gene activation. But when you model protein inheritance across divisions...
Most cells are actually inactive — just glowing their ancestors GFP.
June 19, 2025 at 10:25 PM
5/6
We apply this to yeast expressing GFP under the glc3 promoter.
🌱 At first glance, high fluorescence seems like gene activation. But when you model protein inheritance across divisions...
Most cells are actually inactive — just glowing their ancestors GFP.
We apply this to yeast expressing GFP under the glc3 promoter.
🌱 At first glance, high fluorescence seems like gene activation. But when you model protein inheritance across divisions...
Most cells are actually inactive — just glowing their ancestors GFP.
4/6
Despite the complexity, these dynamics are easy to simulate — protein production, cell division, fluorescence, all of it.
So we flipped the problem: We train neural networks on simulations to learn the likelihood function itself.
Despite the complexity, these dynamics are easy to simulate — protein production, cell division, fluorescence, all of it.
So we flipped the problem: We train neural networks on simulations to learn the likelihood function itself.
June 19, 2025 at 10:25 PM
4/6
Despite the complexity, these dynamics are easy to simulate — protein production, cell division, fluorescence, all of it.
So we flipped the problem: We train neural networks on simulations to learn the likelihood function itself.
Despite the complexity, these dynamics are easy to simulate — protein production, cell division, fluorescence, all of it.
So we flipped the problem: We train neural networks on simulations to learn the likelihood function itself.
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
In this new preprint, we analyze #flowcytometry data of stress regulation in yeast
🧬 We indirectly observe protein levels through fluorescence.
But here's the catch:
1 - Proteins live much longer than a single cell cycle
2 - Cell division follows a biological clock
In this new preprint, we analyze #flowcytometry data of stress regulation in yeast
🧬 We indirectly observe protein levels through fluorescence.
But here's the catch:
1 - Proteins live much longer than a single cell cycle
2 - Cell division follows a biological clock
June 19, 2025 at 10:25 PM
2/6
In this new preprint, we analyze #flowcytometry data of stress regulation in yeast
🧬 We indirectly observe protein levels through fluorescence.
But here's the catch:
1 - Proteins live much longer than a single cell cycle
2 - Cell division follows a biological clock
In this new preprint, we analyze #flowcytometry data of stress regulation in yeast
🧬 We indirectly observe protein levels through fluorescence.
But here's the catch:
1 - Proteins live much longer than a single cell cycle
2 - Cell division follows a biological clock
No likelihood? No problem
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
Simulation-trained conditional normalizing flows for likelihood approximation: a case study in stress regulation kinetics in yeast
Physics-inspired inference often hinges on the ability to construct a likelihood, or the probability of observing a sequence of data given a model. These likelihoods can be directly maximized for para...
arxiv.org
June 19, 2025 at 10:25 PM
No likelihood? No problem
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
6/6
If you plate, you need REPOP.
Preprint -- doi.org/10.1101/2025...
Software -- github.com/PessoaP/REPOP
Special thanks to the Lab Members - Pedro Pessoa, Carol Lu and Stanimir Tashev
As well as Rory Kruithoff and Douglas P Shepherd
#Biophysics #QuantitativeBiology
If you plate, you need REPOP.
Preprint -- doi.org/10.1101/2025...
Software -- github.com/PessoaP/REPOP
Special thanks to the Lab Members - Pedro Pessoa, Carol Lu and Stanimir Tashev
As well as Rory Kruithoff and Douglas P Shepherd
#Biophysics #QuantitativeBiology
REPOP: bacterial population quantification from plate counts
Bacterial counts from native environments, such as soil or the animal gut, often show substantial variability across replicate samples. This heterogeneity is typically attributed to genetic or environ...
www.biorxiv.org
April 7, 2025 at 6:14 PM
6/6
If you plate, you need REPOP.
Preprint -- doi.org/10.1101/2025...
Software -- github.com/PessoaP/REPOP
Special thanks to the Lab Members - Pedro Pessoa, Carol Lu and Stanimir Tashev
As well as Rory Kruithoff and Douglas P Shepherd
#Biophysics #QuantitativeBiology
If you plate, you need REPOP.
Preprint -- doi.org/10.1101/2025...
Software -- github.com/PessoaP/REPOP
Special thanks to the Lab Members - Pedro Pessoa, Carol Lu and Stanimir Tashev
As well as Rory Kruithoff and Douglas P Shepherd
#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
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
GitHub - PessoaP/REPOP
Contribute to PessoaP/REPOP development by creating an account on GitHub.
github.com
April 7, 2025 at 6:14 PM
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
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 preprint, this
- Overestimatese variability
- Can miss real structure in your population: Subpopulations and/or multimodality as biological differences across samples,
As we show in the preprint, this
- Overestimatese variability
- Can miss real structure in your population: Subpopulations and/or multimodality as biological differences across samples,
April 7, 2025 at 6:14 PM
4/6
As we show in the preprint, this
- Overestimatese variability
- Can miss real structure in your population: Subpopulations and/or multimodality as biological differences across samples,
As we show in the preprint, 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.
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.
April 7, 2025 at 6:14 PM
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.
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...
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...
Hello all, 📣📣📣
If you do #PlateCounting , I want you to take a look at this new preprint.🧫🧫🧫
If you don't, I still encourage you to join for an interesting discussion.
Follow the thread 🧵
doi.org/10.1101/2025...
#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP
If you do #PlateCounting , I want you to take a look at this new preprint.🧫🧫🧫
If you don't, I still encourage you to join for an interesting discussion.
Follow the thread 🧵
doi.org/10.1101/2025...
#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP
REPOP: bacterial population quantification from plate counts
Bacterial counts from native environments, such as soil or the animal gut, often show substantial variability across replicate samples. This heterogeneity is typically attributed to genetic or environ...
www.biorxiv.org
April 7, 2025 at 6:14 PM
Hello all, 📣📣📣
If you do #PlateCounting , I want you to take a look at this new preprint.🧫🧫🧫
If you don't, I still encourage you to join for an interesting discussion.
Follow the thread 🧵
doi.org/10.1101/2025...
#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP
If you do #PlateCounting , I want you to take a look at this new preprint.🧫🧫🧫
If you don't, I still encourage you to join for an interesting discussion.
Follow the thread 🧵
doi.org/10.1101/2025...
#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP
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
Happy #PiDay, everybody! 🥧🥧🥧🥧🥧🥧
Today, we celebrate π with a fun (but dubious) way to calculate it:
1️⃣ Toss random points into a square.
2️⃣ Count how many land inside the inscribed circle.
3️⃣ Use the ratio to approximate π/4
labpresse.com/2053-2/
#Bayes #DataScience #MonteCarlo #Probability
Today, we celebrate π with a fun (but dubious) way to calculate it:
1️⃣ Toss random points into a square.
2️⃣ Count how many land inside the inscribed circle.
3️⃣ Use the ratio to approximate π/4
labpresse.com/2053-2/
#Bayes #DataScience #MonteCarlo #Probability
Bayesian PI – Pressé LabWelcome file
labpresse.com
March 14, 2025 at 7:50 PM
Happy #PiDay, everybody! 🥧🥧🥧🥧🥧🥧
Today, we celebrate π with a fun (but dubious) way to calculate it:
1️⃣ Toss random points into a square.
2️⃣ Count how many land inside the inscribed circle.
3️⃣ Use the ratio to approximate π/4
labpresse.com/2053-2/
#Bayes #DataScience #MonteCarlo #Probability
Today, we celebrate π with a fun (but dubious) way to calculate it:
1️⃣ Toss random points into a square.
2️⃣ Count how many land inside the inscribed circle.
3️⃣ Use the ratio to approximate π/4
labpresse.com/2053-2/
#Bayes #DataScience #MonteCarlo #Probability
"What is a GPTase?"
Answer: Protein that destroys large language models
😂 😂 😂
#AI #biophysics #BPS2025
Answer: Protein that destroys large language models
😂 😂 😂
#AI #biophysics #BPS2025
February 17, 2025 at 7:21 PM
"What is a GPTase?"
Answer: Protein that destroys large language models
😂 😂 😂
#AI #biophysics #BPS2025
Answer: Protein that destroys large language models
😂 😂 😂
#AI #biophysics #BPS2025
Reposted by Pedro Pessoa, PhD
It’s my favorite time of the year where I see my colleagues and hear about their amazing work at the annual Biophysical society meeting in LA! The Biological Fluorescence Symposium subgroup session is in full swing! #BPS2025
February 15, 2025 at 6:13 PM
It’s my favorite time of the year where I see my colleagues and hear about their amazing work at the annual Biophysical society meeting in LA! The Biological Fluorescence Symposium subgroup session is in full swing! #BPS2025
Reposted by Pedro Pessoa, PhD
Attending #BPS2025? Want to know more about tangible steps you can take to challenge the attacks on science in the U.S.? Please attend an Emergency Town Hall Meeting on Tuesday at 1:30! @biophysicalsoc.bsky.social @blackinbiophys.bsky.social Please spread the word!
February 15, 2025 at 4:45 PM
Attending #BPS2025? Want to know more about tangible steps you can take to challenge the attacks on science in the U.S.? Please attend an Emergency Town Hall Meeting on Tuesday at 1:30! @biophysicalsoc.bsky.social @blackinbiophys.bsky.social Please spread the word!