Computational biology · Biophysics · Machine learning
What can we reliably learn from noisy biological measurements?
I’m Manuel Razo-Mejia, a Scientist II in Computational Biology at Altos Labs.
I build biophysical models, Bayesian methods, and scientific software to understand biological data. My research spans single-cell genomics, evolutionary dynamics, and gene regulation, with experiments shaping how I model measurements and test predictions.
Selected research
Measuring fitness with uncertainty
Bayesian inference for pooled competition assays, from DNA barcode counts to relative fitness estimates. Released as BarBay.jl.
Learning evolutionary landscapes
Using geometry-aware autoencoders to uncover structure in phenotype-to-fitness maps and study antibiotic resistance.
Reliable single-cell inference
SCRIBE models measurement noise and uncertainty in single-cell RNA sequencing with scalable Bayesian inference.
From measurements to models
I work across mechanistic modeling, statistical inference, and deep learning, choosing the method around the biological question. I also build the software that lets other researchers inspect assumptions and reproduce an analysis.
Previously, I was a Schmidt Science Fellow and postdoctoral scholar at Stanford, following a PhD in Biochemistry and Molecular Biophysics at Caltech. More about my background.