About me
I am a computational biologist at Altos Labs, where I develop hybrid approaches combining biophysical principles, Bayesian inference, and deep learning. Across my work, I keep returning to the same question: what can we reliably learn from noisy biological measurements?
My research has moved from gene regulation to evolutionary dynamics and single-cell genomics. Experience performing experiments, including fluorescence microscopy, shapes how I think about computational problems: understanding how a measurement was made is part of understanding what it means.
Background
- Altos Labs · Scientist II, Computational Biology · August 2025–present. Hybrid modeling and Bayesian methods for biological data.
- SoftMax AI · Visiting Researcher · May–June 2025. Six-week contribution to multi-agent reinforcement learning, including exploration curricula and reward design.
- Stanford University · Postdoctoral Scholar and Schmidt Science Fellow · October 2021–August 2025. Research with Dmitri Petrov and collaboration with Madhav Mani on fitness inference and evolutionary landscapes.
- Superfluid Dx · Statistical Consultant · February–April 2024. Modeling of clinical bulk RNA-seq data, including sequencing depth and batch effects.
- Caltech · PhD, Biochemistry and Molecular Biophysics · 2014–2021. Research with Rob Phillips on the physics and information processing of gene regulation.
- Instituto Politécnico Nacional · BSc, Biotechnological Engineering · 2009–2014.
Communicating science
I enjoy explaining quantitative biology across disciplines and in both English and Spanish. Selected talks and features offer another view of my work and the path that brought me here.
- iBiology seminar, English and Spanish: presentations of my scientific work for a broader audience.
- We are Schmidt Science Fellows: a view of the fellowship community.
- Quanta Magazine: The Math That Tells Cells What They Are: a feature on quantitative approaches to cellular behavior.
Contact
manuel.razo.m@gmail.com · GitHub · Google Scholar · LinkedIn