Brandon Malone

Principal Consultant Generative AI & De Novo Drug Discovery

Madison, Wisconsin, United States
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Summary

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Senior
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Top School
Brandon Malone is a Principal Consultant specializing in generative AI-driven de novo drug discovery, with over a decade of machine learning and bioinformatics experience focused on immuno-oncology and precision medicine. He has translated probabilistic and graph-based ML methods into clinical impact—leading a personalized cancer vaccine platform with sustained recurrence-free outcomes in a Phase I/II cohort and validating a Bayesian "digital twin" that produced a 70% immune response in donor samples. Brandon combines a PhD in machine learning with industry leadership at NEC OncoImmunity and NEC Labs, building regulatory-ready GenAI architectures that embed automated fact verification and citation traceability. He routinely bridges research and product, authoring 40+ papers in top AI and life-science venues while turning complex RNA and T-cell biology into deployable drug-discovery systems. Based in Madison, WI, he now consults on novel GenAI architectures and de novo small-molecule and mini-protein design, often favoring probabilistic approaches that make models interpretable and auditable for clinical translation.
code10 years of coding experience
job11 years of employment as a software developer
bookPh.D., Machine Learning, Ph.D., Machine Learning at Mississippi State University
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Github Skills (15)

meta-learning10
bayesian10
automl9
automated-machine-learning9
feature-extraction9
hyperparameter-tuning9
hyperparameter-optimization8
scikit-learn8
data-science8
python8
time-series7
auto-sklearn6
algorithm5
bioinformatics4
genomics2

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

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bmmalone/pyllars

May 2017 - Feb 2022

This repository contains supporting utilities for Python 3, with an emphasis on data science tasks.
Contributions:431 commits, 3 PRs, 267 pushes in 4 years 9 months
data-sciencepython
dieterich-lab/rp-bp

Apr 2016 - Mar 2018

Rp-Bp is a Bayesian approach to predict, at base-pair resolution, ribosome occupancy and translation.
Contributions:407 commits, 293 pushes, 18 branches in 1 year 11 months
bayesian
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