Daniel Russo is an Associate Director and computational research scientist with a decade of experience developing non-animal alternatives for chemical toxicity profiling and drug discovery, now leading data-driven projects at Merck. He blends deep academic training (PhD in Computational and Integrative Biology) with practical industry experience across FDA, academia, and pharma to create ML and cheminformatics approaches that leverage public and private data for small molecules, peptides, and nanoparticles. Daniel’s background spans hands-on wet-lab work and scalable modeling—from microbiology and GMP testing to deep learning model development on GPU hardware—giving him a rare ability to translate biological questions into robust computational solutions. Known for collaborating across academia, industry, and government, he frequently bridges regulatory and research needs to accelerate safer compound evaluation.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computational and Integrative Biology, GPA 3.96, Doctor of Philosophy (Ph.D.), Computational and Integrative Biology, GPA 3.96 at Rutgers University - Camden
Master of Science (M.S.), Biology, General, GPA 4.0, Master of Science (M.S.), Biology, General, GPA 4.0 at Rutgers, The State University of New Jersey-Camden
Contributions:3 pushes, 2 branches in 6 years 7 months
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