Jose Guzman is an AI research engineer with eight years of experience building multimodal models for biological applications, currently applying that expertise at Cimulate AI from Cambridge, MA. He pairs hands-on machine learning practice—work on protein language models and structure-aware multi-modal systems—with deep theoretical training as a PhD mathematician specializing in logarithmic algebraic geometry at MIT. His trajectory blends industry ML research (Manus, reinforcement learning projects at MIT) with rigorous math research under Davesh Maulik, enabling him to bridge abstract geometric thinking and practical model engineering. Colleagues describe him as a visionary who translates complex mathematical ideas into novel AI approaches for biology, and his background suggests an uncommon fluency in both symbolic math and data-driven model design.
8 years of coding experience
Doctor of Philosophy - PhD Mathematics, Doctor of Philosophy - PhD Mathematics at Massachusetts Institute of Technology
Bachelor of Science - BS Mathematics, Bachelor of Science - BS Mathematics at The University of Texas at Austin
Example project with AWS API Gateway Lambda talking to SQS and a Lambda reading from SQS.
Contributions:27 PRs, 47 pushes, 26 branches in 2 years 9 months
api-gatewayapiaws-api-gatewayaws-apiserverless
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