Summary
Ryan Robinett is a machine learning researcher and PhD candidate at the University of Chicago, focusing on biomathematics through manifold learning, Riemannian optimization, and Wasserstein geometry. He blends rigorous mathematics with practical ML, building scalable algorithms in Python (Scikit-Learn, PyTorch, Pymanopt, JAX) as well as Julia, MATLAB, R, and Wolfram. Over the past decade, he has contributed across academia and research labs, including MIT CSAIL where he designed deterministic DNN topologies via mixed-radix systems and Kronecker products, implemented in Julia and Python, and collaborated with MIT Lincoln Laboratory's Supercomputing Group to train large sparse networks. He also developed mathematical models of receptor signaling during his MIT and Chicago work, reflecting a strong interdisciplinary bent. Based in the Greater Boston area, he is advancing his PhD while leading research at the University of Chicago and the Pritzker School of Molecular Engineering, with a track record of translating theory into testable algorithms. A notable strength is turning abstract functional-analytic ideas into concrete machine learning methods with potential biomedical impact.
12 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at Massachusetts Institute of Technology
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Chicago
English, Spanish, French