Summary
Daniel Pak is a software engineer specializing in AI/ML with nine years of experience applying machine learning to biomedical problems, particularly cardiac digital twin reconstruction. He holds a PhD-focused trajectory from Yale in AI/ML for cardiovascular modeling and has translated academic research into grants and prototypes—winning a PennHealthTech $50k award and securing an NIH F31 pre-doctoral fellowship. His experience spans industry and academia, including roles at Google, Cleerly, Generate Biomedicines (where he improved protein developability predictions using dMaSIF descriptors), and Yale as a postdoc and innovation fellow. Daniel combines deep domain knowledge in bioengineering and robotics with production ML skills, routinely bridging computational research and deployable solutions. Based in the United States, he is an aspiring entrepreneur who publishes technical project work such as an open heart-meshing portfolio that demonstrates hands-on simulation and reconstruction expertise.
9 years of coding experience
3 years of employment as a software developer
Illinois Mathematics and Science Academy
Doctor of Philosophy - PhD, AI/ML for cardiovascular modeling, Doctor of Philosophy - PhD, AI/ML for cardiovascular modeling at Yale University
Bachelor's degree, Bioengineering, Bachelor's degree, Bioengineering at University of Illinois Urbana-Champaign
Master's degree, Robotics and Bioengineering, Master's degree, Robotics and Bioengineering at University of Pennsylvania