Leopold Franz is a PhD candidate at ETH Zürich with a decade of experience applying computational methods to reproductive health, developing algorithms to diagnose and optimize ovarian stimulation for IVF. He blends deep technical expertise in machine learning, time-series processing and bioinformatics with hands-on engineering experience from roles at Roche and Stanford collaborations. An active FemTech advocate, he has organized and partnered on industry summits and translated that ecosystem experience into venture scouting for early-stage deep tech startups. Leopold is skilled at turning large, messy biomedical datasets into production-ready pipelines and visualizations—once processing 42TB of ICU time-series data in 30 minutes for a clinical prediction project. He pairs academic rigor (MSc from ETH, thesis work with Stanford) with entrepreneurial instincts cultivated through hackathons, incubator programs, and sponsor development. Quietly entrepreneurial, he frequently sits at the intersection of lab, clinic and startup, making him effective at moving novel computational ideas toward real-world impact.
10 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Electrical, Electronics and Communications Engineering, A/A, Bachelor's degree, Electrical, Electronics and Communications Engineering, A/A at KTH Royal Institute of Technology
Master of Science - MS, Management, Technology and Economics, 5.5/6, Master of Science - MS, Management, Technology and Economics, 5.5/6 at ETH Zurich
Contributions:11 pushes, 1 branch in 4 years 2 months
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