Fedor Sergeev,

"With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." - John von Neumann

PhD Student

Department of Computer Science
Biomedical Informatics Group
Universitätstrasse 6
8092 Zürich

Informing machine learning models with human insights for better performance, reliability, and interpretability

I did my BSc in Applied Mathematics and Physics at MIPT. I developed computational methods for simulations in physics under the supervision of Igor Petrov and Nikolay Khoklov. In parallel, I worked on applications of deep learning in high-energy physics at GSI and LAMBDA

In my MSc I studied Computational Sciences and Engineering EPFL, combining my interest in numerical and data-driven modeling. My thesis with Pascal Fua and Jonathan Donier was on physics-informed neural networks for modeling fluid flow. During my studies, I interned at startup companies Spiden and Neural Concept, working on synthetic data generation for medical spectroscopic data and 3D computer vision for advanced engineering, respectively.

I joined BMI lab in July 2023 to work on multimodal, representation and Bayesian deep learning on intensive care unit (ICU) data. I am also an ELLIS PhD student, co-supervised by Vincent Fortuin.