Samuel Erickson | EECS, KTH Royal Institute of Technology

About Me

I am a Ph.D. student at KTH Royal Institute of Technology, advised by Prof. Mikael Johansson.

My research is focused on large scale distributed learning for deep learning. But I am broadly interested in machine learning, statistics and optimization, particularly with applications in medicine and finance.

I hold a MSc. in Engineering Mathematics from Linköpings universitet, the first year of which I spent at Stanford University as a visiting graduate student. During the spring of 2024 I conducted my master’s thesis research at Lynx Asset Management. Prior to my year at Stanford, I obtained a BSc. in Applied Physics and Electrical Engineering, and a BSc. in Mathematics, both from Linköping University.

Education

KTH Royal Institute of Technology

PhD, Electrical Engineering 2024 – 2028

Stanford University

Visiting Graduate Student, Electrical Engineering 2022 – 2023

Linköping University

MSc., Engineering Mathematics 2022 – 2024

BSc., Mathematics 2021 – 2024

BSc., Applied Physics and Electrical Engineering 2019 – 2022

Publications

ICML

Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers

Samuel Erickson, Mikael Johansson

Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026.

PDF Code Page BibTex

Lecture notes

An Introduction to Metric Spaces

Samuel Erickson, Anders Björn, David Wiman

PDF Page BibTex Lectures notes for TATA34