Cedric Renggli

Cedric Renggli

I am a first year PhD student at ETH Zurich's DS3Lab, supervised by Ce Zhang.

I hold a bachelor degree from the Bern University of Applied Sciences and received my MSc in Computer Science from ETH Zurich in 2018. My work on Efficient Sparse AllReduce For Scalable Machine Learning got awarded with the silver medal of ETH Zurich for outstanding master thesis. My main research interest lies in comparison-based/preferential optimization. I believe that this technique helps putting humans into the loop in a more structured way, which enables scientists of various domains to analyze their large-scale datasets more efficiently. Additionally, I’m working on different optimization techniques and systems for distributed machine learning algorithms.

My office is located at CAB E 61.2 and feel free to contact me by email cedric.renggli@inf.ethz.ch.



The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat and Cedric Renggli
(Authors ordered alphabetically)
NIPS 2018
Distributed Learning over Unreliable Networks
Hanlin Tang, Chen Yu, Cedric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu and Ce Zhang
Manuscript, Arxiv, 2018
SparCML: High-Performance Sparse Communication for Machine Learning
Cedric Renggli, Dan Alistarh, Torsten Hoefler and Mehdi Aghagolzadeh
Manuscript, Arxiv, 2018


MPIML: A High-Performance Sparse Communication Layer for Machine Learning (Poster)
Cedric Renggli, Dan Alistarh and Torsten Hoefler
NIPS 2017 Workshop: Deep Learning At Supercomputer Scale

MSc Thesis

Efficient Sparse AllReduce For Scalable Machine Learning
Cedric Renggli
Outstanding thesis award: Silver medal of ETH Zurich


Dept. of Computer Science
CAB E 61.2
Universitätsstrasse 6
8092 Zürich