We are a computer science research group led by Ce Zhang with help from many close collaborators and friends.

Members Publications

One-pager Research Statement

Master Thesis: Spring 2018

We are offering a series of master thesis in the spring semester of 2018. Drop us an email at ce.zhang@inf.ethz.ch if you are interested. Some sample topics:

Applications: data-driven astrophysics, meteorology, social sciences, proteomics, reinforcement learning for telescope array control, self-driving etc.

Systems: scalable deep learning over a thousand GPUs, model compression for deep learning, in-database machine learning, blockchain and cryptocurrency as general computation platform, system control with predictive models, etc.

Machine Learning: foundations behind system relaxation: decentralized learning, low precision communication, asynchrony; multi-task learning etc.


SemEval 2018

  • Nora Hollenstein & Jonathan Rotsztejn‘s system ranked first in the relation classification subtask among 28 international teams in SemEval 2018 (Task 7 Subtask 1) ! Their system also ranks top 1 and top 2 for two other relation extraction subtasks (Task 7 Subtask 2).

VLDB 2018

  • Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads.


  • Prof. Heng Guo & Kaan Kara: Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond.

EDBT 2018

  • Demjan Grubic: Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study

NIPS 2017

  • Oral Presentation: Xiangru Lian and Prof. Ji Liu on decentralized learning [paper].
  • A 3-minutes teaser video: 


  • space.ml is featured in a News article in the Science magazine [link].
  • GalaxyGAN is selected as the Editor’s Choice in the Science magazine [link].

VLDB 2017 (Munich Aug 28 – Sep 1)

  • Come by our two talks: Lele Yu on building Bayesian Inference as a new service with hundreds of machines; Zhipeng Zhang on a comparative study on different SimRank algorithms [paper]. 
  • Also don’t miss the demo session: Xupeng Li on ease.ml version 1 — declarative in-database machine learning with a cute homomorphism between relational algebra and linear algebra [paper].

ICML 2017 (Sydney Aug 6 – Aug 11)

  • Hantian Zhang is going to give a talk about ZipML — low precision machine learning on modern hardware [paper].

SIGMOD 2017 (Chicago May 14 – May 19)

  • Jiawei Jiang gave the talk about a distributed machine learning system designed for heterogeneous infrastructure where straggler is expected [paper].
  • HILDA: Come by to hear our vision about ease.ml — Deep Learning in four lines to serve ETH scientists [paper].

Machine Learning on Modern Hardware

  • Kaan Kara: training linear models on FPGA with low precision [FCCM paper]
  • Ewaida Mohsen: Xgboost inference on FPGA that can deal with up to 20M tuples per second [FPL]!

space.ml (with Kevin Schawinski) gets covered by Science (Editor’s Choice), the Atlantic, and WIRED Science.

An ETH Globe article about DS3Lab.

Ce gives talks at ETH Meets New York and his Inaugural Lecture at ETH.
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Hantian and Dan give the ZipML session at NVIDIA GTC 2017