Ce is an Assistant Professor in Computer Science at ETH Zürich. He believes that by making data—along with the processing of data—easily accessible to non-CS users, we have the potential to make the world a better place. His current research focuses on building data systems to support machine learning and help facilitate other sciences. Before joining ETH, Ce was advised by Christopher Ré. He finished his PhD round-tripping between the University of Wisconsin-Madison and Stanford University, and spent another year as a postdoctoral researcher at Stanford. His PhD work produced DeepDive, a trained data system for automatic knowledge-base construction. He participated in the research efforts that won the SIGMOD Best Paper Award (2014) and SIGMOD Research Highlight Award (2015), and was featured in special issues including “Best of VLDB” (2015), and the Nature magazine (2015).
I am looking for students who are excited about system research related to data management and machine learning. Send me an email if you are interested.
- Luyuan Zeng (Master’s Student).
Project. “Big data in small pocket” — Can we run machine learning on daily laptop with the help of an embedded GPU accelerator?
- Hantian Zhang (Master’s Student). Project. Machine Learning “Cheat Sheet” — What is the rule-of-thumbs in picking from machine learning models? Can Kaggle and extensive empirical analysis give an answer to this question?
I also work closely with the following students.
- Xupeng Li (PhD Student @ Peking University, co-advised with Bin Cui )
Project. Math is the new SQL? — Can we build a more declarative interface (hopefully LaTeX-compliant!) for a subset of machine learning workloads?
- Jiawei Jiang (PhD Student @ Peking University, Main Advisor: Bin Cui )
Project. Distributed Systems for Deep Learning.
- Lele Yu (PhD Student @ Peking University, Main Advisor: Bin Cui )
Project. Distributed Systems for Bayesian Inference.
- Yuting Ding (Master’s @ Tsinghua University, Collaboration with Chen Wang)
Project. Industrial Time Series Analysis.
- CAB F 71.2 @ ETH
- +41 44 632 75 29
Students: I am looking for students who are excited about system research related to data management and machine learning. Send me an email if you think that data is awesome, building systems is fun, Switzerland is beautiful, and calling Einstein and von Neumann your fellow alumni would be cool!
To give potential students a better understanding of my research interests, here I have detailed eleven research topics that I am currently excited about.
September 12th 2016
Dan Alistarh and I are trying to jointly find a master’s student at ETH to better understand low-precision distributed deep learning, from both theoretical and system perspective.
Students –you can find the master thesis proposal here.
September 1st 2016
Thank you to D-INFK@ETH to conduct an interview that allows me to articulate my vision for the future! You can find it here.
August 26th 2016
Our collaboration with Kun et al. appeared in Nature Communications today. In this paper, Kun shows that it is possible to build an automatic algorithm for lung cancer diagnoses that can match the accuracy of experienced pathologists.
August 13th 2016
Two machine-learning papers on which I collaborated with other researchers have been accepted by NIPS 2016 and Allerton 2016, respectively. I was fortunate to be able to help on these papers a little bit from a systems perspective.
Allerton 2016: Ioannis and Stefan (Stanford) have had a paper accepted by Allerton 2016. In this paper, Ioannis articulates elegantly the relationship between momentum and asynchrony for distributed stochastic gradient descent. The theoretical result is inspired by Stefan’s distributed deep-learning system, called Omnivore, which is described here.
Thank you to NVIDIA for the generous donation of one Jetson TX1 and one Titan X to support my group. These devices will enable the study of the following question: How fast can we make a subset of machine-learning algorithms, using just your everyday laptops with energy-efficient, credit-card-sized coprocessors?
Big Data in Small Pockets? –see this very preliminary one-pager for our vision! At the end, we hope to support GPUs, FPGAs, or even specially designed hardware. But first, we plan to understand the system trade-offs better with GPUs.
Students –if you are excited by this project, send me an email and let’s chat!
I will spend my summer visiting Peking University (Institute of Network) and Tsinghua University (School of Software). It is exciting to work with professors and students back in my hometown (one of which is also my alma mater)!