[Adapt] FW: 周五（明天上午10点）学术报告

[Kenny Zhu]

Please go to this talk if you are interested. All grad students at ADAPT are
required to go to this talk.

 

Kenny

 

From: Yanyan Shen [mailto:shen-yy at cs.sjtu.edu.cn] 
Sent: Thursday, October 27, 2016 4:35 PM
To: all at cs.sjtu.edu.cn
Cc: shen-yy at cs.sjtu.edu.cn
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Speaker: Ce Zhang (ETH) 

Website: https://www.inf.ethz.ch/personal/ce.zhang/

Time: 10:00-11:30, Friday, Oct 28th

Place: SEIEE, Building#3,  Room 404

 

Title: Accessible Data Sciences with Efficient Data Systems

 

Background:

One important problem for the current state of data science is that many of
the techniques needed to unleash the next big thing are available but still
far from accessible. Specifically, the current machine-learning ecosystems
are difficult to use by non-computer science users and they are still far
from achieving the full potential that can be provided by modern hardware.
With more than five ongoing data sciences applications here at ETH Zurich,
ranging from genomics, social sciences, and astronomy, our dream is to
design the next generation of data science ecosystems that are fast,
scalable, and easier to use. In this talk, I will first describe the
abundant opportunities for data sciences at ETH Zurich, and then describe
two enabling techniques that are being developed by my group. The general
direction of these techniques is the co-design of machine learning (or
artificial intelligence) with modern hardware and systems. I will talk about
our recent work that introduced a data structure for dense linear
regression. It can potentially reduce the memory bandwidth by 20x while
training. Then I will introduce a novel database architecture, which makes
the production system of a leading security company 100x faster. It contains
an SMT solver to answer queries that it was not originally designed for. 

 

Bio:

Ce is an Assistant Professor in Computer Science at ETH Zurich. He believes
that by making data¡ªalong with the processing of data¡ªeasily accessible to
non-computer science 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 R¨¦. He finished his PhD by round-tripping
between the University of Wisconsin¨CMadison 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 CACM Research Highlight (2016), "Best
of VLDB" (2015), and Nature magazine (2015). 

 

 

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