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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span style='color:#44546A'>Please go to this talk if you are interested. All grad students at ADAPT are required to go to this talk.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#44546A'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#44546A'>Kenny<o:p></o:p></span></p><p class=MsoNormal><span style='color:#44546A'><o:p> </o:p></span></p><div><div style='border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0in 0in 0in'><p class=MsoNormal><b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> Yanyan Shen [mailto:shen-yy@cs.sjtu.edu.cn] <br><b>Sent:</b> Thursday, October 27, 2016 4:35 PM<br><b>To:</b> all@cs.sjtu.edu.cn<br><b>Cc:</b> shen-yy@cs.sjtu.edu.cn<br><b>Subject:</b> </span><span lang=ZH-CN style='font-size:10.0pt;font-family:宋体'>周五（明天上午</span><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>10</span><span lang=ZH-CN style='font-size:10.0pt;font-family:宋体'>点）学术报告</span><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'><o:p></o:p></span></p></div></div><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><span lang=ZH-CN style='font-size:14.0pt;font-family:宋体'>各位老师好，</span><span style='font-size:14.0pt'><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:14.0pt'><o:p> </o:p></span></p><p class=MsoPlainText><span lang=ZH-CN style='font-size:14.0pt;font-family:宋体'>明天（</span><span style='font-size:14.0pt'>10</span><span lang=ZH-CN style='font-size:14.0pt;font-family:宋体'>月</span><span style='font-size:14.0pt'>28</span><span lang=ZH-CN style='font-size:14.0pt;font-family:宋体'>日）上午</span><span style='font-size:14.0pt'>10</span><span lang=ZH-CN style='font-size:14.0pt;font-family:宋体'>点的学术报告，欢迎大家前来聆听指导。</span><span style='font-size:14.0pt'><o:p></o:p></span></p><p class=MsoPlainText><span lang=ZH-CN style='font-size:14.0pt;font-family:宋体'>具体内容如下：</span><span style='font-size:14.0pt'><o:p></o:p></span></p><p class=MsoPlainText><span style='font-size:12.0pt'><o:p> </o:p></span></p><p class=MsoPlainText><b><span style='font-size:16.0pt'>Speaker: Ce Zhang (ETH) <o:p></o:p></span></b></p><p class=MsoPlainText><b><span style='font-size:16.0pt'>Website: </span></b><span style='font-size:16.0pt'><a href="https://www.inf.ethz.ch/personal/ce.zhang/">https://www.inf.ethz.ch/personal/ce.zhang/</a><o:p></o:p></span></p><p class=MsoPlainText><b><span style='font-size:16.0pt'>Time: 10:00-11:30, Friday, Oct 28th<o:p></o:p></span></b></p><p class=MsoPlainText><b><span style='font-size:16.0pt'>Place: SEIEE, Building#3, Room 404<o:p></o:p></span></b></p><p class=MsoPlainText><b><span style='font-size:16.0pt'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span style='font-size:16.0pt'>Title: Accessible Data Sciences with Efficient Data Systems</span></b><span style='font-size:12.0pt'><o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><span style='font-size:16.0pt'>Background:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:16.0pt'>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. <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:16.0pt'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:16.0pt'>Bio:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:16.0pt'>Ce is an Assistant Professor in Computer Science at ETH Zurich. He believes that by making data—along with the processing of data—easily 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 Ré. He finished his PhD by 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 CACM Research Highlight (2016), "Best of VLDB" (2015), and Nature magazine (2015).</span><span style='font-size:16.0pt;font-family:"Times New Roman","serif"'> <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:16.0pt'><o:p> </o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>