<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class=""><div class=""><span class="" style="font-size: 14px;"><b class="" style="font-family: LucidaGrande; background-color: rgba(255, 255, 255, 0);"><u class="">Department of Computer Science </u></b><b class="" style="background-color: rgba(255, 255, 255, 0); font-family: LucidaGrande;"><u class="">Seminar</u></b></span></div></div><div class="" style="font-family: LucidaGrande;"><span class="" style="background-color: rgba(255, 255, 255, 0);"><br class=""></span></div><div class=""><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Monday, March 27, 2017</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">2:30 pm</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Ryerson 251 </span></div><div class="" style="font-family: LucidaGrande;"><br class=""></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Shivaram Venkataraman</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">(University of California, Berkeley)</span></div><div class="" style="font-family: LucidaGrande;"><br class=""></div><div class=""><span class="" style="font-size: 14px;"><span class="" style="font-family: LucidaGrande;">Title: </span><span class="" style="font-family: LucidaGrande;">Scalable Systems for Fast and Easy Machine Learning</span><br class="gmail-m_4827095062818533991gmail_msg"><br class="gmail-m_4827095062818533991gmail_msg"><span class="" style="font-family: LucidaGrande;">Abstract: </span></span></div><div class=""><font face="LucidaGrande" class=""><span class="" style="font-size: 14px;">Machine learning models trained on massive datasets power a number of applications; from machine translation to detecting supernovae in astrophysics. However the end of Moore’s law and the shift towards distributed computing architectures presents many new challenges for building and executing such applications in a scalable fashion. <br class=""><br class="">In this talk I will present my research on systems that make it easier to develop new machine learning applications and scale them while achieving high performance. I will first present programming models that let users easily build distributed machine learning applications. Next, I will show how we can exploit the structure of machine learning workloads to build low-overhead performance models that can help users understand scalability and simplify large scale deployments. Finally, I will describe scheduling techniques that can improve scalability and achieve high performance when using distributed data processing frameworks.<br class=""><br class=""></span></font><span class="" style="font-size: 14px; font-family: LucidaGrande;">-----</span></div><div class=""><font face="LucidaGrande" class=""><span class=""><span class="" style="font-size: 14px;"> </span><span class="" style="font-size: 14px;"><br class=""></span></span></font><span class="" style="font-size: 14px;"><span class="" style="font-family: 'Lucida Grande';">Bio: </span></span></div><div class=""><div class=""><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Shivaram Venkataraman is a PhD Candidate at the University of California, Berkeley and is advised by Mike Franklin and Ion Stoica. His research interests are in designing systems and algorithms for large scale data processing and machine-learning. He is a recipient of the Siebel Scholarship and best-of-conference citations at VLDB and KDD. Before coming to Berkeley, he completed his M.S at the University of Illinois, Urbana-Champaign.</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;"><br class=""></span></div></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Host: Shan Lu</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;"><br class=""></span></div><div class="" style="font-family: LucidaGrande;"><font size="2" class="">Refreshments in Ry. 255 after the talk</font></div></div></div></body></html>