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Title: Scalable Algorithms in the Age of Big Data, Network Sciences, and Machine Learning
Shang-Hua Teng University Professor and Seeley G. Mudd Professor of Computer Science and Mathematics Viterbi School of Engineering University of Southern California time (9:30-10:30 7 August 2018)
Abstract:
In the age of network sciences and machine learning, efficient algorithms are now in higher demand more than ever before. Big Data fundamentally challenges the classical notion of efficient algorithms: Algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. Using several basic tasks in network analysis, social influence modeling, machine learning, and optimization as examples - in this talk - I will highlight a family of fundamental algorithmic techniques for designing provably-good scalable algorithms.
About the Speaker:
 Shang-Hua Teng is the University Professor and Seeley G. Mudd Professor of Computer Science and Mathematics at University of Southern California. He has twice won the prestigious Godel Prize in theoretical computer science, first in 2008, for developing the theory of smoothed analysis , and then in 2015, for designing the groundbreaking nearly-linear time Laplacian solver for network systems. Citing him as, "one of the most original theoretical computer scientists in the world", the Simons Foundation named Teng a 2014 Simons Investigator, for pursuing long-term curiosity-driven fundamental research. Prior to joining USC in 2009, he was a professor at Boston University. He has also taught at MIT, the University of Minnesota, and the University of Illinois at Urbana-Champaign. He has worked at Xerox PARC, NASA Ames Research Center, Intel Corporation, IBM Almaden Research Center, Akamai Technologies, Microsoft Research Redmond, Microsoft Research New England and Microsoft Research Asia. Teng is a Fellow of the Association for Computing Machinery (ACM), as well as an Alfred P. Sloan fellow.
Title: Reliability and efficiency problems in cloud and data-processing systems
Shan Lu Associate Professor in the Department of Computer Science at the University of Chicago University of Southern California time (9:30-10:30 8 August 2018)
Abstract:
Cloud and data-processing systems are pervasive nowadays. High reliability and efficiency of these systems are crucial and yet difficult to achieve. This talk will discuss a variety of unique reliability and efficiency problems in programming cloud infrastructure and data-processing software systems. We will first look at empirical studies that help understand these real-world problems, and then discuss techniques that help tackle these problems.
About the Speaker:
 Shan Lu is an Associate Professor in the Department of Computer Science at the University of Chicago. She received her Ph.D. at University of Illinois, Urbana-Champaign, in 2008. She was the Clare Boothe Luce Assistant Professor of Computer Sciences at University of Wisconsin, Madison, from 2009 to 2014. Her research focuses on software reliability and efficiency, particularly detecting, diagnosing, and fixing functional and performance bugs in large software systems. Shan has won Alfred P. Sloan Research Fellow in 2014, Distinguished Alumni Educator Award from Department of Computer Science at University of Illinois in 2013, and NSF Career Award in 2010. Her co-authored papers won Google Scholar Classic Paper 2017, Best Paper Awards at USENIX OSDI 2016 and USENIX FAST 2013, ACM-SIGSOFT Distinguished Paper Awards at ICSE 2015 and FSE 2014, an ACM-SIGPLAN Research Highlight Award at PLDI 2011, and an IEEE Micro Top Picks in ASPLOS 2006. Shan is also a member of the informal ASPLOS Hall of Fame. Shan currently serves as the Vice Chair of ACM-SIGOPS (2015--) and the Associate Editor for IEEE Computer Architecture Letters. She also served as the technical program co-chair for USENIX Annual Technical Conference in 2015 and ACM Asia-Pacific Systems Workshop in 2018.
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