COMAD 2016

Sequences, Choices, and their Dynamics
Ravi Kumar

Sequences arise in many online and offline settings: urls to visit, songs to listen to, videos to watch, restaurants to dine at, and so on. User-generated sequences are tightly related to mechanisms of choice, where a user must select one from a finite set of alternatives. In this talk, we will discuss a class of problems arising from studying such sequences and the role discrete choice theory plays in these problems. We will present modeling and algorithmic approaches to some of these problems and illustrate them in the context of large-scale data analysis.

Presenter Bio: Ravi Kumar has been a senior staff research scientist at Google since June 2012. Prior to this, he was a research staff member at the IBM Almaden Research Center and a principal research scientist at Yahoo! Research. He obtained his Ph.D. in Computer Science from Cornell University in 1998. His research interests include Web search and data mining, algorithms for massive data, and the theory of computation.

Distributed Data Streams and the Power of Geometry
Minos Garofalakis

Effective Big Data analytics pose several difficult challenges for modern data management architectures. One key such challenge arises from the naturally streaming nature of big data, which mandates efficient algorithms for querying and analyzing massive, continuous data streams (that is, data that is seen only once and in a fixed order) with limited memory and CPU-time resources. Such streams arise naturally in emerging large-scale event monitoring applications; for instance, network-operations monitoring in large ISPs, where usage information from numerous sites needs to be continuously collected and analyzed for interesting trends. In addition to memory- and time-efficiency concerns, the inherently distributed nature of such applications also raises important communication-efficiency issues, making it critical to carefully optimize the use of the underlying network infrastructure. In this talk, we introduce the distributed data streaming model, and discuss techniques for tracking complex queries over distributed streams that rely on novel insights from convex geometry. We also outline new research directions in this space.

Presenter Bio: Minos Garofalakis received the M.Sc. and Ph.D. degrees in Computer Science from the University of Wisconsin-Madison in 1994 and 1998, respectively. He worked as a Member of Technical Staff at Bell Labs (1998-2005), as a Senior Researcher at Intel Research Berkeley (2005-2007), and as a Principal Research Scientist at Yahoo! Research (2007-2008). In parallel, he also held an Adjunct Associate Professor position at the EECS Department of the University of California, Berkeley (2006-2008). As of October 2008, he is a Professor of Computer Science at the School of Electronic and

Computer Engineering of the Technical University of Crete, and the Director of the Software Technology and Network Applications Laboratory (SoftNet). Prof. Garofalakis’ research focuses on Big Data analytics, spanning areas such as database systems, data streams, data synopses and approximate query processing, probabilistic databases, and data mining. His work has resulted in over 140 published scientific papers in these areas, and 36 US Patent filings (29 patents issued) for companies such as Lucent, Yahoo!, and AT&T. GoogleScholar gives over 10,000 citations to his work, and an h-index value of 55. Prof. Garofalakis is an ACM Distinguished Scientist (2011), a Senior Member of the IEEE, and a recipient of the IEEE ICDE Best Paper Award (2009), the Bell Labs President’s Gold Award (2004), and the Bell Labs Teamwork Award (2003).

Recommendations in the context of a Social Network
Deepak Agarwal

Recommender systems that arise in the context of social networks have characteristics that give rise to new technical challenges. I will provide an overview and discuss using two examples from LinkedIn -- a) People recommendations and b) Feed optimization. The talk would focus both on scientific methodologies and engineering challenges that are necessary to deploy and maintain such systems in a large scale industrial environment like LinkedIn.

Presenter Bio: Deepak Agarwal leads the relevance and machine learning team at LinkedIn, which is responsible for optimizing and personalizing user experience across all consumer and enterprise products. Prior to that, he was a Principal Research scientist at Yahoo! research, where his work on optimizing content on Yahoo! front page won him the Yahoo! super star award. He is the Fellow of the American Statistical Association and serves on the board of SIGKDD. He is an associate editor for two top-tier statistical journals and regularly serves on the program committee of major data mining and machine

learning conferences. He was a program co-chair of 2012 ACM SIGKDD conference. Most recently, he has co-authored a book on Statistical Methods for Recommender Systems, published by Cambridge university press.

Being Smart: The Role of Timely Analytics
Krithi Ramamritham

These days, unless something has the epithet "smart" attached to it, it is nothing. Smart Energy solutions promise cleaner, cheaper and more reliable energy. Smart Cities promise better quality of life for its citizens. We will argue that for a "system" to be SMART, it should Sense Meaningfully, Analyze and Respond Timely. Using real-world examples from the domains of Smart Energy and Smart Cities, this talk will illustrate the central role of data in being SMART.

Presenter Bio: Krithi Ramamritham is professor at Dept of Computer Science and Engineering at IIT Bombay. His research explores timeliness and consistency issues in computer systems, in particular, databases, real-time systems, and distributed applications. His recent work addresses these issues in the context of Dynamic Data in sensor networks, embedded systems, mobile environments and the web. His recent work has been related to the use of Information and Communication Technologies for creating tools aimed at socio-economic development. He obtained Ph.D. in Computer Science from the University

of Utah in 1981 after his B.Tech. in Electrical Engineering (1976) and M.Tech. in Computer Science (1978), both from the Indian Institute of Technology Madras.