October 09, 2013

Divesh Srivastava, AT&T Labs-Research

In Search of Truth

Divesh Srivastava is the head of Database Research at AT&T Labs-Research, where he is responsible for executing the research initiative of AT&T in database management, and directing the efforts of several world-class researchers. He is a Fellow of the ACM (Association for Computing Machinery), on the board of trustees of the VLDB Endowment, and an associate editor of the ACM Transactions on Database Systems (TODS). He has conducted research on a wide variety of topics in database management, has published over 250 technical articles and has over 75 US patents issued.


The Web has enabled the availability of a huge amount of useful information and people have come to rely on it to fulfill their information needs in a variety of domains. We present a study on the accuracy of data and the quality of Web sources in two domains where quality is important to people's lives: Stock and Flight. We observe that, even in these domains, the quality of the data is less than ideal, with sources providing conflicting, out-of-date and incomplete data. Sources also copy and reformat data from other sources, making it difficult to discover the truth. We describe principled techniques to begin solving these problems, and guide us in search of truth on the Web.

October 28, 2013

Jennifer Rexford, Princeton University

Enabling Innovation Inside the Network

Jennifer Rexford is the Gordon Y.S. Wu Professor of Engineering in the Computer Science Department at Princeton University. While working at AT&T Research from 1996 to 2005, she designed network-management techniques that are in daily use in AT&T's backbone network. Jennifer was chair of ACM SIGCOMM from 2003 to 2007, and currently co-chairs the advisory council of NSF's CISE directorate. She is an ACM Fellow and received ACM's Grace Murray Hopper Award for outstanding young computer professional of the year in 2005. Host: Prof. Vishal Misra misra@cs.columbia.edu


Modern computer networks perform a bewildering array of tasks, from routing and traffic monitoring, to access control and server load balancing. Yet, managing these networks is unnecessarily complicated and error-prone, due to a heterogeneous mix of devices (e.g., routers, switches, firewalls, and network-address translators) with closed and proprietary configuration interfaces. The emergence of Software Defined Networking (SDN) is poised to change all this by offering a clean and open interface between networking devices and the software that controls them. In particular, many commercial switches support the OpenFlow protocol, and a number of campus, data-center, and backbone networks have deployed the new technology. Many example SDN applications (e.g., server load balancing, seamless virtual machine migration, traffic engineering, and energy-efficient networking) illustrate SDN's potential to transform future networks. Yet, while SDN makes it possible to program the network, it does not make it easy. Today's OpenFlow controllers offer very low-level APIs that mimic the underlying switch hardware. To reach SDN's full potential, we need to identify the right higher-level abstractions for creating (and composing) powerful applications. In the Frenetic project (www.frenetic-lang.org), we are designing simple and intuitive abstractions for programming SDNs, including ways to query network state, compose application modules, and update a distributed set of switches. These abstractions substantially lower the barrier for innovating inside the network.

This is joint work with the rest of the Frenetic team, including Nate Foster (Cornell), Arjun Guha (UMass-Amherst), Joshua Reich (Princeton), Cole Schlesinger (Princeton), and David Walker (Princeton).

November 11, 2013

Muthu Muthukrishnan, Rutgers and Microsoft Research

Theory and Applications of Data Stream Algorithms

Muthu Muthukrishnan is a Professor of Computer Science at Rutgers University and a Researcher at Microsoft Research. His research focus is on algorithmic problems with applications to a variety of areas. His recent research is on analyzing massive data streams and on economics and optimization problems in online ad systems. Host: Dan Rubenstein


What problems can be solved while making one (or two) passes over a stream of data, and keeping a small memory? We will present an overview of the techniques we have developed to address this question, and show applications to databases, networking, compressed sensing, FFT and others. We will also show connections to privacy and distributed data analysis, and discuss emerging directions. The emphasis will be on the interplay between theory and practice.

April 09, 2014

Vijay Kumar, University of Pennsylvania

Aerial Robot Swarms

Vijay Kumar is the UPS Foundation Professor with appointments in the Departments of Mechanical Engineering and Applied Mechanics , Computer and Information Science <http://www.cis.upenn.edu/>, and Electrical and Systems Engineering <http://www.ese.upenn.edu/>. He is currently on sabbatical leave as the assistant director of robotics and cyber physical systems at the White House Office of Science and Technology Policy<http://www.whitehouse.gov/administration/eop/ostp>. Kumar's group <http://www.asvijaykumar.org/current-members/> works on creating autonomous ground and aerial robots, designing bio-inspired algorithms for collective behaviors, and on robot swarms. They have won many best paper awards at conferences, and group alumni<http://www.kumarrobotics.org/alumni/> are leaders in teaching, research, business and entrepreneurship. Kumar is a fellow of ASME <https://www.asme.org/> and IEEE<http://www.computer.org/portal/web/guest/home> and a member of the National Academy of Engineering <http://www.nae.edu/>. Host: Peter Allen


Autonomous micro aerial robots can operate in three-dimensional, indoor and outdoor environments, with applications to search and rescue, first response and precision farming. I will provide an overview of our work, and describe the challenges in developing small, agile robots and our recent work in the areas of (a) control and planning, (b) state estimation and mapping, and (c) coordinating large teams of robots.

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