- talks - lists.cs.princeton.edu

Colloquium Speaker Catherine McGeoch Tues Oct 20- 12:30pm
by Nicole E. Wagenblast 19 Oct '15

19 Oct '15

Colloquium Speaker Catherine McGeoch, Amherst College Benchmarking the D-Wave 2X: Challenges and Early Results Tuesday, October 20- 12:30pm Computer Science 105 Benchmarking the D-Wave 2X: Challenges and Early Results A D-Wave platform implements a quantum annealing algorithm in hardware, to solve an NP-hard problem known as Ising Model Optimization (also called Quadratic Unconstrained Boolean Optimization). The ``hardware'' is a processor chip containing qubits that exploit quantum properties such as superposition and entanglement to carry out the computation. This is a heuristic algorithm that belongs to the adiabatic quantum model of computation, an alternative to the more familiar quantum gate model of computation. The task of performance assessment for these novel platforms -- comparing classical heuristics implemented in software to a quantum analog heuristic implemented in hardware -- gives rise to a number of new methodological issues, on top of the usual challenges relating to evaluation of heuristics for NP-hard problems. I will discuss some of these issues and present some early performance results for the D-Wave 2X, a 1000-qubit processor launched in summer 2015.

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Fwd: Special Seminar: GoogleCar Team's Josh Herbach'08
by Jennifer Rexford 19 Oct '15

19 Oct '15

> From: Alain Kornhauser <alaink(a)princeton.edu> > Subject: Special Seminar: GoogleCar Team's Josh Herbach'08 > Date: October 18, 2015 at 10:45:35 PM EDT > To: orf467_f2015(a)princeton.edu > > Wednesday, Oct 21 1:30->2:50 > >

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Colloquium Speaker Catherine McGeoch Tues Oct 20- 12:30pm
by Nicole E. Wagenblast 15 Oct '15

15 Oct '15

Colloquium Speaker Catherine McGeoch, Amherst College Benchmarking the D-Wave 2X: Challenges and Early Results Tuesday, October 20- 12:30pm Computer Science 105 Benchmarking the D-Wave 2X: Challenges and Early Results A D-Wave platform implements a quantum annealing algorithm in hardware, to solve an NP-hard problem known as Ising Model Optimization (also called Quadratic Unconstrained Boolean Optimization). The ``hardware'' is a processor chip containing qubits that exploit quantum properties such as superposition and entanglement to carry out the computation. This is a heuristic algorithm that belongs to the adiabatic quantum model of computation, an alternative to the more familiar quantum gate model of computation. The task of performance assessment for these novel platforms -- comparing classical heuristics implemented in software to a quantum analog heuristic implemented in hardware -- gives rise to a number of new methodological issues, on top of the usual challenges relating to evaluation of heuristics for NP-hard problems. I will discuss some of these issues and present some early performance results for the D-Wave 2X, a 1000-qubit processor launched in summer 2015.

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Colloquium Speaker Jeffrey Siskind Today, 12:30pm
by Nicole E. Wagenblast 15 Oct '15

15 Oct '15

Colloquium Speaker Jeffrey Siskind, from Purdue University Thursday, October 15, 12:30pm Computer Science 105 Seeing, Saying, Doing, and Learning: Integrating Computer Vision, Natural Language Processing, Robotics, and Machine Learning Through Multidirectional Inference The semantics of natural language can be grounded in perception and motor control with a unified cost function that supports multidirectional inference. I will present several instances of this approach. The first is a cost function relating sentences, video, and a lexicon. Performing inference from video and a lexicon to sentences allows it to generate sentential descriptions of video. Performing inference from sentences and a lexicon to video allows it to search a video database for clips that match a sentential query. Performing inference from sentences and video to a lexicon allows it to learn a lexicon. The second is the functional inverse of video captioning. Instead of mapping video and object detections to sentences, one can map video and sentences to object detections. This allows one to use sentential constraint on a video object codetection process to find objects without pretrained object detectors. The third is a cost function relating sentences, robotic navigation paths, and a lexicon. Performing inference from sentences and navigation paths to a lexicon allows it to learn a lexicon. Performing inference from navigation paths and a learned lexicon to sentences allows it to generate sentential descriptions of paths driven by a mobile robot. Performing inference from sentences and a learned lexicon to navigation paths allows it to plan and drive navigation paths that satisfy a sentential navigation request. Finally, one can perform object codetection on the video stream from a robot-mounted camera during navigation to satisfy sentential requests and use the collection of constraints from vision, language, and robotics to detect, localize, and label objects in the environment without any pretrained object detectors. Joint work with Andrei Barbu, Daniel Paul Barrett, Scott Alan Bronikowski, N. Siddharth, and Haonan Yu. Jeffrey M. Siskind received the B.A. degree in computer science from the Technion, Israel Institute of Technology, Haifa, in 1979, the S.M. degree in computer science from the Massachusetts Institute of Technology (M.I.T.), Cambridge, in 1989, and the Ph.D. degree in computer science from M.I.T. in 1992. He did a postdoctoral fellowship at the University of Pennsylvania Institute for Research in Cognitive Science from 1992 to 1993. He was an assistant professor at the University of Toronto Department of Computer Science from 1993 to 1995, a senior lecturer at the Technion Department of Electrical Engineering in 1996, a visiting assistant professor at the University of Vermont Department of Computer Science and Electrical Engineering from 1996 to 1997, and a research scientist at NEC Research Institute, Inc. from 1997 to 2001. He joined the Purdue University School of Electrical and Computer Engineering in 2002 where he is currently an associate professor. His research interests include computer vision, robotics, artificial intelligence, neuroscience, cognitive science, computational linguistics, child language acquisition, automatic differentiation, and programming languages and compilers.

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Speaker- Suman Banerjee Tues Oct 20- 3pm
by Nicole E. Wagenblast 14 Oct '15

14 Oct '15

Suman Banerjee, University of Wisconsin, Madison Tuesday, October 20- 3pm Computer Science 402 ParaDrop: Enabling Third Party Apps in Home WiFi Gateways for Fun and Profit Over the last few years, we have been building a new home WiFi router platform that can allow 3rd party applications and services to be deployed on it. The system, called Paradrop, is all in software and is an extreme form of the popular notion known as edge computing. It leverages popular concepts of virtualization and software-defined networking. It also integrates techniques for better monitoring, management, and mitigation of RF interference experienced in these environments. It hopes to be to the home router market what the smartphones were to the cellphone market. We believe that ParaDrop can be a significant enabler for the emerging services for smarter homes that take advantage of Internet of Things applications. Furthermore, the platform can also be leveraged for delivering a more hands-on educational content on emerging topics, such as Internet-of-Things, mobile development, mobile and wireless systems design, and even basic computer networking. In this talk, I will describe some of the technical developments in creating this platform and outline our ongoing activities. Suman Banerjee is an Professor in Computer Sciences at UW-Madison where he is the founding director of the WiNGS laboratory which broadly focuses on research in wireless and mobile networking systems. He received his undergraduate degree from IIT Kanpur, and MS and PhD degrees from the University of Maryland. He is the inaugural recipient of the ACM SIGMOBILE Rockstar award and a recipient of the NSF Career Award. He is a recipient of multiple award papers at various conferences, such as ACM MobiCom, ACM CoNEXT, and IEEE Dyspan. Further, technology developed by Prof. Banerjee have won various accolades including the first prize at the Wisconsin Governor's Business Plan Competition in 2011 and in the Interdigital Innovation Challenge in 2012. He is currently serving as the chair of ACM SIGMOBILE.

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Fwd: EE Seminar - Thursday, October 15, 4:30pm, EQuad B205 - Bhaskar Krishnamachari-Bandits and Newsvendors: Joint Online Learning and Optimization in Wireless Networks
by Jennifer Rexford 14 Oct '15

14 Oct '15

> > DEPARTMENT OF > ELECTRICAL ENGINEERING SEMINAR SERIES > > > > Speaker: Bhaskar Krishnamachari, University of Southern California > Title: Bandits and Newsvendors: Joint Online Learning and Optimization in Wireless Networks > Date: Thursday, October 15, 2015 > Time: 4:30pm > Room: E-Quad, B205 > Host: Mung Chiang > > > Abstract: Algorithms for online learning and decision-making under uncertainty have become popular in recent years to improve the performance of wireless networks in unknown dynamic environments. I will give a brief overview of certain classic problem formulations such as multi-armed bandits (MAB) and newsvendor problems, talk about their applications to wireless networking, and present some recent results from my group's research in this area. These include results for decentralized MAB, combinatorial MAB, contextual MAB, multi-period newsvendors, and optimized robotic network formation in unknown environments. This talk will cover joint work with students Dr. Yi Gai, Pranav Sarkar, Parisa Mansourifard, and Shangxing Wang, and faculty collaborators Rahul Jain, Tara Javidi, and Nora Ayanian. > > > Bio: Bhaskar Krishnamachari is an Associate Professor and Ming Hsieh Faculty Fellow in Electrical Engineering, and Director of the Autonomous Networks Research Group (http://anrg.usc.edu/ <http://anrg.usc.edu/>) at the University of Southern California's Viterbi School of Engineering. He works on the design and analysis of algorithms and protocols for next generation wireless networks. His co-authored papers have received best paper awards at IPSN (2004, 2010), MSWiM (2006) and MobiCom (2010), a best paper runner-up at SECON (2012), and a top-three paper at MSWiM (2014). He has received the NSF CAREER award (2004), the ASEE Terman Award (2010), and has been included on Technology Review Magazine's TR-35 list (2011), and Popular Science's Brilliant 10 list (2015). He has authored a book titled "Networking Wireless Sensors" published by Cambridge University Press. He is an Editor for the ACM Transactions on Sensor Networks, was the TPC Co-Chair for IPSN 2015, and is a TPC Co-Chair for WiOpt 2016.

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Colloquium Speaker Jeffrey Siskind Thurs Oct 15, 12:30pm
by Nicole E. Wagenblast 14 Oct '15

14 Oct '15

Colloquium Speaker Jeffrey Siskind, from Purdue University Thursday, October 15, 12:30pm Computer Science 105 Seeing, Saying, Doing, and Learning: Integrating Computer Vision, Natural Language Processing, Robotics, and Machine Learning Through Multidirectional Inference The semantics of natural language can be grounded in perception and motor control with a unified cost function that supports multidirectional inference. I will present several instances of this approach. The first is a cost function relating sentences, video, and a lexicon. Performing inference from video and a lexicon to sentences allows it to generate sentential descriptions of video. Performing inference from sentences and a lexicon to video allows it to search a video database for clips that match a sentential query. Performing inference from sentences and video to a lexicon allows it to learn a lexicon. The second is the functional inverse of video captioning. Instead of mapping video and object detections to sentences, one can map video and sentences to object detections. This allows one to use sentential constraint on a video object codetection process to find objects without pretrained object detectors. The third is a cost function relating sentences, robotic navigation paths, and a lexicon. Performing inference from sentences and navigation paths to a lexicon allows it to learn a lexicon. Performing inference from navigation paths and a learned lexicon to sentences allows it to generate sentential descriptions of paths driven by a mobile robot. Performing inference from sentences and a learned lexicon to navigation paths allows it to plan and drive navigation paths that satisfy a sentential navigation request. Finally, one can perform object codetection on the video stream from a robot-mounted camera during navigation to satisfy sentential requests and use the collection of constraints from vision, language, and robotics to detect, localize, and label objects in the environment without any pretrained object detectors. Joint work with Andrei Barbu, Daniel Paul Barrett, Scott Alan Bronikowski, N. Siddharth, and Haonan Yu. Jeffrey M. Siskind received the B.A. degree in computer science from the Technion, Israel Institute of Technology, Haifa, in 1979, the S.M. degree in computer science from the Massachusetts Institute of Technology (M.I.T.), Cambridge, in 1989, and the Ph.D. degree in computer science from M.I.T. in 1992. He did a postdoctoral fellowship at the University of Pennsylvania Institute for Research in Cognitive Science from 1992 to 1993. He was an assistant professor at the University of Toronto Department of Computer Science from 1993 to 1995, a senior lecturer at the Technion Department of Electrical Engineering in 1996, a visiting assistant professor at the University of Vermont Department of Computer Science and Electrical Engineering from 1996 to 1997, and a research scientist at NEC Research Institute, Inc. from 1997 to 2001. He joined the Purdue University School of Electrical and Computer Engineering in 2002 where he is currently an associate professor. His research interests include computer vision, robotics, artificial intelligence, neuroscience, cognitive science, computational linguistics, child language acquisition, automatic differentiation, and programming languages and compilers.

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Colloquium Speaker: Shie Mannor today- 12:30pm
by Nicole E. Wagenblast 13 Oct '15

13 Oct '15

Colloquium Speaker Shie Mannor, Technion, Israel Institute of Technology Monday, October 12, 12:30pm Computer Science 105 Co-hosted by the Computer Science Department and the Center for Statistics and Machine Learning Actionable big data: from data to decisions and back In many sequential decision problems all that we have is a record of historical trajectories. Building dynamic models from these trajectories and ultimately sequential decision policies may result in much uncertainty and bias. In this talk we consider the question of how to create control policies from existing historical data and how to better sample trajectories so that future control policies would be better. This question has been central in reinforcement learning in the last decade if not more, and involves methods from statistics, machine learning, optimization, and control theory. I will start my talk with demonstrating why planning with parameter uncertainty is an important issue. I will then describe several approaches: Bayesian uncertainty model over the unknown parameters, a robust approach that takes a worst case view, and a frequentist approach. I will then discuss the challenges that are posed when the model itself rather than just the parameters may not be fully known. I will then describe two challenging real-world domains that have been studied in my research group in collaboration with experts from industry and academia: diabetes care management in healthcare and asset management in high-voltage transmission grids. For each domain I will describe our efforts to reduce the problem to its bare essentials as a reinforcement learning problem, the algorithms for learning the control policies, and some of the lessons we learned. I graduated from the Technion with a BSc in Electrical Engineering and BA in mathematics (both summa cum laude) in 1996. After that I spent almost four years as an intelligence officer with the Israeli Defense Forces. I was subsequently involved in a few ventures in the high-tech industry. I earned my PhD in Electrical Engineering from the Technion at 2002, under the supervision of Nahum Shimkin. I was then a Fulbright postdoctoral associate with LIDS (MIT) working with John Tsitsiklis for two years. I was at the Department of Electrical and Computer Engineering in McGill University from July 2004 until August 2010, where I held a Canada Research Chair in Machine Learning from 2005 to 2009. I have been with the Department of Electrical Engineering at the Technion since 2008 where I am a professor. I am married to Tally and father to Liam, Oliver and Dylan.

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Colloquium Speaker Kyle Jamieson, Mon April 13, 12:30pm
by Nicole E. Wagenblast 13 Oct '15

13 Oct '15

Colloquium Speaker Kyle Jamieson, University College, London Monday, April 13, 12:30pm Computer Science 105 Leveraging Array Signal Processing in the Wireless Internet of Things Phased array signal processing has long been employed outdoors in radar, underwater in sonar, and underground in seismic monitoring. Today, it has the potential to revolutionize the Internet of Things (IoT) by giving us the ability to track every one of the billions of IoT devices indoors, and meet their exploding bandwidth requirements. But to make the shift to indoor wireless networks, it must cope with strong multipath radio reflections, packetized data transmissions, and commodity hardware platforms. In this talk I will describe two relevant systems through the lens of system-building and experimentation. First, I will describe an indoor location system that uses solely the existing Wi-Fi infrastructure to achieve a median location accuracy of 23 centimeters, and sub-second response time, allowing Wi-Fi-enabled devices to be tracked in real-time. Next, I will present a MIMO-based Sphere Decoder system that leverages novel search algorithms and geometric reasoning to increase wireless throughput, the first of its kind that scales to 256-QAM constellation densities with computational demands that are practically realizable in current ASIC hardware. Finally, I will conclude with a vision of how these techniques will support exciting IoT applications such as video analytics over billions of indoor wireless cameras. Kyle Jamieson is a Senior Lecturer (U.S. equivalent: Assistant Professor) in the Department of Computer Science at University College London (UCL). His research interests are in building wirelessly networked systems for the real world that cut across the boundaries of digital communications and networking. Prior to joining UCL, he received the B.S., M.Eng., and Ph.D. (2008) degrees in Computer Science from the Massachusetts Institute of Technology. He then received a Starting Investigator fellowship from the European Research Council in 2011, Best Paper awards at USENIX 2013 and CoNEXT 2014, and a Google Faculty Research Award in 2015. He regularly serves on the program committees of the ACM MobiCom, USENIX NSDI, and ACM SIGCOMM conferences.

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Colloquium Speaker Kyle Jamieson, Mon April 13, 12:30pm
by Nicole E. Wagenblast 13 Oct '15

13 Oct '15

Colloquium Speaker Kyle Jamieson, University College, London Monday, April 13, 12:30pm Computer Science 105 Leveraging Array Signal Processing in the Wireless Internet of Things Phased array signal processing has long been employed outdoors in radar, underwater in sonar, and underground in seismic monitoring. Today, it has the potential to revolutionize the Internet of Things (IoT) by giving us the ability to track every one of the billions of IoT devices indoors, and meet their exploding bandwidth requirements. But to make the shift to indoor wireless networks, it must cope with strong multipath radio reflections, packetized data transmissions, and commodity hardware platforms. In this talk I will describe two relevant systems through the lens of system-building and experimentation. First, I will describe an indoor location system that uses solely the existing Wi-Fi infrastructure to achieve a median location accuracy of 23 centimeters, and sub-second response time, allowing Wi-Fi-enabled devices to be tracked in real-time. Next, I will present a MIMO-based Sphere Decoder system that leverages novel search algorithms and geometric reasoning to increase wireless throughput, the first of its kind that scales to 256-QAM constellation densities with computational demands that are practically realizable in current ASIC hardware. Finally, I will conclude with a vision of how these techniques will support exciting IoT applications such as video analytics over billions of indoor wireless cameras. Kyle Jamieson is a Senior Lecturer (U.S. equivalent: Assistant Professor) in the Department of Computer Science at University College London (UCL). His research interests are in building wirelessly networked systems for the real world that cut across the boundaries of digital communications and networking. Prior to joining UCL, he received the B.S., M.Eng., and Ph.D. (2008) degrees in Computer Science from the Massachusetts Institute of Technology. He then received a Starting Investigator fellowship from the European Research Council in 2011, Best Paper awards at USENIX 2013 and CoNEXT 2014, and a Google Faculty Research Award in 2015. He regularly serves on the program committees of the ACM MobiCom, USENIX NSDI, and ACM SIGCOMM conferences.

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