- talks - lists.cs.princeton.edu

Mojgan Ghasemi will present her Pre FPO on Tuesday, April 25th, 3:30-5:30pm in J401, Engineering Quadrangle
by Nicki Gotsis 20 Apr '17

20 Apr '17

Mojgan Ghasemi will present her Pre FPO on Tuesday April 25th, 3:30-5:30pm in J401, Engineering Quadrangle. The members of her committee are Jennifer Rexford (Advisor, reader), Theophilus Benson (reader - Duke), Mung Chiang (reader), Nick Feamster (non-reader), Prateek Mittal (non-reader). Everyone is invited to attend her talk. The talk title and abstract follow below: Title: Data-Driven Management of CDN Performance Abstract: Content Distribution Networks (CDNs) carry most of the web content, with the goal of offering good performance to users while keeping the cost low. In this talk, we introduce measurement and analysis techniques to help CDNs balance these goals. First, we allocate resources across the distributed edge servers to jointly minimize network latency and cache miss rates, and evaluate our methods using server request logs from a commercial CDN. Second, we introduced fine-grained measurement of the end-to-end video delivery path to detect and diagnose performance problems for a commercial content provider.Finally, we dive deeper into diagnosing network problems by monitoring TCP connections directly in the network devices.

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MAE Talk: Maja Mataric, Socially Assistive Robotics: Creating Robots That Care, Friday, April 21- 3:30pm
by Nicole E. Wagenblast 18 Apr '17

18 Apr '17

Mechanical and Aerospace Engineering Talk Socially Assistive Robotics: Creating Robots That Care Maja Mataric, University of Southern California Friday, April 21, 2017 - 3:30pm Maeder Hall, ACEE Socially assistive robotics (SAR) is a new field of intelligent robotics that focuses on developing machines capable of assisting users through social rather than physical interaction. The robot’s physical embodiment is at the heart of SAR’s effectiveness, as it leverages the inherently human tendency to engage with lifelike (but not necessarily human-like or otherwise biomimetic) social behavior. People readily ascribe intention, personality, and emotion to robots; SAR leverages this engagement stemming from non-contact social interaction involving speech, gesture, movement demonstration and imitation, and encouragement, to develop robots capable of monitoring, motivating, and sustaining user activities and improving human learning, training, performance and health outcomes. Human-robot interaction (HRI) for SAR is a growing multifaceted research area at the intersection of engineering, health sciences, neuroscience, social, and cognitive sciences. This talk will describe our research into embodiment, modeling and steering social dynamics, and long-term user adaptation for SAR. The research will be grounded in projects involving analysis of multi-modal activity data, modeling personality and engagement, formalizing social use of space and non-verbal communication, and personalizing the interaction with the user over a period of months, among others. The presented methods and algorithms will be validated on implemented SAR systems evaluated by human subject cohorts from a variety of user populations, including stroke patients, children with autism spectrum disorder, and elderly with Alzheimers and other forms of dementia. Maja Mataric´ is the Chan Soon-Shiong Professor of Computer Science, Neuroscience, and Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (rasc.usc.edu) and her Interaction Lab (robotics.usc.edu/interaction). She received a PhD and MS from MIT and BS from the University of Kansas. She is Fellow of AAAS, IEEE, and AAAI, and recipient of the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring, Anita Borg Institute Women of Vision Award in Innovation, NSF Career, MIT TR35 Innovation, and IEEE RAS Early Career Awards, among others. A founder of the rapidly growing field of socially assistive robotics, her research is focused on enabling robots to help through non physical means: through motivation, companionship and coaching. Her work has been validated in therapy, rehabilitation, training, and education for children with autism, stroke survivors, Alzheimer's patients and other special needs populations. She is passionate about conveying the importance of engineering research and careers in STEM to K-12 students and teachers, women and other underrepresented groups in engineering, the media, and policy makers.

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Colloquium Speaker: Rashmi Vinayak, Today- 4:30pm
by Nicole E. Wagenblast 13 Apr '17

13 Apr '17

Colloquium Speaker Departments of Computer Science and Electrical Engineering Rashmi Vinayak, University of California, Berkeley Thursday, April 13, 2017- 4:30pm Computer Science 105 Host: Prof. Kyle Jamieson (CS) and Prof. Naveen Verma (EE) Smart redundancy for big-data systems: Theory and Practice Large-scale distributed storage and caching systems form the foundation of big-data systems. A key scalability challenge in distributed storage systems is achieving fault tolerance in a resource-efficient manner. Towards addressing this challenge, erasure codes provide a storage-efficient alternative to the traditional approach of data replication. However, classical erasure codes come with critical drawbacks: while optimal in utilizing storage space, they significantly increase the usage of other important cluster resources such as network and I/O. In the first part of the talk, I present new erasure codes and theoretical optimality guarantees. The proposed codes reduce the network and I/O usage by 35-70% for typical parameters while retaining the storage efficiency of classical codes. I then present an erasure-coded storage system that employs the proposed codes, and demonstrate significant benefits over the state-of-the-art in evaluations under production setting at Facebo ok. Our codes have been integrated into Apache Hadoop 3.0. The second part of the talk focuses on achieving high performance in distributed caching systems. These systems routinely face the challenges of skew in data popularity, background traffic imbalance, and server failures, which result in load imbalance across servers and degradation in read latencies. I present EC-Cache, a cluster cache that employs erasure coding to achieve a 3-5x improvement as compared to the state-of-the-art. Rashmi K. Vinayak received her PhD in the EECS department at UC Berkeley in 2016, where she is now a postdoctoral researcher at AMPLab/RISELab and BLISS working with Ion Stoica and Kannan Ramchandran. Her dissertation received the Eli Jury Award 2016 from the EECS department at UC Berkeley for outstanding achievement in the area of systems, communications, control, or signal processing. Rashmi is the recipient of the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. She is also a recipient of the Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16. Her research interests lie in the theoretical and system challenges that arise in storage and analysis of big data.

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Friday: Quantitative Social Science Colloquium on Internet Censorship Measurement
by Nick Feamster 12 Apr '17

12 Apr '17

All, I am giving a talk in the Quantitative Social Science (QSS) seminar on our Internet censorship measurement work over the past 4+ years. The talk is on Friday at 12p in Corwin Hall: http://q-aps.princeton.edu/book/QSS-seminar Come to the talk to learn about how we have managed to get continuous measurements of Internet censorship from 170+ countries around the world. -Nick > Begin forwarded message: > > ————— > > Global Measurements of Internet Censorship > Nick Feamster, Princeton University > > Internet users in many countries around the world are subject to various forms of censorship and information control. Despite its widespread nature, however, measuring Internet censorship on a global scale has remained an elusive goal. Internet censorship is known to vary across time and within regions (and Internet Service Providers) within a country. To capture these complex dynamics, Internet censorship must be both continuous and distributed across a large number of vantage points. To date, gathering such information has required recruiting volunteers to perform measurements from within countries of interest; this approach does not permit collection of continuous measurements, and it also does not permit collection from a large number of measurement locations; it may also put the people performing the measurements at risk. Over the past four years, we have developed a collection of measurement techniques to surmount the limitations of these conventional approaches. In this talk, I will describe three such techniques: (1) Encore, a tool that performs cross-origin requests to measure Web filtering; (2) Augur, a tool that exploits side-channel information in the Internet Protocol (IP) to measure filtering using network-level access control lists; and (3) a tool to measure DNS filtering using queries through open DNS resolvers. These three tools allow us—for the first time ever—to characterize Internet censorship continuously, from hundreds of countries around the world, at different layers of the network protocol stack. each of these techniques involves both technical and ethical challenges. I will describe some of the challenges that we faced in designing and implementing these tools, how we tackled these challenges, our experiences with measurements to date, and our plans for the future. Long term, our goal is to collaborate with social scientists to bring data to bear on a wide variety of questions concerning Internet censorship and information control; I will conclude with an appeal to cross-disciplinary work in this area and some ideas for how computer scientists and social scientists might work together on some of these pressing questions going forward. > > This research is in collaboration with Sam Burnett, Roya Ensafi, Paul Pearce, Ben Jones, Frank Li, and Vern Paxson.

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Colloquium Speaker: Rashmi Vinayak, Thursday, April 13- 4:30pm- reminder
by Nicole E. Wagenblast 12 Apr '17

12 Apr '17

Colloquium Speaker Departments of Computer Science and Electrical Engineering Rashmi Vinayak, University of California, Berkeley Thursday, April 13, 2017- 4:30pm Computer Science 105 Host: Prof. Kyle Jamieson (CS) and Prof. Naveen Verma (EE) Smart redundancy for big-data systems: Theory and Practice Large-scale distributed storage and caching systems form the foundation of big-data systems. A key scalability challenge in distributed storage systems is achieving fault tolerance in a resource-efficient manner. Towards addressing this challenge, erasure codes provide a storage-efficient alternative to the traditional approach of data replication. However, classical erasure codes come with critical drawbacks: while optimal in utilizing storage space, they significantly increase the usage of other important cluster resources such as network and I/O. In the first part of the talk, I present new erasure codes and theoretical optimality guarantees. The proposed codes reduce the network and I/O usage by 35-70% for typical parameters while retaining the storage efficiency of classical codes. I then present an erasure-coded storage system that employs the proposed codes, and demonstrate significant benefits over the state-of-the-art in evaluations under production setting at Facebo ok. Our codes have been integrated into Apache Hadoop 3.0. The second part of the talk focuses on achieving high performance in distributed caching systems. These systems routinely face the challenges of skew in data popularity, background traffic imbalance, and server failures, which result in load imbalance across servers and degradation in read latencies. I present EC-Cache, a cluster cache that employs erasure coding to achieve a 3-5x improvement as compared to the state-of-the-art. Rashmi K. Vinayak received her PhD in the EECS department at UC Berkeley in 2016, where she is now a postdoctoral researcher at AMPLab/RISELab and BLISS working with Ion Stoica and Kannan Ramchandran. Her dissertation received the Eli Jury Award 2016 from the EECS department at UC Berkeley for outstanding achievement in the area of systems, communications, control, or signal processing. Rashmi is the recipient of the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. She is also a recipient of the Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16. Her research interests lie in the theoretical and system challenges that arise in storage and analysis of big data.

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Colloquium Speaker: Rashmi Vinayak, Thursday, April 13- 4:30pm
by Nicole E. Wagenblast 07 Apr '17

07 Apr '17

Colloquium Speaker Departments of Computer Science and Electrical Engineering Rashmi Vinayak, University of California, Berkeley Thursday, April 13, 2017- 4:30pm Computer Science 105 Host: Prof. Kyle Jamieson (CS) and Prof. Naveen Verma (EE) Smart redundancy for big-data systems: Theory and Practice Large-scale distributed storage and caching systems form the foundation of big-data systems. A key scalability challenge in distributed storage systems is achieving fault tolerance in a resource-efficient manner. Towards addressing this challenge, erasure codes provide a storage-efficient alternative to the traditional approach of data replication. However, classical erasure codes come with critical drawbacks: while optimal in utilizing storage space, they significantly increase the usage of other important cluster resources such as network and I/O. In the first part of the talk, I present new erasure codes and theoretical optimality guarantees. The proposed codes reduce the network and I/O usage by 35-70% for typical parameters while retaining the storage efficiency of classical codes. I then present an erasure-coded storage system that employs the proposed codes, and demonstrate significant benefits over the state-of-the-art in evaluations under production setting at Facebook. Our codes have been integrated into Apache Hadoop 3.0. The second part of the talk focuses on achieving high performance in distributed caching systems. These systems routinely face the challenges of skew in data popularity, background traffic imbalance, and server failures, which result in load imbalance across servers and degradation in read latencies. I present EC-Cache, a cluster cache that employs erasure coding to achieve a 3-5x improvement as compared to the state-of-the-art. Rashmi K. Vinayak received her PhD in the EECS department at UC Berkeley in 2016, where she is now a postdoctoral researcher at AMPLab/RISELab and BLISS working with Ion Stoica and Kannan Ramchandran. Her dissertation received the Eli Jury Award 2016 from the EECS department at UC Berkeley for outstanding achievement in the area of systems, communications, control, or signal processing. Rashmi is the recipient of the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. She is also a recipient of the Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16. Her research interests lie in the theoretical and system challenges that arise in storage and analysis of big data.

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**Location Change** Systems Speaker: Tam Vu, Today, April 6- 12:30pm
by Nicole E. Wagenblast 06 Apr '17

06 Apr '17

Systems Talk Tam Vu , University of Colorado at Boulder Thursday, April 6- 12:30pm Computer Science 105 Title: Wearable and Wireless Cyber-Physical Systems for Non-invasive Sleep Quality Monitoring Sleep occupies nearly a third of human’s life and acts as a critical daily function, helping our body to balance and regulate vital systems. However, in the United States, more than 35% of the population is sleep deprived. Therefore, quantifying sleep quality is important and has significant clinical value in detecting and diagnosing various sleep-related disorders. Unfortunately, the current “gold standard” for studying patients’ sleep is obtrusive, expensive, and often inaccurate. In this talk, I will introduce our wearable and radio-based sensing systems that promise unobtrusive, low-cost, and accurate sleep study for in-hospital and in-home settings. I will start with WiSpiro, a sensing system that is able to unobtrusively monitor breathing volume and detect sleep disorder breathing in patients using radio signals from afar. I will then discuss LIBS, an in-ear wearable sensing system that can simultaneously monitor human’s brain activities, eye movement, and facial movement, which are critical for fine-grained sleep stage monitoring. I will also identify other potential uses of these systems in a broader context of health care, such as monitoring eating habits and disorders, detecting autism at early stages, improving neurological surgery practice, and detecting seizure. I will conclude the talk by discussing my on-going research as well as my future directions to improve current health care practices through the development of other innovative cyber-physical healthcare systems. Bio: Tam Vu is directing Mobile and Networked Systems (MNS) Lab at University of Colorado Boulder, where he and his team work on building systems to improve pediatric health care practices. At MNS, he designs and implements novel and practical cyber-physical systems to make physiological sensing (e.g. breathing volume measurement, brainwave signal monitoring, muscle movement recording, and sleep quality monitoring) less obtrusive at lower cost. Tam Vu ’s research contribution has been recognized with four best paper awards from ACM SenSys 2016, MobiCom S3 2016, MobiCom 2012, and MobiCom 2011; a Google Faculty Research Award in 2014; and wide press coverage including Denver Post, CNN TV, NY Times, The Wall Street Journal, National Public Radio (NPR), MIT Technology Review, Yahoo News. He is actively pushing his research outcome to practice through technology transfer activities with 9 patents filed in the last 3 years and forming 2 startups to commercialize them. He received his Ph.D. in Computer Science from WINLAB, Rutgers University in 2013 and a BS in Computer Science from Hanoi University of Technology in 2006.

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**Location Change** Systems Speaker: Tam Vu, Thursday, April 6-12:30pm
by Nicole E. Wagenblast 05 Apr '17

05 Apr '17

Systems Talk Tam Vu , University of Colorado at Boulder Thursday, April 6- 12:30pm Computer Science 105 Title: Wearable and Wireless Cyber-Physical Systems for Non-invasive Sleep Quality Monitoring Sleep occupies nearly a third of human’s life and acts as a critical daily function, helping our body to balance and regulate vital systems. However, in the United States, more than 35% of the population is sleep deprived. Therefore, quantifying sleep quality is important and has significant clinical value in detecting and diagnosing various sleep-related disorders. Unfortunately, the current “gold standard” for studying patients’ sleep is obtrusive, expensive, and often inaccurate. In this talk, I will introduce our wearable and radio-based sensing systems that promise unobtrusive, low-cost, and accurate sleep study for in-hospital and in-home settings. I will start with WiSpiro, a sensing system that is able to unobtrusively monitor breathing volume and detect sleep disorder breathing in patients using radio signals from afar. I will then discuss LIBS, an in-ear wearable sensing system that can simultaneously monitor human’s brain activities, eye movement, and facial movement, which are critical for fine-grained sleep stage monitoring. I will also identify other potential uses of these systems in a broader context of health care, such as monitoring eating habits and disorders, detecting autism at early stages, improving neurological surgery practice, and detecting seizure. I will conclude the talk by discussing my on-going research as well as my future directions to improve current health care practices through the development of other innovative cyber-physical healthcare systems. Bio: Tam Vu is directing Mobile and Networked Systems (MNS) Lab at University of Colorado Boulder, where he and his team work on building systems to improve pediatric health care practices. At MNS, he designs and implements novel and practical cyber-physical systems to make physiological sensing (e.g. breathing volume measurement, brainwave signal monitoring, muscle movement recording, and sleep quality monitoring) less obtrusive at lower cost. Tam Vu ’s research contribution has been recognized with four best paper awards from ACM SenSys 2016, MobiCom S3 2016, MobiCom 2012, and MobiCom 2011; a Google Faculty Research Award in 2014; and wide press coverage including Denver Post, CNN TV, NY Times, The Wall Street Journal, National Public Radio (NPR), MIT Technology Review, Yahoo News. He is actively pushing his research outcome to practice through technology transfer activities with 9 patents filed in the last 3 years and forming 2 startups to commercialize them. He received his Ph.D. in Computer Science from WINLAB, Rutgers University in 2013 and a BS in Computer Science from Hanoi University of Technology in 2006.

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Colloquium Speaker: Felix Heide, Today, 12:30pm
by Nicole E. Wagenblast 04 Apr '17

04 Apr '17

Colloquium Speaker Felix Heide, University of British Columbia Tuesday, April 4, 2017 - 12:30pm Computer Science 105 Host: Prof Szymon Rusinkiewicz Capturing the “Invisible”: Computational Imaging for Robust Sensing and Vision Imaging has become an essential part of how we communicate with each other, how autonomous agents sense the world and act independently, and how we research chemical reactions and biological processes. Today's imaging and computer vision systems, however, often fail in critical scenarios, for example in low light or in fog. This is due to ambiguity in the captured images, introduced partly by imperfect capture systems, such as cellphone optics and sensors, and partly present in the signal before measuring, such as photon shot noise. This ambiguity makes imaging with conventional cameras challenging, e.g. low-light cellphone imaging, and it makes high-level computer vision tasks difficult, such as scene segmentation and understanding. In this talk, I will present several examples of algorithms that computationally resolve this ambiguity and make sensing and vision systems robust. These methods rely on three key ingredients: accurate probabilistic forward models, learned priors, and efficient large-scale optimization methods. In particular, I will show how to achieve better low-light imaging using cell-phones (beating Google's HDR+), and how to classify images at 3 lux (substantially outperforming very deep convolutional networks, such as the Inception-v4 architecture). Using a similar methodology, I will discuss ways to miniaturize existing camera systems by designing ultra-thin, focus-tunable diffractive optics. Finally, I will present new exotic imaging modalities which enable new applications at the forefront of vision and imaging, such as seeing through scattering media and imaging objects outside direct line of sight. Felix Heide is a postdoctoral research working with Professor Gordon Wetzstein in the Department of Electrical Engineering at Stanford University. He is interested in the theory and application of computational imaging and vision systems. Researching imaging systems end-to-end, Felix's work lies at the intersection of optics, machine learning, optimization, computer graphics and computer vision. Felix has co-authored over 25 publications and filed 3 patents. He received his Ph.D. in December 2016 at the University of British Columbia under the advisement of Professor Wolfgang Heidrich. His doctoral dissertation focuses on optimization for computational imaging and won the Alain Fournier Ph.D. Dissertation Award.

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Colloquium Speaker: Song Han- Today- 4:30pm
by Nicole E. Wagenblast 03 Apr '17

03 Apr '17

Computer Science and Electrical Engineering Colloquium Speaker Song Han, Stanford University Monday, April 3, 4:30pm Computer Science 105 Host: Prof. Kai Li and Prof. David Wentzlaff Efficient Methods and Hardware for Deep Learning Deep learning has spawned a wide range of AI applications that are changing our lives. However, deep neural networks are both computationally and memory intensive, thus they are power hungry when deployed on embedded systems and data centers with a limited power budget. To address this problem, I will present an algorithm and hardware co-design methodology for improving the efficiency of deep learning. I will first introduce "Deep Compression", which can compress deep neural network models by 10–49× without loss of prediction accuracy for a broad range of CNN, RNN, and LSTMs. The compression reduces both computation and storage. Next, by changing the hardware architecture and efficiently implementing Deep Compression, I will introduce EIE, the Efficient Inference Engine, which can perform decompression and inference simultaneously, saving a significant amount of memory bandwidth. By taking advantage of the compressed model and being able to deal with an irregular computation pattern efficiently, EIE achieves 13× speedup and 3000× better energy efficiency over GPU. Finally, I will revisit the inefficiencies in current learning algorithms, present DSD training, and discuss the challenges and future work in efficient methods and hardware for deep learning. Song Han is a Ph.D. candidate supervised by Prof. Bill Dally at Stanford University. His research focuses on energy-efficient deep learning, at the intersection between machine learning and computer architecture. He proposed the Deep Compression algorithm, which can compress neural networks by 10–49× while fully preserving prediction accuracy. He designed the first hardware accelerator that can perform inference directly on a compressed sparse model, which results in significant speedup and energy saving. His work has been featured by O’Reilly, TechEmergence, TheNextPlatform, and Embedded Vision, and it has impacted the industry. He led research efforts in model compression and hardware acceleration that won the Best Paper Award at ICLR’16 and the Best Paper Award at FPGA’17. Before joining Stanford, Song graduated from Tsinghua University.

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