April 2017 - talks - lists.cs.princeton.edu

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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Colloquium Speaker: Ellie Pavlick, Today- 12:30pm
by Nicole E. Wagenblast 03 Apr '17

03 Apr '17

Colloquium Speaker Ellie Pavlick , University of Pennsylvania Monday, April 3, 2017 - 12:30pm Computer Science 105 Prof. Elad Hazan Natural Language Understanding with Paraphrases and Composition Natural language processing (NLP) aims to teach computers to understand human language. NLP has enabled some of the most visible applications of artificial intelligence, including Google search, IBM Watson, and Apple’s Siri. As AI is applied to increasingly complex domains such as health care, education, and government, NLP will play a crucial role in allowing computational systems to access the vast amount of human knowledge documented in the form of unstructured speech and text. In this talk, I will discuss my work on training computers to make inferences about what is true or false based on information expressed in natural language. My approach combines machine learning with insights from formal linguistics in order to build data-driven models of semantics which are more precise and interpretable than would be possible using linguistically naive approaches. I will begin with my work on automatically adding semantic annotations to the 100 million phrase pairs in the Paraphrase Database (PPDB). These annotations provide the type of information necessary for carrying out precise inferences in natural language, transforming the database into a largest available lexical semantics resource for natural language processing. I will then turn to the problem of compositional entailment, and present an algorithm for performing inferences about long phrases which are unlikely to have been observed in data. Finally, I will discuss my current work on pragmatic reasoning: when and how humans derive meaning from a sentence beyond what is literally contained in the words. I will describe the difficulties that such "common-sense" inference poses for automatic language understanding, and present my on-going work on models for overcoming these challenges. Ellie Pavlick is a PhD student at the University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her dissertation focuses on natural language inference and entailment. Outside of her dissertation research, Ellie has published work on stylistic variation in paraphrase--e.g. how paraphrases can effect the formality or the complexity of language--and on applications of crowdsourcing to natural language processing and social science problems. She has been involved in the design and instruction of Penn's first undergraduate course on Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen Institute for Artificial Intelligence.

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Colloquium Speaker: Song Han- Monday, April 3- 4:30pm- reminder
by Nicole E. Wagenblast 02 Apr '17

02 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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Colloquium Speaker: Ellie Pavlick, Monday, April 3- 12:30pm- reminder
by Nicole E. Wagenblast 02 Apr '17

02 Apr '17

Colloquium Speaker Ellie Pavlick , University of Pennsylvania Monday, April 3, 2017 - 12:30pm Computer Science 105 Prof. Elad Hazan Natural Language Understanding with Paraphrases and Composition Natural language processing (NLP) aims to teach computers to understand human language. NLP has enabled some of the most visible applications of artificial intelligence, including Google search, IBM Watson, and Apple’s Siri. As AI is applied to increasingly complex domains such as health care, education, and government, NLP will play a crucial role in allowing computational systems to access the vast amount of human knowledge documented in the form of unstructured speech and text. In this talk, I will discuss my work on training computers to make inferences about what is true or false based on information expressed in natural language. My approach combines machine learning with insights from formal linguistics in order to build data-driven models of semantics which are more precise and interpretable than would be possible using linguistically naive approaches. I will begin with my work on automatically adding semantic annotations to the 100 million phrase pairs in the Paraphrase Database (PPDB). These annotations provide the type of information necessary for carrying out precise inferences in natural language, transforming the database into a largest available lexical semantics resource for natural language processing. I will then turn to the problem of compositional entailment, and present an algorithm for performing inferences about long phrases which are unlikely to have been observed in data. Finally, I will discuss my current work on pragmatic reasoning: when and how humans derive meaning from a sentence beyond what is literally contained in the words. I will describe the difficulties that such "common-sense" inference poses for automatic language understanding, and present my on-going work on models for overcoming these challenges. Ellie Pavlick is a PhD student at the University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her dissertation focuses on natural language inference and entailment. Outside of her dissertation research, Ellie has published work on stylistic variation in paraphrase--e.g. how paraphrases can effect the formality or the complexity of language--and on applications of crowdsourcing to natural language processing and social science problems. She has been involved in the design and instruction of Penn's first undergraduate course on Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen Institute for Artificial Intelligence.

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