- talks - lists.cs.princeton.edu

Robert Kiefer will present his FPO, "New Abstractions for Mobile Connectivity and Resource Management" Wednesday, 4/27/2016 at 2:00pm in CS 302.
by Nicki Gotsis 20 Apr '16

20 Apr '16

Robert Kiefer will present his FPO, "New Abstractions for Mobile Connectivity and Resource Management" Wednesday, 4/27/2016 at 2:00pm in CS 302. The members of his committee are Michael Freedman (Adviser); Nick Feamster and Jennifer Rexford (Readers); Margaret Martonosi, David August (Examiners). A copy of his/her thesis, is available in Room 310. Everyone is invited to attend his/her talk. The talk title and abstract follow below. "New Abstractions for Mobile Connectivity and Resource Management" Mobile devices have become an important—and for many, primary—means of connecting to the Internet. They o↵er great flexibility in how and where they are used, even with constraints like battery life and data caps. However, their network stack and operating system are lacking when it comes to network mobility and managing their limited resources. First, mobility, if even addressed by devices and apps, is done in an ad-hoc manner to “hide” connectivity issues rather than fix underlying shortcomings. Second, resource management is dealt with in broad strokes, scattered among various app and device settings pages. In this thesis, we provide new abstractions addressing these issues. To better handle mobility in the network stack, we need to remove the overloading of IP/port as both app-level connection identifiers and network addresses, thereby allowing apps to maintain a connection even when addresses change. To that end, we present ECCP, a protocol that splits the current transport layer into two: one for connection management (e.g., startup, teardown, migration) and one for data delivery semantics (e.g., reliable, best-e↵ort, etc). ECCP is part of a larger network architecture, Serval, which addresses several pain points with today’s networked systems, consisting of replicated backend services and mobile, multi-homed clients. We derive a state machine for ECCP supporting migration and multipath, and through Serval, we demonstrate ECCP’s value in scenarios utilizing mobility (e.g., VM migration) while achieving par performance with TCP. To utilize better mobility support, devices need a more expressive resource management system. Currently, network choice and resource management is limited to broad decisions (e.g., if cellular network usage is allowed). Users must either choose “good enough” settings or else micromanage their device and apps, e.g., turning WiFi on/o↵ while streaming music between hotspots. Instead, we introduce Tango, a programmatic, policy-based network resource management platform, that works at the device and app levels to o↵er more flexiblity. Users and apps define policies that dynamically monitor device state to adjust resource usage on their behalf. Using Tango, a user can, e.g., minimize cellular data while streaming music (using it only to prevent playback pauses), saving up to 60-95%.

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Pixl Talk: Alexandre Alahi, Wednesday Apr 20 1:30PM-2:30PM
by Nicole E. Wagenblast 20 Apr '16

20 Apr '16

Speaker : Alexandre Alahi Time : Wednesday Apr 20 1:30PM-2:30PM Location : CS Room 402 Title : Towards Socially - aware Artificial Intelligence : Human Behavior Understanding at Scale Abstract : Over the past sixty years, Intelligent Machines (IM) have made great progress in playing games, tagging images in isolation, and recently making decisions for self-driving vehicles. Despite these advancements, they are still far from making decisions in social scenes and assisting humans in public spaces such as terminals, malls, campuses, or any crowded urban environment. To overcome these limitations, we need to empower machines with social intelligence , i.e., the ability to get along well with others and facilitate mutual cooperation. This is crucial to design smart spaces that adapt to the behavior of humans for efficiency, or develop autonomous machines that assist in crowded public spaces (e.g., delivery robots, or self-navigating segways). In this talk, I will present my work towards socially - aware machines that can understand human social dynamics and learn to forecast them. First, I will highlight the machine vision techniques behind understanding the behavior of more than 100 million individuals captured by multi-modal cameras in urban spaces. I will show how to use sparsity promoting priors to extract meaningful information about human behavior from an overwhelming volume of high dimensional and high entropy data. Second, I will introduce a new deep learning method to forecast human social behavior . The causality behind human behavior is an interplay between both observable and non-observable cues (e.g., intentions). For instance, when humans walk into crowded urban environments such as a busy train terminal, they obey a large number of (unwritten) common sense rules and comply with social conventions. They typically avoid crossing groups and keep a personal distance to their surrounding. I will present detailed insights on how to learn these interactions from millions of trajectories. I will describe a new recurrent neural network that can jointly reason on correlated sequences and simulate human trajectories in crowded scenes. It opens new avenues of research in learning the causalities behind the world we observe. I will conclude my talk by mentioning some ongoing work in applying these techniques to social robots, and the first generation of smart hospitals. Bio : Alexandre Alahi is currently a postdoctoral fellow at Stanford University and received his PhD from EPFL. His research interests span visual information processing, computer vision, machine learning, robotics, and are focused around understanding and forecasting human social behaviors. In particular, he is interested in sparse approximation, deep learning, big visual data processing, real-time machine vision, and multi-modal reasoning. He was awarded the Swiss NSF early and advanced researcher grants. He won the CVPR 2012 Open Source Award for his work on Retina-inspired image descriptor, and the ICDSC 2009 Challenge Prize for his sparsity driven algorithm to track sport players. His PhD was nominated for the EPFL PhD prize. His research has been covered by the Wall Street journal, and PBS TV channel in the US, as well as European newspapers, Swiss TV news, and Euronews TV channel in Europe. He has also co-founded the startup Visiosafe, transferring his research on understanding human behavior into an industrial product. He was selected as the Top 20 Swiss Venture leaders in 2010 and won several startup competitions.

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Colloquium Speaker: Joseph Bonneau, Thursday, April 21- 12:30pm- reminder
by Nicole E. Wagenblast 20 Apr '16

20 Apr '16

Colloquium Speaker Joseph Bonneau , from Stanford University Thursday, April 21- 12:30pm Computer Science 105 Improving security requires both empirically-grounded insights into existing systems and threats, as well as theoretically-grounded solutions that anticipate how future users and attackers will adapt. I will present examples of both. I’ll begin by introducing empirical methods that I created to bring quantitative rigor to the question of how users choose authentication secrets (PINs, passwords, and security questions), a topic that has long been misunderstood due to a lack of data. I'll then present two theoretically-grounded approaches that apply cryptography to providing transparency that trusted authorities are behaving correctly. The first addresses servers for distributing public keys for secure communication, ensuring that the authority cannot lie without being detected. The second ensures that banks that store bitcoins are solvent: that they actually are holding as many bitcoins as they have promised to their clients. Joseph Bonneau is a Postdoctoral Researcher at Stanford University and a Technology Fellow at the Electronic Frontier Foundation. His research focuses on cryptography and security protocols, particularly how they interact with human and organizational behavior and economic incentives. Recently he has focused on Bitcoin and related cryptocurrencies and secure messaging tools. He is also known for his work on passwords and web authentication. He received a PhD from the University of Cambridge under the supervision of Ross Anderson and an BS/MS from Stanford under the supervision of Dan Boneh. Last year he was as a Postdoctoral Fellow at CITP, Princeton and he has previously worked at Google, Yahoo, and Cryptography Research Inc.

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Colloquium Speaker Ben Shneiderman, Fri April 22, 12:30pm
by Nicole E. Wagenblast 19 Apr '16

19 Apr '16

Colloquium Speaker Ben Shneiderman, University of Maryland Friday, April 22, 12:30pm Computer Science 105 The New ABCs of Research: Achieving Breakthrough Collaborations All Princeton ACM / IEEE-CS meetings are open to the public. Students and their parents are welcome. How should we organize research programs in computer science and technology? Research helps us produce innovative new products (the next iPhone), and research also delivers “foundational theories” (new algorithms and supporting technologies). Solving the immense problems of the 21st century will require ambitious research teams that are skilled at producing practical solutions and foundational theories simultaneously – that is the ABC Principle: Applied & Basic Combined. Then these research teams can deliver high-impact outcomes by applying the SED Principle: Blend Science, Engineering and Design Thinking, which encourages use of the methods from all three disciplines. These guiding principles (ABC & SED) are meant to replace Vannevar Bush’s flawed linear model from 1945 that has misled researchers for 70+ years. These new guiding principles will enable students, faculty, business leaders, and government policy makers to accelerate discovery and innovation. The examples in the talk will emphasize how these guiding principles can be applied to reinvigorate computing research. Ben Shneiderman is a Distinguished University Professor in the Department of Computer Science at the University of Maryland. He is also the Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/), and a Member of the University of Maryland Institute for Advanced Computer Studies (UMIACS). He is a Fellow of the AAAS, ACM, IEEE, and NAI, and a Member of the National Academy of Engineering, in recognition of his pioneering contributions to human-computer interaction and information visualization. His contributions include the direct manipulation concept, clickable highlighted web-links, touchscreen keyboards, dynamic query sliders for Spotfire, development of treemaps, novel network visualizations for NodeXL, and temporal event sequence analysis for electronic health records. Ben is the co-author with Catherine Plaisant of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016). His book Leonardo’s Laptop (MIT Press) won the IEEE book award for Distinguished Literary Contribution. Shneiderman’s latest book is The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, February 2016).

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Princeton ACM/IEEE-CS April meeting
by Nicole E. Wagenblast 19 Apr '16

19 Apr '16

The New ABCs of Research: Achieving Breakthrough Collaborations Thursday, April 21, 2016, 8:00pm Refreshments and networking at 7:30pm Computer Science 105 How should we organize research programs in computer science and technology? Research helps us produce innovative new products (the next iPhone), and research also delivers “foundational theories” (new algorithms and supporting technologies). Solving the immense problems of the 21st century will require ambitious research teams that are skilled at producing practical solutions and foundational theories simultaneously – that is the ABC Principle: Applied & Basic Combined. Then these research teams can deliver high-impact outcomes by applying the SED Principle: Blend Science, Engineering and Design Thinking, which encourages use of the methods from all three disciplines. These guiding principles (ABC & SED) are meant to replace Vannevar Bush’s flawed linear model from 1945 that has misled researchers for 70+ years. These new guiding principles will enable students, faculty, business leaders, and government policy makers to accelerate discovery and innovation. The examples in the talk will emphasize how these guiding principles can be applied to reinvigorate computing research. Ben Shneiderman (http://www.cs.umd.edu/~ben) is a Distinguished University Professor in the Department of Computer Science at the University of Maryland. He is also the Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/), and a Member of the University of Maryland Institute for Advanced Computer Studies (UMIACS). He is a Fellow of the AAAS, ACM, IEEE, and NAI, and a Member of the National Academy of Engineering, in recognition of his pioneering contributions to human-computer interaction and information visualization. His contributions include the direct manipulation concept, clickable highlighted web-links, touchscreen keyboards, dynamic query sliders for Spotfire, development of treemaps, novel network visualizations for NodeXL, and temporal event sequence analysis for electronic health records. Ben is the co-author with Catherine Plaisant of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016). His book Leonardo’s Laptop (MIT Press) won the IEEE book award for Distinguished Literary Contribution. Shneiderman’s latest book is The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, February 2016). Information: Dennis Mancl (908) 285-1066 On-line info: http://PrincetonACM.acm.org All Princeton ACM / IEEE-CS meetings are open to the public. Students and their parents are welcome. There is no admission charge, and refreshments are served. A pre-meeting dinner is held at 6:00 p.m. at Ruby Tuesday’s Restaurant on Route 1. Please send email to princetonacm(a)gmail.com in advance if you plan to attend the dinner.

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Colloquium Speaker: Joseph Bonneau, Thursday, April 21- 12:30pm
by Nicole E. Wagenblast 15 Apr '16

15 Apr '16

Colloquium Speaker Joseph Bonneau , from Stanford University Thursday, April 21- 12:30pm Computer Science 105 Improving security requires both empirically-grounded insights into existing systems and threats, as well as theoretically-grounded solutions that anticipate how future users and attackers will adapt. I will present examples of both. I’ll begin by introducing empirical methods that I created to bring quantitative rigor to the question of how users choose authentication secrets (PINs, passwords, and security questions), a topic that has long been misunderstood due to a lack of data. I'll then present two theoretically-grounded approaches that apply cryptography to providing transparency that trusted authorities are behaving correctly. The first addresses servers for distributing public keys for secure communication, ensuring that the authority cannot lie without being detected. The second ensures that banks that store bitcoins are solvent: that they actually are holding as many bitcoins as they have promised to their clients. Joseph Bonneau is a Postdoctoral Researcher at Stanford University and a Technology Fellow at the Electronic Frontier Foundation. His research focuses on cryptography and security protocols, particularly how they interact with human and organizational behavior and economic incentives. Recently he has focused on Bitcoin and related cryptocurrencies and secure messaging tools. He is also known for his work on passwords and web authentication. He received a PhD from the University of Cambridge under the supervision of Ross Anderson and an BS/MS from Stanford under the supervision of Dan Boneh. Last year he was as a Postdoctoral Fellow at CITP, Princeton and he has previously worked at Google, Yahoo, and Cryptography Research Inc.

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Colloquium Speaker Robert West, TODAY, April 15- 12:30pm
by Mitra D. Kelly 15 Apr '16

15 Apr '16

Colloquium Speaker Robert West, Infolab at Stanford University Friday April 15- 12:30pm Computer Science 105 Human Behavior in Networks Humans as well as information are organized in networks. Interacting with these networks is part of our daily lives: we talk to friends in our social network; we find information by navigating the Web; and we form opinions by listening to others and to the media. Thus, understanding, predicting, and enhancing human behavior in networks poses important research problems for computer and data science with practical applications of high impact. In this talk I will present some of my work in this area, focusing on (1) human navigation of information networks and (2) person-to-person opinions in social networks. Network navigation constitutes a fundamental human behavior: in order to make use of the information and resources around us, we constantly explore, disentangle, and navigate networks such as the Web. Studying navigation patterns lets us understand better how humans reason about complex networks and lets us build more human-friendly information systems. As an example, I will present an algorithm for improving website hyperlink structure by mining raw web server logs. The resulting system is being deployed on Wikipedia's full server logs at terabyte scale, producing links that are clicked 10 times as frequently as the average link added by human Wikipedia editors. Communication and coordination through natural language is another prominent human network behavior. Studying the interplay of social network structure and language has the potential to benefit both sociolinguistics and natural language processing. Intriguing opportunities and challenges have arisen recently with the advent of online social media, which produce large amounts of both network and natural language data. As an example, I will discuss my work on person-to-person sentiment analysis in social networks, which combines the sociological theory of structural balance with techniques from natural language processing, resulting in a machine learning model for sentiment prediction that clearly outperforms both text-only and network-only versions. I will conclude the talk by sketching interesting future directions for computational approaches to studying and enhancing human behavior in networks. Bio: Robert West is a sixth-year Ph.D. candidate in Computer Science in the Infolab at Stanford University, advised by Jure Leskovec. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. Previously, he obtained a Master's degree from McGill University in 2010 and a Diplom degree from Technische Universität München in 2007. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker Robert West, Friday April 15- 12:30pm
by Mitra D. Kelly 14 Apr '16

14 Apr '16

Colloquium Speaker Robert West, Infolab at Stanford University Friday April 15- 12:30pm Computer Science 105 Human Behavior in Networks Humans as well as information are organized in networks. Interacting with these networks is part of our daily lives: we talk to friends in our social network; we find information by navigating the Web; and we form opinions by listening to others and to the media. Thus, understanding, predicting, and enhancing human behavior in networks poses important research problems for computer and data science with practical applications of high impact. In this talk I will present some of my work in this area, focusing on (1) human navigation of information networks and (2) person-to-person opinions in social networks. Network navigation constitutes a fundamental human behavior: in order to make use of the information and resources around us, we constantly explore, disentangle, and navigate networks such as the Web. Studying navigation patterns lets us understand better how humans reason about complex networks and lets us build more human-friendly information systems. As an example, I will present an algorithm for improving website hyperlink structure by mining raw web server logs. The resulting system is being deployed on Wikipedia's full server logs at terabyte scale, producing links that are clicked 10 times as frequently as the average link added by human Wikipedia editors. Communication and coordination through natural language is another prominent human network behavior. Studying the interplay of social network structure and language has the potential to benefit both sociolinguistics and natural language processing. Intriguing opportunities and challenges have arisen recently with the advent of online social media, which produce large amounts of both network and natural language data. As an example, I will discuss my work on person-to-person sentiment analysis in social networks, which combines the sociological theory of structural balance with techniques from natural language processing, resulting in a machine learning model for sentiment prediction that clearly outperforms both text-only and network-only versions. I will conclude the talk by sketching interesting future directions for computational approaches to studying and enhancing human behavior in networks. Bio: Robert West is a sixth-year Ph.D. candidate in Computer Science in the Infolab at Stanford University, advised by Jure Leskovec. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. Previously, he obtained a Master's degree from McGill University in 2010 and a Diplom degree from Technische Universität München in 2007. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker: Olga Russakovsky, Today- 12:30pm
by Nicole E. Wagenblast 14 Apr '16

14 Apr '16

Colloquium Speaker Olga Russakovsky, Carnegie Mellon University Thursday, April 14- 12:30pm Computer Science 105 The human side of computer vision Intelligent agents acting in the real world need advanced vision capabilities to perceive, learn from, reason about and interact with their environment. In this talk, I will explore the role that humans play in the design and deployment of computer vision systems. Large-scale manually labeled datasets have proven instrumental for scaling up visual recognition, but they come at a substantial human cost. I will first briefly talk about strategies for making optimal use of human annotation effort for computer vision progress. However, no dataset can foresee all the visual scenarios that a real-world system might encounter. I will argue that seamlessly integrating human expertise at runtime will become increasingly important for open-world computer vision. I will introduce, and demonstrate the effectiveness of, a rigorous mathematical framework for human-machine collaboration. Looking ahead, in order for such collaborations to become practical, the computer vision algorithms we design will need to be both efficient and interpretable. I will conclude by presenting a new deep reinforcement learning model for human action detection in videos that is efficient, interpretable and more accurate than prior art, opening up new avenues for practical human-in-the-loop exploration. Olga Russakovsky recently completed her PhD in computer science at Stanford and is now a postdoctoral fellow at Carnegie Mellon University. Her research is in computer vision, closely integrated with machine learning and human-computer interaction. Her work was featured in the New York Times and MIT Technology Review. She served as a Senior Program Committee member for WACV’16, led the ImageNet Large Scale Visual Recognition Challenge effort for two years, and organized multiple workshops and tutorials on large-scale recognition at premier computer vision conferences ICCV’13, ECCV’14, CVPR’15, ICCV’15 and CVPR’16. In addition, she founded and directs the Stanford AI Laboratory’s outreach camp SAILORS (featured in Wired and published in SIGCSE’16) designed to expose high school students in underrepresented populations to the field of AI.

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Colloquium Speaker: Olga Russakovsky, Thursday, April 14, 12:30pm- reminder
by Nicole E. Wagenblast 13 Apr '16

13 Apr '16

Colloquium Speaker Olga Russakovsky, Carnegie Mellon University Thursday, April 14- 12:30pm Computer Science 105 The human side of computer vision Intelligent agents acting in the real world need advanced vision capabilities to perceive, learn from, reason about and interact with their environment. In this talk, I will explore the role that humans play in the design and deployment of computer vision systems. Large-scale manually labeled datasets have proven instrumental for scaling up visual recognition, but they come at a substantial human cost. I will first briefly talk about strategies for making optimal use of human annotation effort for computer vision progress. However, no dataset can foresee all the visual scenarios that a real-world system might encounter. I will argue that seamlessly integrating human expertise at runtime will become increasingly important for open-world computer vision. I will introduce, and demonstrate the effectiveness of, a rigorous mathematical framework for human-machine collaboration. Looking ahead, in order for such collaborations to become practical, the computer vision algorithms we design will need to be both efficient and interpretable. I will conclude by presenting a new deep reinforcement learning model for human action detection in videos that is efficient, interpretable and more accurate than prior art, opening up new avenues for practical human-in-the-loop exploration. Olga Russakovsky recently completed her PhD in computer science at Stanford and is now a postdoctoral fellow at Carnegie Mellon University. Her research is in computer vision, closely integrated with machine learning and human-computer interaction. Her work was featured in the New York Times and MIT Technology Review. She served as a Senior Program Committee member for WACV’16, led the ImageNet Large Scale Visual Recognition Challenge effort for two years, and organized multiple workshops and tutorials on large-scale recognition at premier computer vision conferences ICCV’13, ECCV’14, CVPR’15, ICCV’15 and CVPR’16. In addition, she founded and directs the Stanford AI Laboratory’s outreach camp SAILORS (featured in Wired and published in SIGCSE’16) designed to expose high school students in underrepresented populations to the field of AI.

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