April 2016 - talks - lists.cs.princeton.edu

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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Colloquium Speaker Robert West, Friday April 15- 12:30pm
by Mitra D. Kelly 11 Apr '16

11 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: Andrej Karpathy Monday April 11, 12:30pm
by Nicole E. Wagenblast 11 Apr '16

11 Apr '16

Colloquium Speaker Andrej Karpathy, Stanford University Monday, April 11- 12:30pm Computer Science 105 Connecting Images and Natural Language Intelligent agents require the ability to perceive their environments, understand their high-level semantics, and communicate with humans. While computer vision has recently made great strides on visual recognition tasks, the predominant paradigm is to predict one or more fixed visual categories for each image. I will describe a line of work that significantly expands the vocabulary of our computer vision systems and allows them to express visual concepts in natural language, such as “a picture of a girl playing with a stack of legos”, or “a couple holding hands and walking on a beach”. In particular, the final model can take an image and both detect and describe in natural language all of its salient regions. My modeling techniques draw on recent advances in Deep Learning that allow us to construct and train neural networks with hundreds of millions of neurons that take raw images and map them directly to natural language sentences. I will show that the model generates qualitatively compelling results and quantitative evaluation and control experiments demonstrate the strength of this approach with respect to simpler baselines and previous methods. Andrej Karpathy is a Computer Science Ph.D. candidate at Stanford University working with Prof. Fei-Fei Li. He received his M.S. from University of British Columbia in Computer Science and his B.S from University of Toronto in Computer Science and Physics. He is interested in the intersection of computer vision, natural language processing and reinforcement learning with the aim of developing agents who can intelligently interact with humans in dynamic environments. His work was featured in the New York Times and MIT Technology Review. He helped develop and instruct a new Computer Science class at Stanford on Convolutional Neural Networks for Visual Recognition. In his spare time he develops Deep Learning libraries in Javascript and maintains websites that support more efficient meta-research.

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