- talks - lists.cs.princeton.edu

Colloquium Speaker: Wyatt Lloyd, Monday, February 27- 12:30pm
by Nicole E. Wagenblast 21 Feb '17

21 Feb '17

Colloquium Speaker Prof. Wyatt Lloyd, University of Southern California Monday, February 27- 12:30pm Computer Science 105 Low Latency and Strong Guarantees for Scalable Storage Scalable storage systems, where data is sharded across many machines, are necessary to support web services whose data is too large for a single machine to handle. An ideal system would provide the lowest latency—to make the web services built on top of it fast—and the strongest guarantees—to make programming the web service easier. Theoretical results prove that such an ideal system is impossible, but all hope is not lost! Our work has made progress on this problem from both directions: providing stronger guarantees for low latency systems and providing lower latency for systems with strong guarantees. I will cover one of these systems, Rococo, in detail. I will also touch on our recent impossibility result, the SNOW Theorem, and how it guided us in building new systems with latency-optimal read-only transactions. Wyatt Lloyd is a third-year Assistant Professor of Computer Science at the University of Southern California. His research interests include the theory, design, implementation, evaluation, and deployment of large-scale distributed systems. He received his Ph.D. from Princeton University in 2013 for the COPS and Eiger systems, which demonstrated stronger semantics were compatible with low latency for scalable geo-replicated storage. He then spent a year as a Postdoctoral Researcher at Facebook, and he continues to collaborate with its engineers on projects related to media processing, storage, and delivery.

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Colloquium Speaker: Jacob Eisenstein, Today- 12:30pm
by Nicole E. Wagenblast 16 Feb '17

16 Feb '17

Colloquium Speaker Prof. Jacob Eisenstein, Georgia Institute of Technology Thursday, February 16, 12:30pm Computer Science 105 Host: Prof. Elad Hazan Putting language in context: social networks and discourse structures for robust language technology The creation of software that can use and understand natural human language would be a transformative technological advance, enabling computational inference to be brought to bear on the vast stores of knowledge that are encoded only as text. Supervised machine learning approaches have brought this possibility into view, but existing methods rely on annotated datasets that focus overwhelmingly on a narrow set of news texts, while failing to generalize to high-impact application domains such as social media and electronic health records. Human readers and listeners successfully comprehend language under difficult circumstances by relying on various forms of contextual information -- knowing who is speaking, and what they might be trying to say. My research brings this same contextual awareness to automated language processing, using deep learning architectures that are constructed to reflect theoretical ideas from sociolinguistics and discourse semantics. I will describe three applications of this methodology: (1) incorporating sociolinguistic variation into document classification; (2) linking textual references to canonicalized entities; (3) predicting the discourse relations that hold between sentences. I will also briefly describe research that uses computational linguistic analysis to obtain new evidence on sociocultural affinity and influence. Jacob Eisenstein is an Assistant Professor in the School of Interactive Computing at Georgia Tech. He works on statistical natural language processing, focusing on computational sociolinguistics, social media analysis, discourse, and machine learning. He is a recipient of the NSF CAREER Award, a member of the Air Force Office of Scientific Research (AFOSR) Young Investigator Program, and was a SICSA Distinguished Visiting Fellow at the University of Edinburgh. His work has also been supported by the National Institutes for Health, the National Endowment for the Humanities, and Google. Jacob was a Postdoctoral researcher at Carnegie Mellon and the University of Illinois. He completed his Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation award. Jacob's research has been featured in the New York Times, National Public Radio, and the BBC. Thanks to his brief appearance in If These Knishes Could Talk, Jacob has a Bacon number of 2.

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Talk on robotics by Siddhartha Srinivasa today at 4:30 in ELE
by Thomas Funkhouser 16 Feb '17

16 Feb '17

Title: *Robotic Manipulation under clutter and uncertainty with and around people* Speaker: Siddhartha Srinivasa, Carnegie Mellon University Series: EE Departmental Seminars Location: E-Quad, B205 Date/Time: Thursday, February 16, 2017 - 4:30pm Abstract: Robots manipulate with super-human speed and dexterity on factory floors. But yet they fail even under moderate amounts of clutter or uncertainty. However, human teleoperators perform remarkable acts of manipulation with the same hardware. My research goal is to bridge the gap between what robotic manipulators can do now and what they are capable of doing. What human operators intuitively possess that robots lack are models of interaction between the manipulator and the world that go beyond pick-and-place. I will describe our work on nonprehensile physics-based manipulation that has produced simple but effective models, integrated with proprioception and perception, that has enabled robots to fearlessly push, pull, and slide objects, and reconfigure clutter that comes in the way of their primary task. But human environments are also filled with humans. Collaborative manipulation is a dance, demanding the sharing of intentions, inferences, and forces between the robot and the human. I will also describe our work on the mathematics of human-robot interaction that has produced a framework for collaboration using Bayesian inference to model the human collaborator, and trajectory optimization to generate fluent collaborative plans. Finally, I will talk about our new initiative on assistive care that focuses on marrying physics, human-robot collaboration, control theory, and rehabilitation engineering to build and deploy caregiving systems. Bio: Siddhartha Srinivasa is the Finmeccanica Associate Professor at The Robotics Institute at Carnegie Mellon University. He works on robotic manipulation, with the goal of enabling robots to perform complex manipulation tasks under uncertainty and clutter, with and around people. To this end, he founded and directs the Personal Robotics Lab, and co-directs the Manipulation Lab. He has been a PI on the Quality of Life Technologies NSF ERC, DARPA ARM-S and the CMU CHIMP team on the DARPA DRC. Sidd is also passionate about building end-to-end systems (HERB, ADA, HRP3, CHIMP, Andy, among others) that integrate perception, planning, and control in the real world. Understanding the interplay between system components has helped produce state of the art algorithms for object recognition and pose estimation (MOPED), and dense 3D modeling (CHISEL, now used by Google Project Tango). Sidd received a B.Tech in Mechanical Engineering from the Indian Institute of Technology Madras in 1999, an MS in 2001 and a PhD in 2005 from the Robotics Institute at Carnegie Mellon University. He played badminton and tennis for IIT Madras, captained the CMU squash team, and lately runs competitively. http://ee.princeton.edu/events/robotic-manipulation-under-clutter-and-uncer…

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Colloquium Speaker: Jacob Eisenstein, Thursday, February 16- 12:30pm- reminder
by Nicole E. Wagenblast 15 Feb '17

15 Feb '17

Colloquium Speaker Prof. Jacob Eisenstein, Georgia Institute of Technology Thursday, February 16, 12:30pm Computer Science 105 Host: Prof. Elad Hazan Putting language in context: social networks and discourse structures for robust language technology The creation of software that can use and understand natural human language would be a transformative technological advance, enabling computational inference to be brought to bear on the vast stores of knowledge that are encoded only as text. Supervised machine learning approaches have brought this possibility into view, but existing methods rely on annotated datasets that focus overwhelmingly on a narrow set of news texts, while failing to generalize to high-impact application domains such as social media and electronic health records. Human readers and listeners successfully comprehend language under difficult circumstances by relying on various forms of contextual information -- knowing who is speaking, and what they might be trying to say. My research brings this same contextual awareness to automated language processing, using deep learning architectures that are constructed to reflect theoretical ideas from sociolinguistics and discourse semantics. I will describe three applications of this methodology: (1) incorporating sociolinguistic variation into document classification; (2) linking textual references to canonicalized entities; (3) predicting the discourse relations that hold between sentences. I will also briefly describe research that uses computational linguistic analysis to obtain new evidence on sociocultural affinity and influence. Jacob Eisenstein is an Assistant Professor in the School of Interactive Computing at Georgia Tech. He works on statistical natural language processing, focusing on computational sociolinguistics, social media analysis, discourse, and machine learning. He is a recipient of the NSF CAREER Award, a member of the Air Force Office of Scientific Research (AFOSR) Young Investigator Program, and was a SICSA Distinguished Visiting Fellow at the University of Edinburgh. His work has also been supported by the National Institutes for Health, the National Endowment for the Humanities, and Google. Jacob was a Postdoctoral researcher at Carnegie Mellon and the University of Illinois. He completed his Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation award. Jacob's research has been featured in the New York Times, National Public Radio, and the BBC. Thanks to his brief appearance in If These Knishes Could Talk, Jacob has a Bacon number of 2.

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Colloquium Speaker: Robert Calderbank, Tues Feb 14, 4:30pm-reminder
by Nicole E. Wagenblast 13 Feb '17

13 Feb '17

Joint talk with CS , PACM , CSML Robert Calderbank, Duke University Tuesday, February 14th- 4:30pm Computer Science 105 In this talk I will explore how data science is changing the way we practice education and research. My title Data+ is that of a ten-week summer research experience offered by the Information Institute at Duke (iiD) in which undergraduates join small project teams, working alongside other teams in a communal environment. They learn how to marshal, analyze, and visualize data, as well as how to communicate with the client sponsoring the project. The 2016 program involved about 70 undergraduate students and 30 graduate student and postdoctoral mentors working on 25 projects. I will describe projects that illuminate different ways in which student centered enquiry can transform university institutions. Our ambition for research is that it impacts society. We used to think of impact as starting with an idea, then developing that idea into a prototype, then turning the prototype into a product, then marketing the product, and so on – it is a long march and the problem with long marches is that most ideas don’t make it. I will describe an example of a different model, one where society is part of the research process. It is a collaboration with Apple on child mental health led by Guillermo Sapiro.

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Colloquium Speaker: Jacob Eisenstein, Thursday, February 16- 12:30pm
by Nicole E. Wagenblast 13 Feb '17

13 Feb '17

Colloquium Speaker Prof. Jacob Eisenstein, Georgia Institute of Technology Thursday, February 16, 12:30pm Computer Science 105 Host: Prof. Elad Hazan Putting language in context: social networks and discourse structures for robust language technology The creation of software that can use and understand natural human language would be a transformative technological advance, enabling computational inference to be brought to bear on the vast stores of knowledge that are encoded only as text. Supervised machine learning approaches have brought this possibility into view, but existing methods rely on annotated datasets that focus overwhelmingly on a narrow set of news texts, while failing to generalize to high-impact application domains such as social media and electronic health records. Human readers and listeners successfully comprehend language under difficult circumstances by relying on various forms of contextual information -- knowing who is speaking, and what they might be trying to say. My research brings this same contextual awareness to automated language processing, using deep learning architectures that are constructed to reflect theoretical ideas from sociolinguistics and discourse semantics. I will describe three applications of this methodology: (1) incorporating sociolinguistic variation into document classification; (2) linking textual references to canonicalized entities; (3) predicting the discourse relations that hold between sentences. I will also briefly describe research that uses computational linguistic analysis to obtain new evidence on sociocultural affinity and influence. Jacob Eisenstein is an Assistant Professor in the School of Interactive Computing at Georgia Tech. He works on statistical natural language processing, focusing on computational sociolinguistics, social media analysis, discourse, and machine learning. He is a recipient of the NSF CAREER Award, a member of the Air Force Office of Scientific Research (AFOSR) Young Investigator Program, and was a SICSA Distinguished Visiting Fellow at the University of Edinburgh. His work has also been supported by the National Institutes for Health, the National Endowment for the Humanities, and Google. Jacob was a Postdoctoral researcher at Carnegie Mellon and the University of Illinois. He completed his Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation award. Jacob's research has been featured in the New York Times, National Public Radio, and the BBC. Thanks to his brief appearance in If These Knishes Could Talk, Jacob has a Bacon number of 2.

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Cyber-Physical Systems in Aerospace Engineering
by Scott Karlin 11 Feb '17

11 Feb '17

Cyber-Physical Systems in Aerospace Engineering Eric Feron, Georgia Institute of Technology Monday, February 13, 2017, 3:30pm EQUAD Rm J223 The Cyber-Physical Systems (CPS) of today are, in principle, no different from the controlled systems of yesterday. However, what justifies the new name, especially in Aerospace Engineering, is the spectacular increase in available embedded computing power allowing the systems to offer a vastly expanded range of functionalities. In addition, the safety-critical or mission-critical nature of anything that aerospace engineering builds makes it very important that the systems be verified and validated every step of the way from concept to operation. However, on the one hand, software verification and validation (V&V) alone has been reported to burn in excess of 50% of the B787's development budget. On the other hand, the proponents of novel air vehicles, such as Amazon, Google, Zee, or A^3 by Airbus group, challenge the high cost of CPS development and would like to see a "lite" V&V approved by certification agencies. This talk will concentrate on the basic principles of Aerospace CPS development, indicating the traditional way such systems are specified, built, verified, and validated. The author's specific attempts at addressing the high cost of V&V, notably credible embedded software synthesis, will be highlighted along the way, but current needs and existing challenges will be equally emphasized, regardless of who ends up meeting them. The difficult question of certifying advanced autonomy algorithms will be discussed, along with the recent solutions proposed by the engineering and policy research community. Eric Feron received the B.S. degree from École Polytechnique, Palaiseau, France, the M.S. degree from the École Normale Supérieure, Paris, France, and the Ph.D. degree from Stanford University, Stanford, CA. He is the Dutton–Ducoffe Professor of Aerospace Software Engineering, Georgia Institute of Technology, Atlanta, GA and a consulting Professor of Applied Mathematics at École Nationale de l’Aviation Civile, Toulouse, France. Prior to that, he was with the faculty of the Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, for 12 years. His former research students are distributed throughout academia, government, and industry. He has published three books and several research papers. His research interests include using fundamental concepts of control systems, optimization, and computer science to address important problems in aerospace engineering such as agile control of unmanned aerial vehicles and multi-agent operations in air transportation systems, and aerospace cyber-physical system certification.

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Colloquium Speaker: Robert Calderbank, Tues Feb 14, 4:30pm
by Nicole E. Wagenblast 08 Feb '17

08 Feb '17

Joint talk with CS , PACM , CSML Robert Calderbank, Duke University Tuesday, February 14th- 4:30pm Computer Science 105 In this talk I will explore how data science is changing the way we practice education and research. My title Data+ is that of a ten-week summer research experience offered by the Information Institute at Duke (iiD) in which undergraduates join small project teams, working alongside other teams in a communal environment. They learn how to marshal, analyze, and visualize data, as well as how to communicate with the client sponsoring the project. The 2016 program involved about 70 undergraduate students and 30 graduate student and postdoctoral mentors working on 25 projects. I will describe projects that illuminate different ways in which student centered enquiry can transform university institutions. Our ambition for research is that it impacts society. We used to think of impact as starting with an idea, then developing that idea into a prototype, then turning the prototype into a product, then marketing the product, and so on – it is a long march and the problem with long marches is that most ideas don’t make it. I will describe an example of a different model, one where society is part of the research process. It is a collaboration with Apple on child mental health led by Guillermo Sapiro.

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ORFE Talk: The Implications of Privacy-Aware Choice, Today, 4:30pm
by Nicole E. Wagenblast 07 Feb '17

07 Feb '17

ORFE Colloquium Speaker Rachel Cummings, from California Institute of Technology Tuesday, February 7th- 4:30pm Sherrerd Hall, Room 101 The Implications of Privacy-Aware Choice Privacy concerns are becoming a major obstacle to using data in the way that we want. It's often unclear how current regulations should translate into technology, and the changing legal landscape surrounding privacy can cause valuable data to go unused. In addition, when people know that their current choices may have future consequences, they might modify their behavior to ensure that their data reveal less --- or perhaps, more favorable --- information about themselves. Given these concerns, how can we continue to make use of potentially sensitive data, while providing satisfactory privacy guarantees to the people whose data we are using? Answering this question requires an understanding of how people reason about their privacy and how privacy concerns affect behavior. In this talk, we will see how strategic and human aspects of privacy interact with existing tools for data collection and analysis. I will begin by adapting the standard model of consumer choice theory to a setting where consumers are aware of, and have preferences over, the information revealed by their choices. In this model of privacy-aware choice, I will show that little can be inferred about a consumer's preferences once we introduce the possibility that she has concerns about privacy, even when her preferences are assumed to satisfy relatively strong structural properties. Next, I will analyze how privacy technologies affect behavior in a simple economic model of data-driven decision making. Intuition suggests that strengthening privacy protections will both increase utility for the individuals providing data and decrease usefulness of the computation. I will demonstrate that this intuition can fail when strategic concerns affect consumer behavior. Finally, I'll discuss ongoing behavioral experiments, designed to empirically measure how people trade off privacy for money, and to test whether human behavior is consistent with theoretical models for the value of privacy.

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ORFE Talk: The Implications of Privacy-Aware Choice, Tuesday, Feb 7, 4:30pm- Reminder
by Nicole E. Wagenblast 06 Feb '17

06 Feb '17

ORFE Colloquium Speaker Rachel Cummings, from California Institute of Technology Tuesday, February 7th- 4:30pm Sherrerd Hall, Room 101 The Implications of Privacy-Aware Choice Privacy concerns are becoming a major obstacle to using data in the way that we want. It's often unclear how current regulations should translate into technology, and the changing legal landscape surrounding privacy can cause valuable data to go unused. In addition, when people know that their current choices may have future consequences, they might modify their behavior to ensure that their data reveal less --- or perhaps, more favorable --- information about themselves. Given these concerns, how can we continue to make use of potentially sensitive data, while providing satisfactory privacy guarantees to the people whose data we are using? Answering this question requires an understanding of how people reason about their privacy and how privacy concerns affect behavior. In this talk, we will see how strategic and human aspects of privacy interact with existing tools for data collection and analysis. I will begin by adapting the standard model of consumer choice theory to a setting where consumers are aware of, and have preferences over, the information revealed by their choices. In this model of privacy-aware choice, I will show that little can be inferred about a consumer's preferences once we introduce the possibility that she has concerns about privacy, even when her preferences are assumed to satisfy relatively strong structural properties. Next, I will analyze how privacy technologies affect behavior in a simple economic model of data-driven decision making. Intuition suggests that strengthening privacy protections will both increase utility for the individuals providing data and decrease usefulness of the computation. I will demonstrate that this intuition can fail when strategic concerns affect consumer behavior. Finally, I'll discuss ongoing behavioral experiments, designed to empirically measure how people trade off privacy for money, and to test whether human behavior is consistent with theoretical models for the value of privacy.

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