February 2015 - talks - lists.cs.princeton.edu

Friday March 6: 1:30-2:30 talk on Networks of Networks of Quantum Repeaters
by Margaret Martonosi 27 Feb '15

27 Feb '15

Speaker: Rodney Van Meter, Keio University Title: Networks of Networks of Quantum Repeaters Date/time: Friday March 6, 1:30-2:30pm. Location: CS 401. Abstract: Experimental progress toward quantum repeaters is moving at a tremendous rate, and theorists have proposed half a dozen approaches to managing errors to create high-fidelity entanglement along a chain of repeaters. The next frontier is extending from one-dimensional chains to complex topologies. Problems in network engineering include robust protocol design and resource management. I will give an overview of these issues, then discuss the even more daunting challenge of creating networks of networks -- a true quantum Internet -- capable of coupling networks that are heterogeneous in both physical technology and error management scheme. Bio: Rodney Van Meter received a B.S. in engineering and applied science from the California Institute of Technology in 1986, an M.S. in computer engineering from the University of Southern California in 1991, and a Ph.D. in computer science from Keio University in 2006. His current research centers on quantum computer architecture and quantum networking. Other research interests include storage systems, networking, and post-Moore's Law computer architecture. He is now an Associate Professor of Environment and Information Studies at Keio University's Shonan Fujisawa Campus. Dr. Van Meter is a member of AAAS, ACM and IEEE. Host: Margaret Martonosi (contact me if you’d like to get on his one-on-one schedule.) Margaret Martonosi H. T. Adams '35 Professor of Computer Science. Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm

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Colloquium Speaker Sarah Loos Tues March 3, 12:30pm
by Nicole E. Wagenblast 26 Feb '15

26 Feb '15

Colloquium Speaker Sarah Loos, from Carnegie Mellon University Tuesday, March 3, 12:30pm Computer Science 105 Verifying distributed car and aircraft systems with logic and refinement Formal methods and theorem proving have been used successfully in many discrete applications, such as chip design and verification, but computation is not confined to the discrete world. Increasingly, we depend on discrete software to control safety-critical components of continuous physical systems. It is vital that these hybrid (discrete and continuous) systems behave as designed, or they will cause more damage than they intend to fix. In this talk, I will present several challenges associated with verifying hybrid systems and how we use differential dynamic logics to ensure safety for hybrid systems under a continuously infinite range of circumstances and unbounded time horizon. I will discuss the first formal proof of safety for a distributed car control system and the first formal verification of distributed, flyable, collision avoidance protocols for aircraft. In both cases, our verification holds for an unbounded number of cars or aircraft, so the systems are protected against unexpected emergent behavior, which simulation and testing may miss. Providing the first formal proofs of these systems required the development of several seemingly unrelated techniques. We have since identified a unifying challenge for each case study: it is difficult to compare hybrid systems, even when their behaviors are very similar. I will discuss the development of a logic with first-class support for refinement relations on hybrid systems, called differential refinement logic, which aims to address this core challenge for hybrid systems verification within a formal framework. Sarah Loos is a Ph.D. student in the computer science department at Carnegie Mellon University. Her research interests include logical analysis and formal verification of distributed hybrid systems, such as distributed car control and collision avoidance protocols for aircraft. She is a Department of Energy computational science graduate fellow and an NSF graduate research fellow. In addition to her role as co-editor of the ACM-W newsletter, Sarah has served as a student member on the board of trustees and board of advisors for the Anita Borg Institute for Women and Technology. She currently serves on the board of directors for the Foundation for Learning Equality, a non-profit tech company dedicated to bringing the online learning revolution to communities with little or no internet connectivity. Sarah received her undergraduate B.S. degrees in computer science and math from Indiana University.

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Siyu Yang will present his Pre-FPO on March 3, 2015 at 3pm in Room 401.
by Nicki Gotsis 26 Feb '15

26 Feb '15

Siyu Yang will present his Pre-FPO on March 3, 2015 at 3pm in Room 401. His committee members are Professor Robert Tarjan (advisor), Prof Robert Sedgewick (reader), Dr. David Lee (non-Princeton reader), Prof Andrea LaPaugh (non-reader), Prof Bernard Chazelle (non-reader). Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Eager and Lazy Deletion Rebalancing in Binary Search Trees The binary search tree is a fundamental data structure with many applications. Following the invention of AVL tree, many kinds of balanced search trees were developed, with the aim of obtaining simpler updating or improved efficiency or both. In most of such data structures, deletion rebalancing is more complicated than insertion rebalancing. This leads naturally to the question of how much rebalancing is necessary to guarantee a logarithmic height bound. We explore ways to make binary search trees more efficient by delaying rebalancing on deletion. This talk will focus on variants of AVL trees. We propose a variant of weak AVL trees with an even weaker balance condition. Our trees improved the amortized time bounds for rebalancing steps while keeping the same worst-case height guarantee as weak AVL trees. We also propose another variant with lazy rebalancing steps separated from deletion operations themselves. The algorithm's performance is guaranteed by running a background process that rebalances unbalanced parts of the tree. Unlike the similar work of relaxed AVL trees, in which the tree only eventually becomes balanced, our method guarantees that the tree remains balanced at all times, if the background process is run sufficiently often. Finally, I will summarize with some ongoing work.

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Colloquium Speaker Joseph Gonzalez Today, 12:30pm
by Nicole E. Wagenblast 26 Feb '15

26 Feb '15

Colloquium Speaker Joseph Gonzalez, University of California Thursday, February 26, 12:30pm Computer Science 105 Learning Systems: Systems and Abstractions for Large-Scale Machine Learning The challenges of advanced analytics and big data cannot be address by developing new machine learning algorithms or new computing systems in isolation. Some of the recent advances in machine learning have come from new systems that can apply complex models to big data problems. Likewise, some of the recent advances in systems have exploited fundamental properties in machine learning to reach new points in the system design space. By considering the design of scalable learning systems from both perspectives, we can address bigger problems, expose new opportunities in algorithm and system design, and define the new fundamental abstractions that will accelerate research in these complementary fields. In this talk, I will present my research in learning systems spanning the design of efficient inference algorithms, the development of graph processing systems, and the unification of graphs and unstructured data. I will describe how the study of graphical model inference and power-law graph structure shaped the common abstractions in contemporary graph processing systems, and how new insights in system design enabled order-of-magnitude performance gains over general purpose data-processing systems. I will then discuss how lessons learned in the context of specialized graph-processing systems can be lifted to more general data-processing systems enabling users to view data as graph and tables interchangeably while preserving the performance gains of specialized systems. Finally, I will present a new direction for the design of learning systems that looks beyond traditional analytics and model fitting to the entire machine learning life-cycle spanning model training, serving, and management. Joseph Gonzalez is a postdoc in the UC Berkeley AMPLab and cofounder of GraphLab. Joseph received his PhD from the Machine Learning Department at Carnegie Mellon University where he worked with Carlos Guestrin on parallel algorithms and abstractions for scalable probabilistic machine learning. Joseph is a recipient of the AT&T Labs Graduate Fellowship and the NSF Graduate Research Fellowship.

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Colloquium Speaker Joseph Gonzalez Thurs Feb 26, 12:30pm
by Nicole E. Wagenblast 25 Feb '15

25 Feb '15

Colloquium Speaker Joseph Gonzalez, University of California Thursday, February 26, 12:30pm Computer Science 105 Learning Systems: Systems and Abstractions for Large-Scale Machine Learning The challenges of advanced analytics and big data cannot be address by developing new machine learning algorithms or new computing systems in isolation. Some of the recent advances in machine learning have come from new systems that can apply complex models to big data problems. Likewise, some of the recent advances in systems have exploited fundamental properties in machine learning to reach new points in the system design space. By considering the design of scalable learning systems from both perspectives, we can address bigger problems, expose new opportunities in algorithm and system design, and define the new fundamental abstractions that will accelerate research in these complementary fields. In this talk, I will present my research in learning systems spanning the design of efficient inference algorithms, the development of graph processing systems, and the unification of graphs and unstructured data. I will describe how the study of graphical model inference and power-law graph structure shaped the common abstractions in contemporary graph processing systems, and how new insights in system design enabled order-of-magnitude performance gains over general purpose data-processing systems. I will then discuss how lessons learned in the context of specialized graph-processing systems can be lifted to more general data-processing systems enabling users to view data as graph and tables interchangeably while preserving the performance gains of specialized systems. Finally, I will present a new direction for the design of learning systems that looks beyond traditional analytics and model fitting to the entire machine learning life-cycle spanning model training, serving, and management. Joseph Gonzalez is a postdoc in the UC Berkeley AMPLab and cofounder of GraphLab. Joseph received his PhD from the Machine Learning Department at Carnegie Mellon University where he worked with Carlos Guestrin on parallel algorithms and abstractions for scalable probabilistic machine learning. Joseph is a recipient of the AT&T Labs Graduate Fellowship and the NSF Graduate Research Fellowship.

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Colloquium Speaker Suman Jana today, 12:30pm
by Nicole E. Wagenblast 25 Feb '15

25 Feb '15

Colloquium Speaker Suman Jana, Stanford University Wednesday, February 25, 2015 - 12:30pm Computer Science 105 Rise of the Planet of the Apps: Security and Privacy in the Age of Bad Code Computing is undergoing a major shift. Third-party applications hosted in online software markets have become ubiquitous on all kinds of platforms: mobile phones, Web browsers, gaming devices, even household robots. These applications often include yet more third-party code for advertising, analytics, etc. These trends have dramatically increased the amount of bad code throughout the software stack - buggy code, malicious code, code that overcollects private information intentionally or by accident, overprivileged code vulnerable to abuse - as well as the amount of sensitive data processed by bad code. In this talk, I will demonstrate that existing application platforms are ill-suited to dealing with bad code, thus causing security and privacy problems. I will then show how to isolate bad code without affecting its useful functionality, by redesigning the interfaces across the software stack and controlling the information released to the applications by the platform. I will also show how automated testing can identify bad code and help developers improve their applications. Suman Jana is a postdoctoral researcher at Stanford University. He earned his PhD in 2014 from the University of Texas, where he was supported by the Google PhD Fellowship. He is broadly interested in identifying fundamental flaws in existing systems and building new systems with strong security and privacy guarantees. Suman received the 2014 PET Award for Outstanding Research in Privacy-Enhancing Technologies, Best Practical Paper Award from the 2014 IEEE Symposium on Security and Privacy (Oakland), Best Student Paper Award from the 2012 IEEE Symposium on Security and Privacy, and the 2012 Best Applied Security Paper Award. Suman's research has been widely covered in popular media, and his code has been deployed at Google, Mozilla, and Apache.

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Correction- Colloquium Speaker Joseph Gonzalez Thurs Feb 26, 12:30pm
by Nicole E. Wagenblast 24 Feb '15

24 Feb '15

Colloquium Speaker Joseph Gonzalez, University of California Thursday, February 26, 12:30pm Computer Science 105 Learning Systems: Systems and Abstractions for Large-Scale Machine Learning The challenges of advanced analytics and big data cannot be address by developing new machine learning algorithms or new computing systems in isolation. Some of the recent advances in machine learning have come from new systems that can apply complex models to big data problems. Likewise, some of the recent advances in systems have exploited fundamental properties in machine learning to reach new points in the system design space. By considering the design of scalable learning systems from both perspectives, we can address bigger problems, expose new opportunities in algorithm and system design, and define the new fundamental abstractions that will accelerate research in these complementary fields. In this talk, I will present my research in learning systems spanning the design of efficient inference algorithms, the development of graph processing systems, and the unification of graphs and unstructured data. I will describe how the study of graphical model inference and power-law graph structure shaped the common abstractions in contemporary graph processing systems, and how new insights in system design enabled order-of-magnitude performance gains over general purpose data-processing systems. I will then discuss how lessons learned in the context of specialized graph-processing systems can be lifted to more general data-processing systems enabling users to view data as graph and tables interchangeably while preserving the performance gains of specialized systems. Finally, I will present a new direction for the design of learning systems that looks beyond traditional analytics and model fitting to the entire machine learning life-cycle spanning model training, serving, and management. Joseph Gonzalez is a postdoc in the UC Berkeley AMPLab and cofounder of GraphLab. Joseph received his PhD from the Machine Learning Department at Carnegie Mellon University where he worked with Carlos Guestrin on parallel algorithms and abstractions for scalable probabilistic machine learning. Joseph is a recipient of the AT&T Labs Graduate Fellowship and the NSF Graduate Research Fellowship.

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Colloquium Speaker Joseph Gonzalez Thurs Feb 24, 12:30pm
by Nicole E. Wagenblast 24 Feb '15

24 Feb '15

Colloquium Speaker Joseph Gonzalez, University of California Thursday, February 24, 12:30pm Computer Science 105 Learning Systems: Systems and Abstractions for Large-Scale Machine Learning The challenges of advanced analytics and big data cannot be address by developing new machine learning algorithms or new computing systems in isolation. Some of the recent advances in machine learning have come from new systems that can apply complex models to big data problems. Likewise, some of the recent advances in systems have exploited fundamental properties in machine learning to reach new points in the system design space. By considering the design of scalable learning systems from both perspectives, we can address bigger problems, expose new opportunities in algorithm and system design, and define the new fundamental abstractions that will accelerate research in these complementary fields. In this talk, I will present my research in learning systems spanning the design of efficient inference algorithms, the development of graph processing systems, and the unification of graphs and unstructured data. I will describe how the study of graphical model inference and power-law graph structure shaped the common abstractions in contemporary graph processing systems, and how new insights in system design enabled order-of-magnitude performance gains over general purpose data-processing systems. I will then discuss how lessons learned in the context of specialized graph-processing systems can be lifted to more general data-processing systems enabling users to view data as graph and tables interchangeably while preserving the performance gains of specialized systems. Finally, I will present a new direction for the design of learning systems that looks beyond traditional analytics and model fitting to the entire machine learning life-cycle spanning model training, serving, and management. Joseph Gonzalez is a postdoc in the UC Berkeley AMPLab and cofounder of GraphLab. Joseph received his PhD from the Machine Learning Department at Carnegie Mellon University where he worked with Carlos Guestrin on parallel algorithms and abstractions for scalable probabilistic machine learning. Joseph is a recipient of the AT&T Labs Graduate Fellowship and the NSF Graduate Research Fellowship.

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Colloquium Speaker Suman Jana Wed Feb 25, 12:30pm
by Nicole E. Wagenblast 24 Feb '15

24 Feb '15

Colloquium Speaker Suman Jana, Stanford University Wednesday, February 25, 2015 - 12:30pm Computer Science 105 Rise of the Planet of the Apps: Security and Privacy in the Age of Bad Code Computing is undergoing a major shift. Third-party applications hosted in online software markets have become ubiquitous on all kinds of platforms: mobile phones, Web browsers, gaming devices, even household robots. These applications often include yet more third-party code for advertising, analytics, etc. These trends have dramatically increased the amount of bad code throughout the software stack - buggy code, malicious code, code that overcollects private information intentionally or by accident, overprivileged code vulnerable to abuse - as well as the amount of sensitive data processed by bad code. In this talk, I will demonstrate that existing application platforms are ill-suited to dealing with bad code, thus causing security and privacy problems. I will then show how to isolate bad code without affecting its useful functionality, by redesigning the interfaces across the software stack and controlling the information released to the applications by the platform. I will also show how automated testing can identify bad code and help developers improve their applications. Suman Jana is a postdoctoral researcher at Stanford University. He earned his PhD in 2014 from the University of Texas, where he was supported by the Google PhD Fellowship. He is broadly interested in identifying fundamental flaws in existing systems and building new systems with strong security and privacy guarantees. Suman received the 2014 PET Award for Outstanding Research in Privacy-Enhancing Technologies, Best Practical Paper Award from the 2014 IEEE Symposium on Security and Privacy (Oakland), Best Student Paper Award from the 2012 IEEE Symposium on Security and Privacy, and the 2012 Best Applied Security Paper Award. Suman's research has been widely covered in popular media, and his code has been deployed at Google, Mozilla, and Apache.

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Colloquium Speaker Matt Fredrikson, Today, February 24, 4:30pm
by Nicole E. Wagenblast 24 Feb '15

24 Feb '15

Colloquium Speaker Matt Fredrikson, University of Wisconsin, Madison Tuesday, February 24, 4:30pm Computer Science 105 Inference Attacks: Understanding Privacy in the Era of "Privacy is Dead" As data from far-reaching sources is collected, aggregated, and re-packaged to enable new and smarter applications, confidentiality and data security are at greater risk than ever before. Some of the most surprising and invasive threats to materialize in recent years are brought about by so-called inference attacks: successful attempts to learn sensitive information by leveraging public data such as social network updates, published research articles, and web APIs. In this talk, I will focus on two of my research efforts to better understand and defend against these attacks. First I will discuss work that examines the privacy risks that arise when machine learning models are used in a popular medical application, and illustrate the consequences of applying differential privacy as a defense. This work uncovered a new type of inference attack on machine learning models, and shows via an in-depth case study how to understand privacy "in situ" by balancing the attacker's chance of success against the likelihood of harmful medical outcomes. The second part of the talk will detail work that helps developers correctly write privacy-aware applications using verification tools. I will illustrate how a wide range of confidentiality guarantees can be framed in terms of a new logical primitive called Satisfiability Modulo Counting, and describe a tool that I have developed around this primitive that automatically finds privacy bugs in software (or proves that the software is bug-free). Through a better understanding of how proposed defenses impact real applications, and by providing tools that help developers implement the correct defense for their task, we can begin to proactively identify potential threats to privacy and take steps to ensure that they will not surface in practice. Matt Fredrikson is a Ph.D. candidate in the department of Computer Sciences at the University of Wisconsin-Madison. His research interests lie at the intersection of security, privacy, and formal methods, covering topics in software security, privacy issues associated with machine-learning models, and applied cryptography. His work has been profiled by Reuters, Technology Review, and New Scientist, and received the best paper award at USENIX Security 2014. He is a recipient of the Microsoft Research Graduate Fellowship Award.

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