- talks - lists.cs.princeton.edu

Linguang Zhang will present his FPO, "Learning and Deploying Local Features" on Monday, 5/6/2019 at 1pm in CS 402.
by Nicki Mahler 23 Apr '19

23 Apr '19

Linguang Zhang will present his FPO, "Learning and Deploying Local Features" on Monday, 5/6/2019 at 1pm in CS 402. The members of his committee are as follows: Szymon Rusinkiewicz (adviser); Readers: Thomas Funkhouser and Olga Russakovsky; Nonreaders: Adam Finkelstein and Jia Deng. A copy of his thesis is available upon request. Everyone is invited to attend his talk. The talk abstract follows below: Many computer vision applications including image matching, image-based reconstruction and localization rely on extracting and matching robust local features. A typical local feature pipeline first detects repeatable keypoints in the image (i.e., keypoint detector), and then computes a short vector to uniquely describe each keypoint (i.e., feature descriptor). Both the keypoint detector and the feature descriptor are conventionally hand-crafted based on what is intuitive to the designer. For example, corners or blobs are popular choices of keypoints, and the image gradient is a useful clue for descriptors. However, it is often dicult to define these principles to accommodate various applications. In this thesis, we study data-driven approaches which can more easily tailor the local feature pipeline for target applications. We start with a mobile robotics application that leverages local features extracted from ground texture images to achieve high-precision global localization. The second part of the thesis addresses the problem that existing keypoint detectors that are optimized for natural images su↵er from sub-optimal performance on texture images. We therefore learn a keypoint detector specifically for each type of texture using a deep neural network. Our detector automatically learns to identify keypoints that are distinctive in the target texture rather than relying on a set of pre-defined rules. Finally, we focus on a non-parametric approach for learning feature descriptors. Many well-performing local feature descriptors are trained using a triplet loss that includes a tunable margin, which limits its ability to generalize to other types of data and problems. We propose to replace the hard margin with a soft margin that self-tunes as learning progresses. To summarize, we first demonstrate through a novel visual-based localization system where a customized local feature pipeline is critical. Then, we tackle both the keypoint detector and the feature descriptor with generalizable data-driven approaches.

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Microsoft's Bill Buxton speaking Monday, April 29
by Emily Lawrence 23 Apr '19

23 Apr '19

Bill Buxton, Principal Research at Microsoft, will be talking about creativity, innovation and design, especially design research and technology. Monday, April 29th at 4:30pm in the Friend Center Convocation Room. Open to the public. RSVP: https://www.tfaforms.com/4734441 BIO A Principal Researcher at Microsoft Research, Bill has had a 40- year involvement in research, practice and commentary around design, innovation and human aspects of technology. Following a 20-year career as a professional musician, he morphed into a researcher and interaction designer, at the University of Toronto, Xerox PARC, Alias Research and SGI Inc. He has been awarded four honourary doctorates, is co-recipient of an Academy Award for Scientific and Technical Achievement, received an ACM/SIGCHI Lifetime Achievement Award, and is a Fellow of the ACM. Bill has published, lectured and consulted widely, and is an Adjunct Professor at the University of Toronto, and a Distinguished Professor of Industrial Design at the Technical University Eindhoven. Other than his family, mountains and rivers are his first love. For more information, see billbuxton.com TALK TITLE & ABSTRACT The Buxton-Microsoft Collection: On the Importance of the History of the Past in Shaping the History of the Future History, backed up by current data, tells us that what we perceive as rapid technological change is generally due to a large number of technologies evolving slowly. For example, the recent history of information and telecommunications technologies says that it takes about 20-30 years for a technology, once identified, to reach maturity (defined, for example, as becoming a $1B industry). That says, for example, that anything which is going to become a $1B industry in the next 5 years most likely already has a 15 year history - a history which is generally below the radar, is nevertheless discoverable by those who know how and where to look. Over the past 45 years I have accumulated a collection of gadgets that captures the history of technological growth. For you, the value may just be nostalgia. However, my hope is this. In this age where the things which we produce create such a strong cultural impact, can we really afford not to take advantage of the learnings of the past hidden in plain sight? Beth Ellen Jarvie Communication Coordinator Keller Center School of Engineering and Applied Science Princeton University ACE 25 Engineering Quadrangle Princeton, NJ 08544 609-258-1643

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Yixin Sun will present her Pre FPO, "Enhancing Anonymity Systems under Network and User Dynamics" on April 24, 2019 at 3pm in CS 402
by Nicki Mahler 22 Apr '19

22 Apr '19

Yixin Sun will present her Pre FPO, "Enhancing Anonymity Systems under Network and User Dynamics" on April 24, 2019 at 3pm in CS 402. The members of her committee are as follows: Prateek Mittal (reader), Nick Feamster (reader), Mung Chiang (advisor), Jennifer Rexford (non-reader), Ed Felten (non-reader) Everyone is invited to attend her talk. The talk title and abstract follow below. Title: Enhancing Anonymity Systems under Network and User Dynamics Abstract: Privacy on the Internet is eroding rapidly. Anonymity systems have been designed to protect the identity and privacy of users from untrusted destinations and third parties on the Internet. However, many anonymity systems assume that important system components, such as the network infrastructure and client location, are static. In practice, these components are highly dynamic and exhibit changes over time. In this dissertation, I exploit network and user dynamics to uncover new attacks in anonymity systems, and consequently build defenses to protect user anonymity from such new attacks. First, we present Raptor attacks, that can be launched by Autonomous Systems (ASes) to compromise user anonymity in the Tor network. AS-level adversaries can exploit the asymmetric nature of Internet routing and launch active routing attacks to observe user traffic and deanonymize users via traffic correlation analysis. We successfully demonstrate the attacks in the wild with 95% accuracy rate. Second, we design the Counter-Raptor defense to safeguard Tor against active routing attacks via a new Tor guard relay selection algorithm that incorporates the resilience of relays into consideration. We show that the algorithm successfully improves the security for Tor clients by up to 36% on average (up to 166% for certain clients). Finally, we present Tempest attack that exploits client mobility to compromise anonymity. Using experimental analysis on real-world datasets, we demonstrate that mobility degrades user privacy across a wide range of anonymity systems, including deployed systems such as Tor; path-selection protocols for Tor such as DeNASA and Counter-RAPTOR; and network-layer anonymity protocols for Internet routing such as HORNET.

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Fwd: Rigetti QC Programming workshop: Friday April 19 at 1pm in CS Small Auditorium
by Margaret Martonosi 19 Apr '19

19 Apr '19

QC Programming workshop starting *now* in CS Small Auditorium! Join us! -mrm Margaret Martonosi H. T. Adams '35 Professor of Computer Science. Director, Keller Center for Innovation in Engineering Education Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm > Begin forwarded message: > > From: Margaret Martonosi <mrm(a)Princeton.EDU> > Subject: Rigetti QC Programming workshop: Friday April 19 at 1pm in CS Small Auditorium > Date: April 12, 2019 at 7:55:20 AM EDT > To: quantum-list(a)Princeton.EDU > Reply-To: Margaret Martonosi <mrm(a)Princeton.EDU> > > HI All- > > Rigetti will be here next week to give a QC Programming tutorial. CS Small auditorium at 1-3pm on Friday April 19. Be there! (More details below.) > > Margaret Martonosi > H. T. Adams '35 Professor of Computer Science. > Director, Keller Center for Innovation in Engineering Education > Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm > > >> Begin forwarded message: >> >> From: Amy Brown <amy(a)rigetti.com> >> Subject: Re: Quantum Computing Workshop at Princeton >> >> >> LEARN HOW TO PROGRAM A QUANTUM COMPUTER >> Learn about quantum computers and how to program them using Rigetti's pyQuil quantum programming language, explained by Amy Brown, a Rigetti Quantum Solutions Engineer. A working knowledge of complex numbers, matrix arithmetic, and programming in Python is all that is required. The workshop will be held on Friday, April 19 at 1pm in CS Small Auditorium. Follow the instructions at this link to get set up with pyQuil, the Forest SDK, and the notebooks that will be used in the workshop: https://github.com/amyfbrown/pyQuil-workshop-notebooks/blob/master/README.md >> >> Looking forward to it, >> Amy >> >> >> Amy Brown, B.S. Physics >> Quantum Solutions Engineer >> amy(a)rigetti.com >> +1 (914) 924-4317 > > > Margaret Martonosi > H. T. Adams '35 Professor of Computer Science. > Director, Keller Center for Innovation in Engineering Education > Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm

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Ge Wang gives Evnin Lecture tonight 7pm in Friend 101
by Adam Finkelstein 18 Apr '19

18 Apr '19

Ge Wang, Princeton COS PhD and one of the founding members of the Princeton Laptop Orchestra, is giving the Evnin Lecture tonight 7pm in Friend 101: https://www.princeton.edu/events/2019/evnin-lecture-artful-design-technolog…

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EE Seminar Series: NOW in B205-Vivienne Sze, MIT
by Emily Lawrence 18 Apr '19

18 Apr '19

EE SEMINAR SERIES Speaker: Vivienne Sze, MIT Title: Efficient Computing for AI and Robotics Day: Thursday, April 18, 2019 Time: 4:30pm Room: B205 Engineering Quadrangle Host: Prof. Verma and Prof. Wentzlaff Abstract: Computing near the sensor is preferred over the cloud due to privacy and/or latency concerns for a wide range of applications including robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to the throughput and accuracy requirements of the application. In this talk, we will describe how joint algorithm and hardware design can be used to reduce energy consumption while delivering real-time and robust performance for applications including deep learning, computer vision, autonomous navigation/exploration and video/image processing. We will show how energy-efficient techniques that exploit correlation and sparsity to reduce compute, data movement and storage costs can be applied to various tasks including image classification, depth estimation, super-resolution, localization and mapping. Bio: Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department. Her research interests include energy-aware signal processing algorithms, and low-power circuit and system design for portable multimedia applications, including computer vision, deep learning, autonomous navigation, and video process/coding. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she designed low-power algorithms and architectures for video coding. She also represented TI in the JCT-VC committee of ITU-T and ISO/IEC standards body during the development of High Efficiency Video Coding (HEVC), which received a Primetime Engineering Emmy Award. She is a co-editor of the book entitled "High Efficiency Video Coding (HEVC): Algorithms and Architectures" (Springer, 2014). Prof. Sze received the B.A.Sc. degree from the University of Toronto in 2004, and the S.M. and Ph.D. degree from MIT in 2006 and 2010, respectively. In 2011, she received the Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT. She is a recipient of the 2018 Facebook Faculty Award, the 2017 Qualcomm Faculty Award, the 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Research Program (YIP) Award, the 2016 3M Non-Tenured Faculty Award, the 2014 DARPA Young Faculty Award, the 2007 DAC/ISSCC Student Design Contest Award, and a co-recipient of the 2017 CICC Outstanding Invited Paper Award, the 2016 IEEE Micro Top Picks Award and the 2008 A-SSCC Outstanding Design Award. This seminar is supported with funds from the Korhammer Lecture Series

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QC Programming Workshop: Friday April 19 at 1-3pm in CS Small Auditorium
by Margaret Martonosi 18 Apr '19

18 Apr '19

Hello all- Engineers from Rigetti (a quantum computing startup) will be here *TOMORROW* Friday April 19 to lead a 2-hour QC programming workshop. 1-3pm in the CS Small Auditorium. All are welcome to join and learn! (More details below.) -mrm Margaret Martonosi H. T. Adams '35 Professor of Computer Science. Director, Keller Center for Innovation in Engineering Education Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm >> Begin forwarded message: >> >> From: Amy Brown <amy(a)rigetti.com> >> Subject: Re: Quantum Computing Workshop at Princeton >> >> >> LEARN HOW TO PROGRAM A QUANTUM COMPUTER >> Learn about quantum computers and how to program them using Rigetti's pyQuil quantum programming language, explained by Amy Brown, a Rigetti Quantum Solutions Engineer. A working knowledge of complex numbers, matrix arithmetic, and programming in Python is all that is required. The workshop will be held on Friday, April 19 at 1pm in CS Small Auditorium. Follow the instructions at this link to get set up with pyQuil, the Forest SDK, and the notebooks that will be used in the workshop: https://github.com/amyfbrown/pyQuil-workshop-notebooks/blob/master/README.md >> >> Looking forward to it, >> Amy >> >> >> Amy Brown, B.S. Physics >> Quantum Solutions Engineer >> amy(a)rigetti.com >> +1 (914) 924-4317 > > > Margaret Martonosi > H. T. Adams '35 Professor of Computer Science. > Director, Keller Center for Innovation in Engineering Education > Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm > > > > > > Begin forwarded message: > > From: Margaret Martonosi <mrm(a)Princeton.EDU> > Subject: Rigetti QC Programming workshop: Friday April 19 at 1pm in CS Small Auditorium > Date: April 12, 2019 at 7:55:20 AM EDT > To: quantum-list(a)Princeton.EDU > Reply-To: Margaret Martonosi <mrm(a)Princeton.EDU> > > HI All- > > Rigetti will be here next week to give a QC Programming tutorial. CS Small auditorium at 1-3pm on Friday April 19. Be there! (More details below.) > > Margaret Martonosi > H. T. Adams '35 Professor of Computer Science. > Director, Keller Center for Innovation in Engineering Education > Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm > > >> Begin forwarded message: >> >> From: Amy Brown <amy(a)rigetti.com> >> Subject: Re: Quantum Computing Workshop at Princeton >> >> >> LEARN HOW TO PROGRAM A QUANTUM COMPUTER >> Learn about quantum computers and how to program them using Rigetti's pyQuil quantum programming language, explained by Amy Brown, a Rigetti Quantum Solutions Engineer. A working knowledge of complex numbers, matrix arithmetic, and programming in Python is all that is required. The workshop will be held on Friday, April 19 at 1pm in CS Small Auditorium. Follow the instructions at this link to get set up with pyQuil, the Forest SDK, and the notebooks that will be used in the workshop: https://github.com/amyfbrown/pyQuil-workshop-notebooks/blob/master/README.md >> >> Looking forward to it, >> Amy >> >> >> Amy Brown, B.S. Physics >> Quantum Solutions Engineer >> amy(a)rigetti.com >> +1 (914) 924-4317 > > > Margaret Martonosi > H. T. Adams '35 Professor of Computer Science. > Director, Keller Center for Innovation in Engineering Education > Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm

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Diana Cai will present her General Exam on Thursday, April 25, 2019 at 12:30pm in CS 105
by Nicki Mahler 18 Apr '19

18 Apr '19

Diana Cai will present her General Exam on Thursday, April 25, 2019 at 12:30pm in CS 105. The members of her committee are: Ryan Adams (adviser), Barbara Engelhardt (adviser), and Tom Griffiths. Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. Title: Robust and Reliable Probabilistic Modeling Abstract: Probabilistic modeling is a pillar of modern statistical data analysis and has proved to be particularly salient for extracting interpretable latent structure in scientific and engineering applications, and the resulting inferences are an important part of decision making. Thus, it increasingly important to develop models that are interpretable, robust, deployable at scale. Robust probabilistic modeling is an important problem, as the stability of the model’s inferences directly impacts the decision making process. A natural way of providing robustness is to use a flexible modeling framework that encompasses a wide class of models. Bayesian nonparametric modeling provides a flexible probabilistic framework for learning from data, where the number of parameters of the model grows with the number of observations. We discuss a few applications of Bayesian nonparametrics, including for sparse network modeling and a Bayesian interpretation of the count-min sketch. Lastly, we outline a few research directions in robust and reliable probabilistic modeling, including probabilistic modeling under model misspecification and inference under computational constraints. Reading list: Bishop. Pattern Recognition and Machine Learning. Springer, 2006. Ferguson. A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1973. Blackwell and MacQueen. Ferguson Distributions via Polya Urn Schemes. Annals of Statistics, 1973. Broderick, Jordan, and Pitman. Beta processes, stick breaking, and power laws. Bayesian Analysis, 2012. Caron and Fox. Sparse graphs using exchangeable random measures. Journal of the Royal Statistical Society: Series B, 2017. Cormode and Muthukrishnan. An Improved Data Stream Summary: The Count-Min Sketch and its Applications. Journal of Algorithms, 2005. Miller and Harrison. A simple example of Dirichlet process mixture inconsistency for the number of components. NeurIPS, 2013. Miller and Harrison. Mixture models with a prior on the number of components. Journal of the American Statistical Association, 2018. Miller and Dunson. Robust Bayesian modeling via coarsening. Journal of the American Statistical Association, 2018. Hennig. Probabilistic interpretation of linear solvers. SIAM Journal on Optimization, 2015. Cockayne, Oates, Ipsen, Girolami. A Bayesian Conjugate Gradient Method. Bayesian Analysis, 2019.

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Colloquium Speaker-James Bornholt, TODAY at 12:30pm
by Emily C. Lawrence 18 Apr '19

18 Apr '19

Colloquium Speaker James Bornholt from University of Washington Thursday, April 18 - 12:30pm Computer Science - Room 105 Host: Margaret Martonosi [ https://www.cs.princeton.edu/events/25821 | https://www.cs.princeton.edu/events/25821 ] Optimizing the Automated Programming Stack The scale and pervasiveness of modern software poses a challenge for programmers: software reliability is more important than ever, but the complexity of computer systems continues to grow. Automated programming tools are powerful weapons for programmers to tackle this challenge: verifiers that check software correctness, and synthesizers that generate new correct-by-construction programs. These tools are most effective when they apply domain-specific optimizations, but doing so today requires considerable formal methods expertise. In this talk, I present a new application-driven approach to optimizing the automated programming stack underpinning modern domain-specific tools. I will demonstrate the importance of programming tools in the context of memory consistency models, which define the behavior of multiprocessor CPUs and whose subtleties often elude even experts. Our new tool, MemSynth , automatically synthesizes formal descriptions of memory consistency models from examples of CPU behavior. We have used MemSynth to synthesize descriptions of the x86 and PowerPC memory models, each of which previously required person-years of effort to describe by hand, and found several ambiguities and underspecifications in both architectures. I will then present symbolic profiling, a new technique we designed and implemented to help people identify the scalability bottlenecks in automated programming tools. These tools use symbolic evaluation, which evaluates all paths through a program, and is an execution model that defies both human intuition and standard profiling techniques. Symbolic profiling diagnoses scalability bottlenecks using a novel performance model for symbolic evaluation that accounts for all-paths execution. We have used symbolic profiling to find and fix performance issues in 8 state-of-the-art automated tools, improving their scalability by orders of magnitude, and our techniques have been adopted in industry. Finally, I will give a sense of the importance of future application-driven optimizations to the automated programming stack, with applications that inspire improvements to the stack and in turn beget even more powerful automated tools. James Bornholt is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Emina Torlak , Dan Grossman, and Luis Ceze . His research interests are in programming languages and formal methods, with a focus on automated program verification and synthesis. His work has received an ACM SIGPLAN Research Highlight, two IEEE Micro Top Picks selections, an OSDI best paper award, and a Facebook Ph.D. fellowship.

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Reminder: Colloquium Speaker-James Bornholt, tomorrow at 12:30pm
by Emily C. Lawrence 17 Apr '19

17 Apr '19

Colloquium Speaker James Bornholt from University of Washington Thursday, April 18 - 12:30pm Computer Science - Room 105 Host: Margaret Martonosi [ https://www.cs.princeton.edu/events/25821 | https://www.cs.princeton.edu/events/25821 ] Optimizing the Automated Programming Stack The scale and pervasiveness of modern software poses a challenge for programmers: software reliability is more important than ever, but the complexity of computer systems continues to grow. Automated programming tools are powerful weapons for programmers to tackle this challenge: verifiers that check software correctness, and synthesizers that generate new correct-by-construction programs. These tools are most effective when they apply domain-specific optimizations, but doing so today requires considerable formal methods expertise. In this talk, I present a new application-driven approach to optimizing the automated programming stack underpinning modern domain-specific tools. I will demonstrate the importance of programming tools in the context of memory consistency models, which define the behavior of multiprocessor CPUs and whose subtleties often elude even experts. Our new tool, MemSynth , automatically synthesizes formal descriptions of memory consistency models from examples of CPU behavior. We have used MemSynth to synthesize descriptions of the x86 and PowerPC memory models, each of which previously required person-years of effort to describe by hand, and found several ambiguities and underspecifications in both architectures. I will then present symbolic profiling, a new technique we designed and implemented to help people identify the scalability bottlenecks in automated programming tools. These tools use symbolic evaluation, which evaluates all paths through a program, and is an execution model that defies both human intuition and standard profiling techniques. Symbolic profiling diagnoses scalability bottlenecks using a novel performance model for symbolic evaluation that accounts for all-paths execution. We have used symbolic profiling to find and fix performance issues in 8 state-of-the-art automated tools, improving their scalability by orders of magnitude, and our techniques have been adopted in industry. Finally, I will give a sense of the importance of future application-driven optimizations to the automated programming stack, with applications that inspire improvements to the stack and in turn beget even more powerful automated tools. James Bornholt is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Emina Torlak , Dan Grossman, and Luis Ceze . His research interests are in programming languages and formal methods, with a focus on automated program verification and synthesis. His work has received an ACM SIGPLAN Research Highlight, two IEEE Micro Top Picks selections, an OSDI best paper award, and a Facebook Ph.D. fellowship.

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