December 2018 - talks - lists.cs.princeton.edu

Elena Balashova will present her Pre FPO "Structure-Aware Shape Synthesis" on December 13, 2018 at 12:30pm in CS 105.
by Nicki Gotsis 07 Dec '18

07 Dec '18

Elena Balashova will present her Pre FPO "Structure-Aware Shape Synthesis" on December 13, 2018 at 12:30pm in CS 105. The members of her committee are as follows: Tom Funkhouser (adviser), Szymon Rusinkiewicz, Vivek Singh (Siemens), Adam Finkelstein, Olga Russakovsky All are invited to attend. Abstract follows below. Abstract: Shape synthesis is an important area of computer vision and graphics that concerns creation of new shapes and reconstruction from partial data. Its goal is to learn a model that can generate shapes within an object category suitable for novel shape creation, interpolation, completion, editing, and other geometric modeling applications. Existing tools learn shape properties from large collections of shapes. Although these methods have been very successful at learning how to synthesize the coarse shapes of objects in categories with highly diverse shapes, they have not always produced examples that reconstruct important structural elements of a shape. In this talk, I describe how structure can be incorporated into the synthesis process, and how it can be used to improve generative models. I discuss how the specific properties of the data influence the effect and design of chosen structure. First, I introduce a template-defined skeleton structure for learning a part-aware generative model in typography, where the shapes have a known structure and can be explained by a small number of templates. Next, I describe a semantic landmark-based structure and how it can be used to improve a generative model of examples with extremely varied topology by means of a geometric shape-structure consistency loss. Finally, I present a scenario of noisy archaeological wall painting (fresco) reconstruction from eroded fragments, where there is no well-defined structure and exponentially many arrangement possibilities – in this case, I show that structure can inferred and assembled through consistency of arrangement

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Colloquium Speaker-Thomas Anderson TODAY at 12:30 and 4:30
by Emily C. Lawrence 06 Dec '18

06 Dec '18

Colloquium Speaker Thomas Anderson from the University of Washington Giving TWO TALKS on Thursday, December 6 Thursday, December 6 - 12:30pm Computer Science - Room 105 “High Performance Operating Systems in the Data Center” Abstract: The ongoing shift of enterprise computing to the cloud provides an opportunity to rethink operating systems for this new setting. I will discuss two specific technologies, kernel bypass for high performance networking and low latency non-volatile storage, and their implications for operating system design. In each case, delivering the performance of the underlying hardware requires novel approaches to the division of labor between hardware, the operating system kernel, and the application library. Thursday, December 6 - 4:30pm Computer Science - Room 104 “A Case for An Open Source CS Curriculum” Abstract: Despite rapidly increasing enrollment in CS courses, the academic CS community is failing to keep pace with demand for trained CS students, leading to escalating starting salaries for our students. Further, the knowledge of how to teach students up to the state of the art is increasingly segregated into a small cohort of schools who mostly cater to students from families in the top 10% of the income distribution. Even in the best case, those schools lack the aggregate capacity to teach more than a small fraction of the nation's need for engineers and computer scientists. MOOCs can help, but they are mainly effective at retraining existing college graduates. In practice, most low and middle income students need a human teacher. In this talk I argue for building an open source CS curriculum, with autograded projects, instructional software, textbooks, and slideware, as an aid for teachers who want to improve the education in advanced CS topics at schools attended by the children of the 90%. I will give as an example our work on replicating teaching advanced operating systems and distributed systems. Bio: Tom Anderson is the Warren Francis and Wilma Kolm Bradley Chair in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His research interests span all aspects of building practical, robust, and efficient computer systems, including distributed systems, operating systems, computer networks, multiprocessors, and security. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, as well as winner of the USENIX Lifetime Achievement Award, the USENIX STUG Award, the IEEE Koji Kobayashi Computer and Communications Award, the ACM SIGOPS Mark Weiser Award, and the IEEE Communications Society William R. Bennett Prize. He is also an ACM Fellow, past program chair of SIGCOMM and SOSP, and he has co-authored twenty-one award papers and one widely used undergraduate textbook.

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REMINDER: Colloquium Speaker-Thomas Anderson tomorrow at 12:30 and 4:30
by Emily C. Lawrence 05 Dec '18

05 Dec '18

Colloquium Speaker Thomas Anderson from the University of Washington Giving TWO TALKS on Thursday, December 6 Thursday, December 6 - 12:30pm Computer Science - Room 105 “High Performance Operating Systems in the Data Center” Abstract: The ongoing shift of enterprise computing to the cloud provides an opportunity to rethink operating systems for this new setting. I will discuss two specific technologies, kernel bypass for high performance networking and low latency non-volatile storage, and their implications for operating system design. In each case, delivering the performance of the underlying hardware requires novel approaches to the division of labor between hardware, the operating system kernel, and the application library. Thursday, December 6 - 4:30pm Computer Science - Room 104 “A Case for An Open Source CS Curriculum” Abstract: Despite rapidly increasing enrollment in CS courses, the academic CS community is failing to keep pace with demand for trained CS students, leading to escalating starting salaries for our students. Further, the knowledge of how to teach students up to the state of the art is increasingly segregated into a small cohort of schools who mostly cater to students from families in the top 10% of the income distribution. Even in the best case, those schools lack the aggregate capacity to teach more than a small fraction of the nation's need for engineers and computer scientists. MOOCs can help, but they are mainly effective at retraining existing college graduates. In practice, most low and middle income students need a human teacher. In this talk I argue for building an open source CS curriculum, with autograded projects, instructional software, textbooks, and slideware, as an aid for teachers who want to improve the education in advanced CS topics at schools attended by the children of the 90%. I will give as an example our work on replicating teaching advanced operating systems and distributed systems. Bio: Tom Anderson is the Warren Francis and Wilma Kolm Bradley Chair in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His research interests span all aspects of building practical, robust, and efficient computer systems, including distributed systems, operating systems, computer networks, multiprocessors, and security. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, as well as winner of the USENIX Lifetime Achievement Award, the USENIX STUG Award, the IEEE Koji Kobayashi Computer and Communications Award, the ACM SIGOPS Mark Weiser Award, and the IEEE Communications Society William R. Bennett Prize. He is also an ACM Fellow, past program chair of SIGCOMM and SOSP, and he has co-authored twenty-one award papers and one widely used undergraduate textbook.

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Colloquium Speaker: Andrew Goldberg, TODAY at 12:30
by Emily C. Lawrence 04 Dec '18

04 Dec '18

Colloquium Speaker Andrew Goldberg from Amazon.com Tuesday, December 4 - 12:30pm Computer Science - Room 105 “Lost in Translation: Production Code Efficiency” When software engineers re-implement a high-performance research prototype code, one often observes one to two orders of magnitude drop in performance. This holds even if both implementations use the same language. The gap is even wider when one goes from a simple language (e.g., C++) to a more sophisticated one (e.g., Java). One of the root causes of this phenomenon is the misinterpretation by software engineers of what they learn in school. Theoretical computer scientists ignore constant factors for the sake of machine-independent analysis. Programming language researchers focus on compilers that automatically handle low-level OS and architectural issues such as memory management. Software engineering professors emphasize abstraction and re-usability. Many software engineers learn to ignore constant factors, rely on compilers for the low-level efficiency, and use generic primitives for re-usability. This is tempting to do as one has to worry about fewer issues when coding, and one needs to know fewer primitives and data structures. However, in practice constant factors do matter, compilers do not always take advantage of computer architecture features, and generic primitives may be less efficient than the simple ones sufficient for the task. Ignoring these issues can lead to significant loss of computational efficiency and increased memory consumption. Power consumption also increases significantly. In this talk we give several examples of inefficient program fragments and discuss them. These examples show that software engineers need to pay attention to low-level details when choosing data . Bio: Andrew Goldberg is a Senior Principal Scientist at Amazon.com, Inc. His research interests are in design, analysis, and computational evaluation of algorithms and data structures, algorithm engineering, computational game theory, electronic commerce, and parallel and distributed algorithms, and complexity theory. His algorithms are widely used in industry and academia. Goldberg got his Ph.D. degree in Computer Science from MIT in 1987, where he was a Hertz Foundation Fellow. Before joining Amazon in 2014, he worked at GTE Laboratories, Stanford University, NEC Research Institute, InterTrust Technologies, Inc., and Microsoft Research. Goldberg received a number of awards for his research contributions, including the NSF Presidential Young Investigator Award and the ONR Young Investigator Award. He is a Fellow of ACM and SIAM.

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CSML and Astrophysics Colloquium: Julian Kates-Harbeck, Tuesday 12/11
by Emily Lawrence 03 Dec '18

03 Dec '18

Speaker: Julian Kates-Harbeck Title: Disruption Prediction in Tokamak Fusion Reactors via Deep Learning at Scale Day: Tuesday, December 11, 2018 Time: 4:30 pm Room: CSML, Classroom 103 Host: CSML & Astrophysics; Professors Peter Ramadge and Steven Crowley Abstract: The prediction and avoidance of large-scale plasma instabilities called "disruptions" is a crucial step towards successful power generation from magnetic confinement fusion in tokamaks. Inspired by the recent success of deep learning (DL) algorithms in generalizing and learning from multi-modal and high-dimensional data across diverse domains, we present a new approach to forecast disruptions based on convolutional and recurrent neural networks. Our method for the first time overcomes the key limitations of past work in that it (i) delivers reliable predictions on machines other than the one it was trained on - a crucial requirement for large and powerful future reactors that cannot afford "training" disruptions; (ii) utilizes high-dimensional training data such as profiles to add new physics information and boost predictive performance; and (iii) engages supercomputing at the largest scale to deliver solutions with improved accuracy and speed. Trained on experimental data from the largest tokamaks in the US (DIII-D) and the world (JET), our algorithm moreover can be tuned for physics-specific tasks such as prediction with long warning times, and opens up possible avenues for moving forward the goal from passive disruption prediction to active reactor control and optimization. Bio Julian Kates-Harbeck received his bachelor's degree in physics at Stanford and earned a master's in computer science (with a focus on artificial intelligence and machine learning) at the same university. Before entering graduate school, he co-founded a tech startup, where he was responsible for product, hiring, and strategy. He is currently pursuing a PhD in physics at Harvard, studying dynamics on complex social and biological networks. He also works on various scientific challenge problems that can be impacted by -- and drive the development of -- state of the art methods in artificial intelligence.

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CSML Colloquium: Neil Lawrence, Monday 12/10
by Emily Lawrence 03 Dec '18

03 Dec '18

Speaker: Neil Lawrence, University of Sheffield Title: Machine Learning and the Physical World Day: Monday, December 10, 2018 Time: 4:00 pm Room: Julius Romo Rabinowitz 399 Host: Ryan P. Adams and CSML Abstract: Machine learning is a data driven endeavor, but real world systems are physical and mechanistic. In this talk we will review approaches to integrating machine learning with real world systems. Our focus will be on emulation (otherwise known as surrogate modeling). Bio: Neil Lawrence is Professor of Machine Learning at the University of Sheffield and the co-host of Talking Machines. He is currently on leave of absence based at Amazon in Cambridge where he is a Director of Machine Learning. Neil's main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. His recent focus has been on the deployment of machine learning technology in practice, particularly under the banner of data science. He is also the co-host of the Talking Machines podcast. http://inverseprobability.com/biog.html

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Amy Tai will present her FPO "Leveraging Distributed Storage Redundancy in Datacenters" on Monday, 12/10/2018 at 2pm, in CS 105.
by Nicki Gotsis 03 Dec '18

03 Dec '18

Amy Tai will present her FPO "Leveraging Distributed Storage Redundancy in Datacenters" on Monday, 12/10/2018 at 2pm, in CS 105. The members of her committee are as follows: Examiners: Michael Freedman (adviser), Kai Li, and Jennifer Rexford; Readers: Wyatt Lloyd and Kyle Jamieson A copy of her thesis is available upon request. Everyone is invited to attend her talk. The talk abstract follows below: All distributed storage systems replicate data objects, providing built-in redundancy that is designed to help the system withstand failures. Such redundancy is unavoidable because withstanding failures is a critical goal of distributed systems, and redundancy is the only way to tolerate failures that cause loss of data access. However, with the proliferation of data, it is becoming ever more paramount to reduce the costs of distributed storage systems. To balance the need to reduce storage costs and the need to withstand failures, this thesis explores two ways we can leverage the unavoidable redundancy in distributed storage systems to eliminate additional storage overheads in other parts of the storage stack. The first system we present is Replex. The key end-to-end observation in this work is that distributed secondary indices duplicate the work done by replication. Secondary indices often store full copies of data objects, in addition to the replicas of data objects that are created by default to handle failures. In Replex, we eliminate the additional storage overhead of secondary indices by treating them as data replicas during replication time. The second system we present is DIRECT. The key end-to-end observation here is that the redundancy created by replication can and should be used to correct bit errors at the hardware level. Traditionally, disks are expected to abstract bit errors from software, and in fact flash devices are shipped with aggressive internal error correction mechanisms to prevent errors from percolating to the user for the calculated lifetime of the device. In DIRECT, we argue that the underlying premise that disks should not expose bit errors is incorrect. In doing so, DIRECT enables the use of flash devices well beyond their advertised lifetime, which is a huge cost savings for datacenter operators. Therefore, by applying existing storage redundancy to enable two key properties in datacenter storage systems– secondary indexing and flash reliability– this thesis shows that distributed storage systems can be designed without burdensome storage overheads. 1

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REMINDER: Colloquium Speaker: Andrew Goldberg, tomorrow at 12:30
by Emily C. Lawrence 03 Dec '18

03 Dec '18

Colloquium Speaker Andrew Goldberg from Amazon.com Tuesday, December 4 - 12:30pm Computer Science - Room 105 “Lost in Translation: Production Code Efficiency” When software engineers re-implement a high-performance research prototype code, one often observes one to two orders of magnitude drop in performance. This holds even if both implementations use the same language. The gap is even wider when one goes from a simple language (e.g., C++) to a more sophisticated one (e.g., Java). One of the root causes of this phenomenon is the misinterpretation by software engineers of what they learn in school. Theoretical computer scientists ignore constant factors for the sake of machine-independent analysis. Programming language researchers focus on compilers that automatically handle low-level OS and architectural issues such as memory management. Software engineering professors emphasize abstraction and re-usability. Many software engineers learn to ignore constant factors, rely on compilers for the low-level efficiency, and use generic primitives for re-usability. This is tempting to do as one has to worry about fewer issues when coding, and one needs to know fewer primitives and data structures. However, in practice constant factors do matter, compilers do not always take advantage of computer architecture features, and generic primitives may be less efficient than the simple ones sufficient for the task. Ignoring these issues can lead to significant loss of computational efficiency and increased memory consumption. Power consumption also increases significantly. In this talk we give several examples of inefficient program fragments and discuss them. These examples show that software engineers need to pay attention to low-level details when choosing data . Bio: Andrew Goldberg is a Senior Principal Scientist at Amazon.com, Inc. His research interests are in design, analysis, and computational evaluation of algorithms and data structures, algorithm engineering, computational game theory, electronic commerce, and parallel and distributed algorithms, and complexity theory. His algorithms are widely used in industry and academia. Goldberg got his Ph.D. degree in Computer Science from MIT in 1987, where he was a Hertz Foundation Fellow. Before joining Amazon in 2014, he worked at GTE Laboratories, Stanford University, NEC Research Institute, InterTrust Technologies, Inc., and Microsoft Research. Goldberg received a number of awards for his research contributions, including the NSF Presidential Young Investigator Award and the ONR Young Investigator Award. He is a Fellow of ACM and SIAM.

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