- talks - lists.cs.princeton.edu

Colloquium Speaker-Joy Arulraj, Tuesday, April 17
by Emily C. Lawrence 11 Apr '18

11 Apr '18

Joy Arulraj from Carnegie Mellon University Tuesday, April 17, 2018 - 12:30pm Computer Science - Room 105 Host: Wyatt Lloyd “Data Management on Non-Volatile Memory” We are at an exciting point in the evolution of memory technology. Device manufacturers have created a new non-volatile memory (NVM) technology that can serve as both system memory and storage. NVM supports fast reads and writes similar to volatile memory, but all writes to it are persistent like a solid-state disk. The advent of NVM invalidates decades of design decisions that are deeply embedded in today's database management systems (DBMSs). These systems are unable to take full advantage of NVM because their internal architectures are predicated on the assumption that memory is volatile. With NVM, many of the components of today's DBMSs are unnecessary and will degrade the performance of data-intensive applications. Thus, the best way to resolve these shortcomings is by designing a new system explicitly tailored for NVM. In this talk, I will present my research on the design and development of an NVM DBMS, called Peloton. Peloton's architecture shows that the impact of NVM spans across all the layers of the DBMS. I will first introduce write-behind logging, an NVM-centric protocol that improves the availability of the database system by two orders-of-magnitude compared to the widely-used write-ahead logging protocol. I will then present the BzTree , an NVM-centric index data structure that illustrates how to simplify programming on NVM. In drawing broader lessons from this work, I will argue that all types of software systems, including file systems, machine-learning systems, and key-value stores, are amenable to similar architectural changes to achieve high performance and availability on NVM. Bio: Joy Arulraj is a Ph.D. candidate at Carnegie Mellon University. His research interests are in database systems and data science. As part of his dissertation work, he has studied and built the first non-volatile memory database system, called Peloton, for performing large-scale transaction processing and real-time data analytics. He is the recipient of the Carnegie Mellon Presidential Fellowship and a Samsung Ph.D. Fellowship.

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Colloquium Speaker-Jacob Andreas, Monday, April 16
by Emily C. Lawrence 10 Apr '18

10 Apr '18

Jacob Andreas from University of California at Berkeley Monday, April 16, 2018 - 12:30pm Computer Science - Room 105 Host: Sanjeev Arora “Learning from Language” Natural language is built from a library of concepts and compositional operators that provide a rich source of information about how humans understand the world. Can this information help us build better machine learning models? In this talk, we'll explore three ways of integrating compositional linguistic structure and learning: using language as a source of modular reasoning operators for question answering, as a scaffold for fast and generalizable reinforcement learning, and as a tool for understanding representations in neural networks. Bio: Jacob Andreas is a fifth-year PhD student at UC Berkeley working in natural language processing. He holds a B.S. from Columbia and an M.Phil. from Cambridge, where he studied as a Churchill scholar. His papers were recognized at NAACL 2016 and ICML 2017. Jacob has been an NSF graduate fellow, a Huawei--Berkeley AI fellow, and a Facebook fellow.

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Yinda Zhang - PreFPO today April 10, 2018 at 2:00 in room CS402
by Barbara A. Mooring 10 Apr '18

10 Apr '18

Yinda Zhang will present PreFPO on Tuesday, April 10, 2018 at 2:00 in room CS402 Committee Member: Thomas Funkhouser, Adviser Olga Russakovsky Szymon Rusinkiewicz Ryan Adams James Hays (Georgia Institute of Technology) All are welcomed to attend. Title and abstract are below. Title: “From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments” Abstract: Understanding 3D geometry and semantics of the surrounding environment is in critically high demand for many applications, such as autonomous driving, robotics, augmented reality, etc. However, it is extremely challenging due to the low quality depth measurements due to failures and noisy measurements from sensors, limited access to ground truth data, and cluttered scenes with heavy occlusions and intervening objects. In this presentation, I will introduces a full spectrum of 3D scene understanding works to handle these challenging issues. Starting from estimating a depth map, which is one of the most important immediate measurements of the 3D geometry of the scene, we introduce a learning based active stereo system that learns self-supervisely and reduces the disparity error to 1/10th of other canonical stereo systems. To further handle the missing depth caused by sensor failures, we propose a method to effectively complete the depth map using information from an aligned color image. Beyond per pixel depth, we then attempt to predict other high-level semantics on each pixel, such as surface normals and object boundaries. However, realizing the lack of large scale supervision, we design a synthetic data generation framework, which creates photo-realistic color rendering and various of accurate pixel-wise ground truths to facilitate the learning process and improve the performance on real data. In the end, we pursue holistic scene understanding by estimating a 3D representation of the scene, in which objects and room layout are represented using 3D bounding box and planar surface respectively. We propose methods to produce such representation from either a single color panorama or depth image leveraging scene context. On the whole, these proposed methods produce understanding of both 3D geometry and semantics from the most fine-grained pixel level to the holistic scene scale, which build foundations and could possibly inspire future works for 3D scene understanding. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Colloquium Speaker-Amit Levy, TODAY at 12:30pm
by Emily C. Lawrence 10 Apr '18

10 Apr '18

Amit Levy from Stanford University Tuesday, April 10, 2018 - 12:30pm Computer Science - Room 105 Host: Mike Freedman "Closing the Loop on Secure System Design" Abstract: Despite decades of research building secure operating systems, many deployed systems must still choose between flexible application APIs and security. As a result, the vast majority of programmers are unable to improve these systems. This is not merely a result of poor system building. It is hard to design highly extensible systems that are both secure and useful. Moreover, evaluating novel designs with actual developers is critical in order to make sure system builders can adopt research systems in practice. Fortunately, in emerging application domains, such as the Internet of Things, there are no entrenched operating systems and application portability is less important. This makes it possible evaluate research techniques for building more secure and extensible systems with developers who are willing to adopt them. I'll describe Tock, an operating system for microcontrollers that enables third-party developers to extend the system. Tock uses the Rust type-system to isolate kernel extensions and the hardware to isolate applications. I'll discuss how we continuously evaluate Tock by engaging with practitioners, and how lessons from practitioners have fed back into the system's design. Bio: Amit Levy is a PhD candidate in Computer Science at Stanford University. He builds secure operating system kernels, web platforms, and network systems that help make computers more programmable by third-party application developers.

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Distinguished Lecturer Jeff Dean, April 24 at 4:30
by Emily C. Lawrence 10 Apr '18

10 Apr '18

Jeff Dean from Google Research Tuesday, April 24, 2018 - 4:30pm Friend Center - Room 101 Host: Kai Li Event page: http://www.cs.princeton.edu/events/25679 "Deep Learning to Solve Challenging Problems" For the past six years, the Google Brain team has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. We have made significant progress in computer vision, speech recognition, language understanding, machine translation, healthcare, robotic control, and other areas. Our group has open-sourced the TensorFlow system, a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the research and computer systems work we've done with an eye towards how it can be used to solve challenging problems. This talk describes joint work with many people at Google. Bio: Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he co-founded and leads the Google Brain team, Google's deep learning and artificial intelligence research team. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co- implementor of Google's distributed computing infrastructure, including the MapReduce , BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing and the Mark Weiser Award. **A reception in the Friend Center Upper Atrium will follow immediately after the talk.

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CORRECTION - Yinda Zhang PreFPO tomorrow April 10, 2018 at 2:00 pm CS402
by Barbara A. Mooring 09 Apr '18

09 Apr '18

Yinda Zhang will present PreFPO on Tuesday, April 10, 2018 at 2:00 in room CS402 Committee Member: Thomas Funkhouser, Adviser Olga Russakovsky Szymon Rusinkiewicz Ryan Adams James Hays (Georgia Institute of Technology) All are welcomed to attend. Title and abstract are below. Title: “From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments” Abstract: Understanding 3D geometry and semantics of the surrounding environment is in critically high demand for many applications, such as autonomous driving, robotics, augmented reality, etc. However, it is extremely challenging due to the low quality depth measurements due to failures and noisy measurements from sensors, limited access to ground truth data, and cluttered scenes with heavy occlusions and intervening objects. In this presentation, I will introduces a full spectrum of 3D scene understanding works to handle these challenging issues. Starting from estimating a depth map, which is one of the most important immediate measurements of the 3D geometry of the scene, we introduce a learning based active stereo system that learns self-supervisely and reduces the disparity error to 1/10th of other canonical stereo systems. To further handle the missing depth caused by sensor failures, we propose a method to effectively complete the depth map using information from an aligned color image. Beyond per pixel depth, we then attempt to predict other high-level semantics on each pixel, such as surface normals and object boundaries. However, realizing the lack of large scale supervision, we design a synthetic data generation framework, which creates photo-realistic color rendering and various of accurate pixel-wise ground truths to facilitate the learning process and improve the performance on real data. In the end, we pursue holistic scene understanding by estimating a 3D representation of the scene, in which objects and room layout are represented using 3D bounding box and planar surface respectively. We propose methods to produce such representation from either a single color panorama or depth image leveraging scene context. On the whole, these proposed methods produce understanding of both 3D geometry and semantics from the most fine-grained pixel level to the holistic scene scale, which build foundations and could possibly inspire future works for 3D scene understanding. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Yinda Zhang - PreFPO - April 10, 2018 at 2:00 pm CS402
by Barbara A. Mooring 09 Apr '18

09 Apr '18

Yinda Zhang will present PreFPO on Tuesday, April 20, 2018 at 2:00 in room CS402 Committee Member: Thomas Funkhouser, Adviser Olga Russakovsky Szymon Rusinkiewicz Ryan Adams James Hays (Georgia Institute of Technology) All are welcomed to attend. Title and abstract are below. Title: “From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments” Abstract: Understanding 3D geometry and semantics of the surrounding environment is in critically high demand for many applications, such as autonomous driving, robotics, augmented reality, etc. However, it is extremely challenging due to the low quality depth measurements due to failures and noisy measurements from sensors, limited access to ground truth data, and cluttered scenes with heavy occlusions and intervening objects. In this presentation, I will introduces a full spectrum of 3D scene understanding works to handle these challenging issues. Starting from estimating a depth map, which is one of the most important immediate measurements of the 3D geometry of the scene, we introduce a learning based active stereo system that learns self-supervisely and reduces the disparity error to 1/10th of other canonical stereo systems. To further handle the missing depth caused by sensor failures, we propose a method to effectively complete the depth map using information from an aligned color image. Beyond per pixel depth, we then attempt to predict other high-level semantics on each pixel, such as surface normals and object boundaries. However, realizing the lack of large scale supervision, we design a synthetic data generation framework, which creates photo-realistic color rendering and various of accurate pixel-wise ground truths to facilitate the learning process and improve the performance on real data. In the end, we pursue holistic scene understanding by estimating a 3D representation of the scene, in which objects and room layout are represented using 3D bounding box and planar surface respectively. We propose methods to produce such representation from either a single color panorama or depth image leveraging scene context. On the whole, these proposed methods produce understanding of both 3D geometry and semantics from the most fine-grained pixel level to the holistic scene scale, which build foundations and could possibly inspire future works for 3D scene understanding. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Reminder: Colloquium Speaker-Amit Levy, tomorrow at 12:30pm
by Emily C. Lawrence 09 Apr '18

09 Apr '18

Amit Levy from Stanford University Tuesday, April 10, 2018 - 12:30pm Computer Science - Room 105 Host: Mike Freedman "Closing the Loop on Secure System Design" Abstract: Despite decades of research building secure operating systems, many deployed systems must still choose between flexible application APIs and security. As a result, the vast majority of programmers are unable to improve these systems. This is not merely a result of poor system building. It is hard to design highly extensible systems that are both secure and useful. Moreover, evaluating novel designs with actual developers is critical in order to make sure system builders can adopt research systems in practice. Fortunately, in emerging application domains, such as the Internet of Things, there are no entrenched operating systems and application portability is less important. This makes it possible evaluate research techniques for building more secure and extensible systems with developers who are willing to adopt them. I'll describe Tock, an operating system for microcontrollers that enables third-party developers to extend the system. Tock uses the Rust type-system to isolate kernel extensions and the hardware to isolate applications. I'll discuss how we continuously evaluate Tock by engaging with practitioners, and how lessons from practitioners have fed back into the system's design. Bio: Amit Levy is a PhD candidate in Computer Science at Stanford University. He builds secure operating system kernels, web platforms, and network systems that help make computers more programmable by third-party application developers.

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Colloquium Speaker-Jose Faleiro, TODAY at 12:30
by Emily C. Lawrence 09 Apr '18

09 Apr '18

CS Department Colloquium Speaker Jose Faleiro from Yale University Monday, April 9, 2018 - 12:30pm Computer Science - Room 105 Host: Wyatt Lloyd "High Performance Serializable Transactions via Deterministic Execution" Abstract: Concurrency, the processing of multiple requests simultaneously, is one of the most challenging problems large-scale server applications face in practice. Accordingly, database systems research has long made the case for automatically handling concurrency in the database by guaranteeing serializability . Serializability shields applications from reasoning about concurrency, and allows developers to focus entirely on implementing application logic. Unfortunately, in the 40-plus years since its inception, serializability has not seen wide adoption in practice. This is because weaker guarantees, which expose applications to concurrency and the inevitable bugs that arise, perform significantly better. In this talk, I will discuss my research on addressing the performance limitations of serializability via deterministic transaction execution. Deterministic transaction execution exploits the fact that a large class of modern server applications do not require the full generality of conventional database transactions. By tailoring transaction execution mechanisms for this class of applications, my research shows that it is possible to achieve serializability with minimal performance overhead. I will first describe a serializable multi-versioning mechanism that decouples conflicting reads and writes, and subsequently outperforms a state-of-the-art implementation of the weaker guarantee of snapshot isolation by over 3x. Next, I will describe piecewise visibility, a concurrency control mechanism that isolates requests at a finer granularity than entire transactions, which consequently permits aggressive serializable transaction interleavings and outperforms the weaker guarantee of read committed by over 3x. Finally, I will discuss ongoing work that applies deterministic transaction execution principles to address replication lag in Facebook’s production MySQL infrastructure. Bio: Jose Faleiro is a PhD candidate in computer science at Yale University. His research interests are in data management systems, multi-core systems, and distributed systems. His thesis research investigates the use of deterministic execution to enable scalable and efficient transaction processing on main-memory multi-core database systems. In addition to his academic research at Yale, Jose has worked on large-scale real world systems, including Microsoft’s Orleans cloud programming framework, and Facebook’s production MySQL infrastructure. He is the recipient of the Alan J. Perlis Fellowship at Yale, and a Microsoft Research Tech Transfer Award. He has an undergraduate degree in computer science from the Birla Institute of Technology and Science (BITS), Pilani , India.a

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Reminder: Colloquium Speaker-Jose Faleiro, tomorrow at 12:30
by Emily C. Lawrence 08 Apr '18

08 Apr '18

CS Department Colloquium Speaker Jose Faleiro from Yale University Monday, April 9, 2018 - 12:30pm Computer Science - Room 105 Host: Wyatt Lloyd "High Performance Serializable Transactions via Deterministic Execution" Abstract: Concurrency, the processing of multiple requests simultaneously, is one of the most challenging problems large-scale server applications face in practice. Accordingly, database systems research has long made the case for automatically handling concurrency in the database by guaranteeing serializability . Serializability shields applications from reasoning about concurrency, and allows developers to focus entirely on implementing application logic. Unfortunately, in the 40-plus years since its inception, serializability has not seen wide adoption in practice. This is because weaker guarantees, which expose applications to concurrency and the inevitable bugs that arise, perform significantly better. In this talk, I will discuss my research on addressing the performance limitations of serializability via deterministic transaction execution. Deterministic transaction execution exploits the fact that a large class of modern server applications do not require the full generality of conventional database transactions. By tailoring transaction execution mechanisms for this class of applications, my research shows that it is possible to achieve serializability with minimal performance overhead. I will first describe a serializable multi-versioning mechanism that decouples conflicting reads and writes, and subsequently outperforms a state-of-the-art implementation of the weaker guarantee of snapshot isolation by over 3x. Next, I will describe piecewise visibility, a concurrency control mechanism that isolates requests at a finer granularity than entire transactions, which consequently permits aggressive serializable transaction interleavings and outperforms the weaker guarantee of read committed by over 3x. Finally, I will discuss ongoing work that applies deterministic transaction execution principles to address replication lag in Facebook’s production MySQL infrastructure. Bio: Jose Faleiro is a PhD candidate in computer science at Yale University. His research interests are in data management systems, multi-core systems, and distributed systems. His thesis research investigates the use of deterministic execution to enable scalable and efficient transaction processing on main-memory multi-core database systems. In addition to his academic research at Yale, Jose has worked on large-scale real world systems, including Microsoft’s Orleans cloud programming framework, and Facebook’s production MySQL infrastructure. He is the recipient of the Alan J. Perlis Fellowship at Yale, and a Microsoft Research Tech Transfer Award. He has an undergraduate degree in computer science from the Birla Institute of Technology and Science (BITS), Pilani , India.a

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