- talks - lists.cs.princeton.edu

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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Colloquium Speaker Thomas Anderson giving 2 talks on Thursday, Dec 6
by Emily C. Lawrence 30 Nov '18

30 Nov '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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TODAY November 29, 4:30pm, E-Quad B205 - Pramod Viswanath, University of Illinois at Urbana-Champaign
by Emily Lawrence 29 Nov '18

29 Nov '18

EE SEMINAR SERIES Speaker: Pramod Viswanath, University of Illinois at Urbana-Champaign Title: Why Blocks and Why Chains; A First Principles (Re)Design of Blockchains Day: Thursday, November 29, 2018 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. Naveen Verma Abstract: Today's blockchains do not scale in a meaningful way. As more nodes join the system, the efficiency of the system (computation, communication, and storage) degrades, or at best stays constant. Furthermore, the security of the permission less system imposes limitations on the core performance metrics of throughput, latency and reliability. We take a first principle approach to the blockchain ecosystem addressing each of the various components holistically. Our approach is characterized by seeking fundamental limits (those prescribed by the physics of the underlying network) to performance and designing algorithms that attain them. This research is informed by decades of experience in information theory, coding theory, algorithms, wireless communication and packet networks. This talk will highlight key outcomes of this research program, including: Prism (a new consensus algorithm that guarantees information theoretically optimal throughput, latency, reliability), Spider (a new networking protocol for off-chain payment channels), Polyshard (a new coded storage architecture), and Dandelion (a new network privacy layer). References: https://arxiv.org/abs/1810.08092 https://arxiv.org/abs/1809.10361 https://arxiv.org/abs/1809.05088 https://arxiv.org/abs/1809.07468 Bio: Pramod Viswanath received the Ph.D. degree in EECS from UC Berkeley in 2000. >From 2000 to 2001, he was a member of research staff at Flarion technologies, NJ. Since 2001, he is on the faculty at University of Illinois at Urbana Champaign in Electrical and Computer Engineering, where he currently is a professor. He is a coauthor, with David Tse, of the text Fundamentals of Wireless Communication, which has been used in over 60 institutions around the world. He is coinventor of the opportunistic beamforming method and codesigner of Flash-OFDM communication algorithms adapted into fourth-generation cellular systems. His current research interests are in blockchain technologies from a variety of angles: networking protocols, consensus algorithms, payment channels, distributed coded storage and incentive designs. He is co-founder and CEO of Applied Protocol Research, a startup doing research on blockchain technologies. Applied Protocol Research is staffed by academics (professors, PhDs, and intern graduate students), with a wide variety of backgrounds (EE/CS/ECON covering both theory/systems from different institutions (Berkeley, CMU, Illinois, MIT, Stanford, USC, UW-Seattle). This talk is joint work by the speaker with: Mohammad Alizadeh (MIT), Salman Avestimehr (USC), Giulia Fanti (CMU), Sreeram Kannan (UW-Seattle), Sewoong Oh (Illinois) and David Tse (Stanford).

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Colloquium Speaker: Andrew Goldberg, Tuesday, Dec 4
by Emily C. Lawrence 28 Nov '18

28 Nov '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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Donghun Lee will present his Pre-FPO, "On Finite Horizon Behaviors of Machine Learning: the Max-Operator Bias in Q-learning and the Framework of Learning to Learn Optimally" on Thursday, November 29, 2018 at 1:30pm in CS 105
by Nicki Gotsis 27 Nov '18

27 Nov '18

Donghun Lee will present his Pre-FPO on Thursday, November 29, 2018 at 1:30pm in CS 105. The members of his committee are Warren Powell (ORFE) (adviser), Ryan Adams, and Peter Ramadge (EE). Everyone is invited to attend his talk. The talk title and abstract follow below. Title: On Finite Horizon Behaviors of Machine Learning: the Max-Operator Bias in Q-learning and the Framework of Learning to Learn Optimally Abstract: With greater popularity of machine learning in industry and growing number of available packages and toolkits, many end users of machine learning face the problem of deploying machine learning algorithms to their applications. Most algorithms with asymptotic guarantees leave finite-time issues such as initialization or tuning at the mercy of the end users, to whom the burden may cause undesirable outcomes in practice with finite time horizon. This talk will cover my research on finite time horizon behavior of machine learning algorithms: firstly on Q-learning algorithm, and secondly on the problem of "learning to learn optimally". First, I will review the positive bias innate in Q-learning algorithm, and present a method to correct the bias on-the-fly. Then, I will address the problem of applying machine learning algorithm to finite time horizon problems to formulate the "learning to learn optimally" framework. Using the framework, I will construct a knowledge-gradient based algorithm for adaptive deployment of machine learning application with finite time horizon. Some work in progress will be summarized at the end of the talk.

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Fwd: Thursday, November 29, 4:30pm, E-Quad B205 - Pramod Viswanath, University of Illinois at Urbana-Champaign
by Jennifer Rexford 26 Nov '18

26 Nov '18

> > From: EE Executive Office <eemain(a)Princeton.EDU> > Subject: Thursday, November 29, 4:30pm, E-Quad B205 - Pramod Viswanath, University of Illinois at Urbana-Champaign > Date: November 26, 2018 at 10:38:23 AM EST > To: ee-seminar(a)Princeton.EDU > Reply-To: EE Executive Office <eemain(a)Princeton.EDU> > > > > EE SEMINAR SERIES > > > Speaker: > Pramod Viswanath, University of Illinois at Urbana-Champaign > Title: > Why Blocks and Why Chains; A First Principles (Re)Design of Blockchains > Day: > Thursday, November 29, 2018 > Time: > 4:30 pm > Room: > B205 Engineering Quadrangle > Host: > Prof. Naveen Verma > > > Abstract: > Today's blockchains do not scale in a meaningful way. As more nodes join the system, the efficiency of the system (computation, communication, and storage) degrades, or at best stays constant. Furthermore, the security of the permission less system imposes limitations on the core performance metrics of throughput, latency and reliability. We take a first principle approach to the blockchain ecosystem addressing each of the various components holistically. Our approach is characterized by seeking fundamental limits (those prescribed by the physics of the underlying network) to performance and designing algorithms that attain them. This research is informed by decades of experience in information theory, coding theory, algorithms, wireless communication and packet networks. This talk will highlight key outcomes of this research program, including: > Prism (a new consensus algorithm that guarantees information theoretically optimal throughput, latency, reliability), > Spider (a new networking protocol for off-chain payment channels), > Polyshard (a new coded storage architecture), and > Dandelion (a new network privacy layer). > > References: > https://arxiv.org/abs/1810.08092 <https://arxiv.org/abs/1810.08092> > https://arxiv.org/abs/1809.10361 <https://arxiv.org/abs/1809.10361> > https://arxiv.org/abs/1809.05088 <https://arxiv.org/abs/1809.05088> > https://arxiv.org/abs/1809.07468 <https://arxiv.org/abs/1809.07468> > > > Bio: > Pramod Viswanath received the Ph.D. degree in EECS from UC Berkeley in 2000. From 2000 to 2001, he was a member of research staff at Flarion technologies, NJ. Since 2001, he is on the faculty at University of Illinois at Urbana Champaign in Electrical and Computer Engineering, where he currently is a professor. He is a coauthor, with David Tse, of the text Fundamentals of Wireless Communication, which has been used in over 60 institutions around the world. He is coinventor of the opportunistic beamforming method and codesigner of Flash-OFDM communication algorithms adapted into fourth-generation cellular systems. > > His current research interests are in blockchain technologies from a variety of angles: networking protocols, consensus algorithms, payment channels, distributed coded storage and incentive designs. He is co-founder and CEO of Applied Protocol Research, a startup doing research on blockchain technologies. Applied Protocol Research is staffed by academics (professors, PhDs, and intern graduate students), with a wide variety of backgrounds (EE/CS/ECON covering both theory/systems from different institutions (Berkeley, CMU, Illinois, MIT, Stanford, USC, UW-Seattle). > > This talk is joint work by the speaker with: Mohammad Alizadeh (MIT), Salman Avestimehr (USC), Giulia Fanti (CMU), Sreeram Kannan (UW-Seattle), Sewoong Oh (Illinois) and David Tse (Stanford).

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CANCELED: Colloquium Speaker-Andy Pavlo
by Emily C. Lawrence 16 Nov '18

16 Nov '18

Due to yesterday's weather events, today's talk has been canceled. Colloquium Speaker Andy Pavlo from Carnegie Mellon University Friday, November 16 - 12:30pm Computer Science - Room 105 “Make Your Database Dream of Electric Sheep: Designing for Autonomous Operation” In the last 20 years, researchers and vendors have built advisory tools to assist DBAs in tuning and physical design. Most of this previous work is incomplete because they require humans to make the final decisions about any database changes and are reactionary measures that fix problems after they occur. What is needed for a "self-driving" DBMS are components that are designed for autonomous operation. This will enable new optimizations that are not possible today because the complexity of managing these systems has surpassed the abilities of humans. In this talk, I present the core design principles of an autonomous DBMS. These are necessary to support ample data collection, fast state changes, and accurate reward observations. I will discuss techniques on how to build a new autonomous DBMS or the steps needed to retrofit an existing one to enable automated management. Our work is based on our experiences at CMU from developing an automatic tuning service ( OtterTune ) and our self-driving DBMS (Peloton). Bio: Andy Pavlo is an Assistant Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. He also used to raise clams.

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REMINDER: Colloquium Speaker-Andy Pavlo, tomorrow at 12:30pm
by Emily C. Lawrence 15 Nov '18

15 Nov '18

Colloquium Speaker Andy Pavlo from Carnegie Mellon University Friday, November 16 - 12:30pm Computer Science - Room 105 “Make Your Database Dream of Electric Sheep: Designing for Autonomous Operation” In the last 20 years, researchers and vendors have built advisory tools to assist DBAs in tuning and physical design. Most of this previous work is incomplete because they require humans to make the final decisions about any database changes and are reactionary measures that fix problems after they occur. What is needed for a "self-driving" DBMS are components that are designed for autonomous operation. This will enable new optimizations that are not possible today because the complexity of managing these systems has surpassed the abilities of humans. In this talk, I present the core design principles of an autonomous DBMS. These are necessary to support ample data collection, fast state changes, and accurate reward observations. I will discuss techniques on how to build a new autonomous DBMS or the steps needed to retrofit an existing one to enable automated management. Our work is based on our experiences at CMU from developing an automatic tuning service ( OtterTune ) and our self-driving DBMS (Peloton). Bio: Andy Pavlo is an Assistant Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. He also used to raise clams.

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