- talks - lists.cs.princeton.edu

Reminder: Colloquium speaker: Eleazar Eskin, tomorrow at 12:30PM
by Emily C. Lawrence 21 Nov '19

21 Nov '19

CS Colloquium Speaker Eleazar Eskin , University of California, Los Angeles Friday, November 22 - 12:30pm Computer Science - Room 105 Host: Ben Raphael [ https://www.cs.princeton.edu/events/25902 | https://www.cs.princeton.edu/events/25902 ] Leveraging Allelic Heterogeneity in Association Studies Over 100,000 variants have been implicated in human traits through genome-wide association studies (GWAS). Virtually all reported GWAS identify associations by measuring the correlation between a single variant and a phenotype of interest. Recently, several studies reported that at many risk loci, there may exist multiple causal variants, a phenomenon referred to as allelic heterogeneity. Naturally, for a locus with multiple causal variants with small effect sizes, the standard association test is underpowered to detect the associations. Alternatively, an approach considering effects of multiple variants simultaneously may increase statistical power. Counterintuitively, most approaches that consider multiple variants in association studies find fewer associations than the single SNP association test. This is due to the fact that most multiple variant methods assume a structure of allelic heterogeneity which is very different from what is observed in genetic studies. In this work, we propose a new statistical method, Model-based Association test Reflecting causal Status (MARS), that tries to find an association between variants in risk loci and a phenotype, considering multiple variants at each locus. One of the main advantages of MARS is that it only requires the existing summary statistics to detect associated risk loci. Thus, MARS is applicable to any association study with summary statistics, even though individual level data is not available for the study. Utilizing extensive simulated data sets, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while robustly controls the type I error. Applied to data of 44 tissues provided by the Genotype- Tissue Expression ( GTEx ) consortium, we show that MARS identifies more eGenes compared to previous approaches in most of the tissues; e.g. MARS identified 16% more eGenes than the ones reported by the GTEx consortium. Moreover, applied to Northern Finland Birth Cohort (NFBC) data, we demonstrate that MARS effectively identifies association loci with improved power (56% of more loci found by MARS) in GWAS studies compared to the standard association test. Bio: Dr. Eleazar Eskin serves as the inaugural chair for the UCLA Department of Computational Medicine. Fascinated by the intersection of computer science and biology, Dr. Eskin is researching and developing computational methods for the analysis of genetic variations in human disease. There are millions of variants in the genome and identifying the variants involved in disease often requires tens of thousands of patient samples. In order to analyze these tremendously large datasets, Dr. Eskin and his team are solving challenging computational problems and developing new computational techniques. He received his PhD in computer science from Columbia University. A recipient of the Alfred P. Sloan Foundation Research Fellowship, Dr. Eskin’s work is supported by the National Science Foundation and the National Institutes of Health.

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Reminder: Speaker David Hay tomorrow at 11:00AM
by Emily C. Lawrence 21 Nov '19

21 Nov '19

CS Speaker David Hay, Hebrew University Friday, November 22 - 11:00AM Computer Science - Room 105 Host: Jennifer Rexford [ https://www.cs.princeton.edu/events/25900 | https://www.cs.princeton.edu/events/25900 ] Network Level IoT Security Computer networks have undergone and continue to experience a major transformation, whereby billions of low-cost devices are being connected to the network to provide additional functionality and better user experience. Unlike traditional network devices, these devices, collectively known as the ``Internet of Things'' ( IoT ), typically have very limited computational, memory, and power resources. These IoT devices became a major security concerns, both due to human factors and to technical challenges in deploying security mechanisms on devices with low resources. The number and diversity of IoT devices creates a huge attack surface that is often exploited by attackers to launch large-scale attacks, sometimes exploiting well-known vulnerabilities. This talk will highlight the security concerns of IoT devices from a networking perspective and explore how to secure IoT devices using whitelists, in which communication between a device and an endpoint is prohibited unless that endpoint appears in the corresponding whitelist. Finally, we will discussed deployment options for such a solution (namely, within the internet gateway, as virtual network function within the ISP network, or a combination of the two). Bio: David Hay is an Associate Professor with the Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel. He received the B.A. (summa cum laude) and Ph.D. degrees in computer science from the Technion —Israel Institute of Technology, Haifa, Israel, in 2001 and 2007, respectively. In addition, he was with IBM Haifa Research Labs, Haifa, Israel; Cisco Systems, San Jose, CA, USA; the Electronic Department, Politecnico di Torino, Turin, Italy; and the Electrical Engineering Department with Columbia University, New York, NY, USA. In 2010, he co-founded (with Prof. Brembler -Barr) the DEEPNESS lab, focusing on deep packet inspection in next-generation network devices. He has served as a technical program committee member of numerous networking conferences, and since 2018 serves as en editor of ACM/IEEE Transactions on Networking. His research interests are in computer networks—in particular, network algorithmics , packet classification, deep packet inspection, network survivability and resilience, software-defined networking, network-function virtualization, and various aspects of network security.

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Colloquium Speaker Jason Weston, Tuesday, 11/26 at 1:30pm
by Emily C. Lawrence 20 Nov '19

20 Nov '19

CS Colloquium Speaker Jason Weston, Facebook Tuesday, November 26 - 1:30PM Computer Science - Room 104 Host: Danqi Chen [ https://www.cs.princeton.edu/events/25903 | https://www.cs.princeton.edu/events/25903 ] Putting together the threads of conversational AI Maybe we don't have enough threads yet to knit together the whole, but let's try anyway! We present our view of what is necessary for conversational AI, and the pieces we have worked on so far to get there. In particular: software ( ParlAI , a unified platform for dialogue research), various neural architectures for memory, reasoning, retrieval and generation, and interactive learning, tasks for employing personality (Persona-Chat), knowledge (Wizard of Wikipedia) and perception (Image-Chat), evaluation studies & techniques (Acute- Eval ), and a recent competition (ConvAI2). We'll try not to be too pessimistic (!), but we'll also discuss how far we still have to go. Bio: Jason Weston is a research scientist at Facebook, NY and a Visiting Research Professor at NYU. He earned his PhD in machine learning at Royal Holloway, University of London and at AT&T Research in Red Bank, NJ (advisors: Alex Gammerman , Volodya Vovk and Vladimir Vapnik ) in 2000. From 2000 to 2001, he was a researcher at Biowulf technologies. From 2002 to 2003 he was a research scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen , Germany. From 2003 to 2009 he was a research staff member at NEC Labs America, Princeton. From 2009 to 2014 he was a research scientist at Google, NY. His interests lie in statistical machine learning, with a focus on reasoning, memory, perception, interaction and communication. Jason has published over 100 papers, including best paper awards at ICML and ECML, and a Test of Time Award for his work "A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning", ICML 2008 (with Ronan Collobert ). He was part of the YouTube team that won a National Academy of Television Arts & Sciences Emmy Award for Technology and Engineering for Personalized Recommendation Engines for Video Discovery. He was listed as the 16th most influential machine learning scholar at AMiner and one of the top 50 authors in Computer Science in Science.

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MAE Crocco Colloquium Seminar Speaker: Dr. Leslie Kaelbling, Friday, Nov 22
by Emily Lawrence 18 Nov '19

18 Nov '19

MAE Crocco Colloquium speaker Dr. Leslie Kaelbling, MIT Computer Science and Engineering Friday, November 22nd - 12:30 pm Bowen Hall, room 222 Host: Ani Majumdar https://mae.princeton.edu/about-mae/events/doing-our-robots-what-nature-did- us Doing for Our Robots What Nature Did for Us. We, as robot engineers, have to think hard about our role in the design of robots and how it interacts with learning, both in "the factory" (that is, at engineering time) and in "the wild" (that is, when the robot is delivered to a customer). I will share some general thoughts about the strategies for robot design and then talk in detail about some work I have been involved in, both in the design of an overall architecture for an intelligent robot and in strategies for learning to integrate new skills into the repertoire of an already competent robot. Bio: Dr. Leslie Pack Kaelbling is the Panasonic Professor of Computer Science and Engineering at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology. She has made research contributions to decision-making under uncertainty, learning, and sensing with applications to robotics, with a particular focus on reinforcement learning and planning in partially observable domains. She holds an A.B in Philosophy and a Ph.D. in Computer Science from Stanford University, and has had research positions at SRI International and Teleos Research and a faculty position at Brown University. She is the recipient of the US National Science Foundation Presidential Faculty Fellowship, the IJCAI Computers and Thought Award, and several teaching prizes; she has been elected a fellow of the AAAI. She was the founder and editor-in-chief of the Journal of Machine Learning Research.

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Colloquium speaker: Eleazar Eskin, Friday, 11/22 at 12:30PM
by Emily C. Lawrence 18 Nov '19

18 Nov '19

CS Colloquium Speaker Eleazar Eskin , University of California, Los Angeles Friday, November 22 - 12:30pm Computer Science - Room 105 Host: Ben Raphael [ https://www.cs.princeton.edu/events/25902 | https://www.cs.princeton.edu/events/25902 ] Leveraging Allelic Heterogeneity in Association Studies Over 100,000 variants have been implicated in human traits through genome-wide association studies (GWAS). Virtually all reported GWAS identify associations by measuring the correlation between a single variant and a phenotype of interest. Recently, several studies reported that at many risk loci, there may exist multiple causal variants, a phenomenon referred to as allelic heterogeneity. Naturally, for a locus with multiple causal variants with small effect sizes, the standard association test is underpowered to detect the associations. Alternatively, an approach considering effects of multiple variants simultaneously may increase statistical power. Counterintuitively, most approaches that consider multiple variants in association studies find fewer associations than the single SNP association test. This is due to the fact that most multiple variant methods assume a structure of allelic heterogeneity which is very different from what is observed in genetic studies. In this work, we propose a new statistical method, Model-based Association test Reflecting causal Status (MARS), that tries to find an association between variants in risk loci and a phenotype, considering multiple variants at each locus. One of the main advantages of MARS is that it only requires the existing summary statistics to detect associated risk loci. Thus, MARS is applicable to any association study with summary statistics, even though individual level data is not available for the study. Utilizing extensive simulated data sets, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while robustly controls the type I error. Applied to data of 44 tissues provided by the Genotype- Tissue Expression ( GTEx ) consortium, we show that MARS identifies more eGenes compared to previous approaches in most of the tissues; e.g. MARS identified 16% more eGenes than the ones reported by the GTEx consortium. Moreover, applied to Northern Finland Birth Cohort (NFBC) data, we demonstrate that MARS effectively identifies association loci with improved power (56% of more loci found by MARS) in GWAS studies compared to the standard association test. Bio: Dr. Eleazar Eskin serves as the inaugural chair for the UCLA Department of Computational Medicine. Fascinated by the intersection of computer science and biology, Dr. Eskin is researching and developing computational methods for the analysis of genetic variations in human disease. There are millions of variants in the genome and identifying the variants involved in disease often requires tens of thousands of patient samples. In order to analyze these tremendously large datasets, Dr. Eskin and his team are solving challenging computational problems and developing new computational techniques. He received his PhD in computer science from Columbia University. A recipient of the Alfred P. Sloan Foundation Research Fellowship, Dr. Eskin’s work is supported by the National Science Foundation and the National Institutes of Health.

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Speaker: David Hay, Friday, 11/22 at 11:00AM
by Emily C. Lawrence 18 Nov '19

18 Nov '19

CS Speaker David Hay, Hebrew University Friday, November 22 - 11:00AM Computer Science - Room 105 Host: Jennifer Rexford [ https://www.cs.princeton.edu/events/25900 | https://www.cs.princeton.edu/events/25900 ] Network Level IoT Security Computer networks have undergone and continue to experience a major transformation, whereby billions of low-cost devices are being connected to the network to provide additional functionality and better user experience. Unlike traditional network devices, these devices, collectively known as the ``Internet of Things'' ( IoT ), typically have very limited computational, memory, and power resources. These IoT devices became a major security concerns, both due to human factors and to technical challenges in deploying security mechanisms on devices with low resources. The number and diversity of IoT devices creates a huge attack surface that is often exploited by attackers to launch large-scale attacks, sometimes exploiting well-known vulnerabilities. This talk will highlight the security concerns of IoT devices from a networking perspective and explore how to secure IoT devices using whitelists, in which communication between a device and an endpoint is prohibited unless that endpoint appears in the corresponding whitelist. Finally, we will discussed deployment options for such a solution (namely, within the internet gateway, as virtual network function within the ISP network, or a combination of the two). Bio: David Hay is an Associate Professor with the Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel. He received the B.A. (summa cum laude) and Ph.D. degrees in computer science from the Technion —Israel Institute of Technology, Haifa, Israel, in 2001 and 2007, respectively. In addition, he was with IBM Haifa Research Labs, Haifa, Israel; Cisco Systems, San Jose, CA, USA; the Electronic Department, Politecnico di Torino, Turin, Italy; and the Electrical Engineering Department with Columbia University, New York, NY, USA. In 2010, he co-founded (with Prof. Brembler -Barr) the DEEPNESS lab, focusing on deep packet inspection in next-generation network devices. He has served as a technical program committee member of numerous networking conferences, and since 2018 serves as en editor of ACM/IEEE Transactions on Networking. His research interests are in computer networks—in particular, network algorithmics , packet classification, deep packet inspection, network survivability and resilience, software-defined networking, network-function virtualization, and various aspects of network security.

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Francis Bach TODAY at 12:30pm
by Emily C. Lawrence 15 Nov '19

15 Nov '19

Distinguished Colloquium Speaker Francis Bach from INRIA Friday, November 15 - 12:30pm Computer Science - Room 105 Hosts: Barbara Engelhardt, Sanjeev Arora, Peter Ramadge [ https://www.cs.princeton.edu/events/25882 | https://www.cs.princeton.edu/events/25882 ] Can learning theory resist deep learning? Machine learning algorithms are ubiquitous in most scientific, industrial and personal domains, with many successful applications. As a scientific field, machine learning has always been characterized by the constant exchanges between theory and practice, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Many of the recent and well publicized applications come from deep learning, where these exchanges are harder to make, in part because the objective functions used to train neural networks are not convex. In this talk, I will present recent results on the global convergence of gradient descent for some specific non-convex optimization problems, illustrating these difficulties and the associated pitfalls (joint work with Lénaïc Chizat and Edouard Oyallon ). Bio: Francis Bach is a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure , and a joint team between CNRS, ENS and INRIA. Since 2016, he is an adjunct Professor at Ecole Normale Supérieure . He completed his Ph.D. in Computer Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris, he then joined the WILLOW project-team at INRIA/ Ecole Normale Superieure /CNRS from 2007 to 2010. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council, and received the Inria young researcher prize in 2012, the ICML test-of-time award in 2014, as well as the Lagrange prize in continuous optimization in 2018. In 2015, he was program co-chair of the International Conference in Machine learning (ICML), and general chair in 2018; he is now co-editor- inchief of the Journal of Machine Learning Research. Francis Bach is primarily interested in machine learning, and especially in graphical models, sparse methods, kernel-based learning, large-scale convex optimization, computer vision and signal processing.

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Reminder: Colloquium Speaker Francis Bach, tomorrow at 12:30pm
by Emily C. Lawrence 14 Nov '19

14 Nov '19

Distinguished Colloquium Speaker Francis Bach from INRIA Friday, November 15 - 12:30pm Computer Science - Room 105 Hosts: Barbara Engelhardt, Sanjeev Arora, Peter Ramadge [ https://www.cs.princeton.edu/events/25882 | https://www.cs.princeton.edu/events/25882 ] Can learning theory resist deep learning? Machine learning algorithms are ubiquitous in most scientific, industrial and personal domains, with many successful applications. As a scientific field, machine learning has always been characterized by the constant exchanges between theory and practice, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Many of the recent and well publicized applications come from deep learning, where these exchanges are harder to make, in part because the objective functions used to train neural networks are not convex. In this talk, I will present recent results on the global convergence of gradient descent for some specific non-convex optimization problems, illustrating these difficulties and the associated pitfalls (joint work with Lénaïc Chizat and Edouard Oyallon ). Bio: Francis Bach is a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure , and a joint team between CNRS, ENS and INRIA. Since 2016, he is an adjunct Professor at Ecole Normale Supérieure . He completed his Ph.D. in Computer Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris, he then joined the WILLOW project-team at INRIA/ Ecole Normale Superieure /CNRS from 2007 to 2010. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council, and received the Inria young researcher prize in 2012, the ICML test-of-time award in 2014, as well as the Lagrange prize in continuous optimization in 2018. In 2015, he was program co-chair of the International Conference in Machine learning (ICML), and general chair in 2018; he is now co-editor- inchief of the Journal of Machine Learning Research. Francis Bach is primarily interested in machine learning, and especially in graphical models, sparse methods, kernel-based learning, large-scale convex optimization, computer vision and signal processing.

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Jeff Mogul, TODAY at 12:30pm
by Emily C. Lawrence 13 Nov '19

13 Nov '19

Distinguished CS Colloquium Speaker Jeff Mogul, Google Networking Infrastructure Wednesday, November 13 - 12:30pm Computer Science - Room 105 Host: Kyle Jamieson [ https://www.cs.princeton.edu/events/25892 | https://www.cs.princeton.edu/events/25892 ] Self-Driving Networks: Is it a good idea to mix AI and computer networks? Just as you wouldn't want to be in a car without seat belts, even if it's a self-driving car, you probably don't want your Self-Driving Network ( SelfDN ) to rely completely on control-theory or ML, no matter how sophisticated or formally verified. While autonomous systems promise to solve many problems that afflict networks, just as they promise to solve many problems that afflict our roadways, they are not immune from a multitude of practical problems and unexpected consequences. At Google, we have been moving towards highly-automated networking, and along the way we have learned a lot about the risks involved, and how to build robust networks in spite of the wonders of automation. I will talk about some of the problems that are likely to afflict SelfDNs as they enter the real world. Bio: Jeff Mogul works on fast, cheap, reliable, and flexible networking infrastructure for Google. Until 2013, he was a Fellow at HP Labs, doing research primarily on computer networks and operating systems issues for enterprise and cloud computer systems; previously, he worked at the DEC/Compaq Western Research Lab. He received his PhD from Stanford in 1986, an MS from Stanford in 1980, and an SB from MIT in 1979. He is an ACM Fellow. Jeff is the author or co-author of several Internet Standards; he contributed extensively to the HTTP/1.1 specification. He was an associate editor of Internetworking: Research and Experience, and has been the chair or co-chair of a variety of conferences and workshops, including SIGCOMM, OSDI, NSDI, USENIX, HotOS , and ANCS.

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Reminder: Colloquium Speaker Jeff Mogul, tomorrow at 12:30pm
by Emily C. Lawrence 12 Nov '19

12 Nov '19

Distinguished CS Colloquium Speaker Jeff Mogul, Google Networking Infrastructure Wednesday, November 13 - 12:30pm Computer Science - Room 105 Host: Kyle Jamieson [ https://www.cs.princeton.edu/events/25892 | https://www.cs.princeton.edu/events/25892 ] Self-Driving Networks: Is it a good idea to mix AI and computer networks? Just as you wouldn't want to be in a car without seat belts, even if it's a self-driving car, you probably don't want your Self-Driving Network ( SelfDN ) to rely completely on control-theory or ML, no matter how sophisticated or formally verified. While autonomous systems promise to solve many problems that afflict networks, just as they promise to solve many problems that afflict our roadways, they are not immune from a multitude of practical problems and unexpected consequences. At Google, we have been moving towards highly-automated networking, and along the way we have learned a lot about the risks involved, and how to build robust networks in spite of the wonders of automation. I will talk about some of the problems that are likely to afflict SelfDNs as they enter the real world. Bio: Jeff Mogul works on fast, cheap, reliable, and flexible networking infrastructure for Google. Until 2013, he was a Fellow at HP Labs, doing research primarily on computer networks and operating systems issues for enterprise and cloud computer systems; previously, he worked at the DEC/Compaq Western Research Lab. He received his PhD from Stanford in 1986, an MS from Stanford in 1980, and an SB from MIT in 1979. He is an ACM Fellow. Jeff is the author or co-author of several Internet Standards; he contributed extensively to the HTTP/1.1 specification. He was an associate editor of Internetworking: Research and Experience, and has been the chair or co-chair of a variety of conferences and workshops, including SIGCOMM, OSDI, NSDI, USENIX, HotOS , and ANCS.

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