November 2019 - talks - lists.cs.princeton.edu

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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Colloquium Speaker: Francis Bach, Friday 11/15 at 12:30pm
by Emily C. Lawrence 08 Nov '19

08 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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TODAY! Joint Princeton Optimization Seminar/CSML Seminar: 4:30pm in Sherrerd Hall 101: Elisa Celis, Yale University
by Emily Lawrence 07 Nov '19

07 Nov '19

Speaker: Elisa Celis Assistant Professor of Statistics and Data Science Yale University Title: Optimizing for Fairness in ML Day: Thursday, November 7, 2019 Time: 4:30-5:30 Room: Sherrerd 101 Host: CSML and ORFE, S. S. Wilks Memorial Seminar in Statistics Bio: Elisa Celis is an Assistant Professor of Statistics and Data Science at Yale University. Elisa’s research focuses on problems that arise at the interface of computation and machine learning and its societal ramifications. Specifically, she studies the manifestation of social and economic biases in our online lives via the algorithms that encode and perpetuate them. Her work spans multiple areas including social computing and crowdsourcing, data science, and algorithm design with a current emphasis on fairness and diversity in artificial intelligence and machine learning. Elisa received her B.Sci. from Harvey Mudd College in 2006 in computer science & mathematics, and a Ph.D. in computer science & engineering from the University of Washington in 2012. Prior to joining Yale, she was a research scientist at Xerox Research India where she managed the crowdsourcing research thrust worldwide, and a senior research scientist at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland in computer and information sciences, where she was a co-founder of the Computation Nature and Society Think Tank. Abstract: Recent events have made evident the fact that algorithms can be discriminatory, reinforce human prejudices, accelerate the spread of misinformation, and are generally not as objective as they are widely thought to be. In this talk, I will present some vignettes from my recent work addressing the problem of social bias in ML models. In particular, correcting for housing and employment discrimination in online advertising, auditing social and intersectional biases in contextual NLP systems, and diversifying image search results via simple and fast approaches that do not require labeled training data. This work leads to new ML approaches that have the ability to alleviate bias and increase diversity while simultaneously maintaining theoretical or empirical performance with respect to their original metrics.

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Colloquium Speaker: Tushar Krishna, TODAY at 12:30
by Emily C. Lawrence 07 Nov '19

07 Nov '19

CS Colloquium Speaker Tushar Krishna from Georgia Tech Thursday, November 7 - 12:30pm E-Quad, B205 Host: Margaret Martonosi [ https://www.cs.princeton.edu/events/25888 | https://www.cs.princeton.edu/events/25888 ] Enabling Continuous Learning through Synaptic Plasticity in Hardware Ever since modern computers were invented, the dream of creating artificial intelligence (AI) has captivated humanity. We are fortunate to live in an era when, thanks to deep learning (DL), computer programs have paralleled, and in many cases even surpassed human level accuracy in tasks like visual perception and speech synthesis. However, we are still far away from realizing general-purpose AI. The problem lies in the fact that the development of supervised learning based DL solutions today is mostly open loop. A typical DL model is created by hand-tuning the deep neural network (DNN) topology by a team of experts over multiple iterations, followed by training over petabytes of labeled data. Once trained, the DNN provides high accuracy for the task at hand; if the task changes, however, the DNN model needs to be re-designed and re-trained before it can be deployed. A general-purpose AI system, in contrast, needs to have the ability to constantly interact with the environment and learn by adding and removing connections within the DNN autonomously, just like our brain does. This is known as synaptic plasticity. In this talk we will present our research efforts towards enabling general-purpose AI leveraging plasticity in both the algorithm and hardware. First, we will present GeneSys (MICRO 2018), a HW-SW prototype of a closed loop learning system for continuously evolving the structure and weights of a DNN for the task at hand using genetic algorithms, providing 100-10000x higher performance and energy-efficiency over state-of-the-art embedded and desktop CPU and GPU systems. Next, we will present a DNN accelerator substrate called MAERI (ASPLOS 2018), built using light-weight, non-blocking, reconfigurable interconnects, that supports efficient mapping of regular and irregular DNNs with arbitrary dataflows , providing ~100% utilization of all compute units, resulting in 3X speedup and energy-efficiency over our prior work Eyeriss (ISSCC 2016). Finally, time permitting, we will describe our research in enabling rapid design-space exploration and prototyping of hardware accelerators using our dataflow DSL + cost-model called MAESTRO (MICRO 2019). Bio: Tushar Krishna is an Assistant Professor in the School of Electrical and Computer Engineering at Georgia Tech. He also holds the ON Semiconductor Junior Professorship. He has a Ph.D. in Electrical Engineering and Computer Science from MIT (2014), a M.S.E in Electrical Engineering from Princeton University (2009), and a B.Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Delhi (2007). Before joining Georgia Tech in 2015, Dr. Krishna spent a year as a post-doctoral researcher at Intel, Massachusetts. Dr. Krishna’s research spans computer architecture, interconnection networks, networks-on-chip ( NoC ) and deep learning accelerators - with a focus on optimizing data movement in modern computing systems. Three of his papers have been selected for IEEE Micro’s Top Picks from Computer Architecture, one more received an honorable mention, and two have won best paper awards. He received the National Science Foundation (NSF) CRII award in 2018, and both a Google Faculty Award and a Facebook Faculty Award in 2019. He also received the “Class of 1940 Course Survey Teaching Effectiveness” Award from Georgia Tech in 2018.

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