April 2017 - talks - lists.cs.princeton.edu

Physics 42nd annual Hamilton Lecture: Thurs. May 4; Charles Kane (U.Penn), “Topological Phases of Matter,” 8 p.m., McDonnell A-02
by Nicole E. Wagenblast 28 Apr '17

28 Apr '17

Princeton University Department of Physics 42 nd annual Donald R. Hamilton Lecture Charles Kane University of Pennsylvania “ Topological Phases of Matter ” Matter can arrange itself in the most ingenious ways. In addition to the solid, liquid and gas phases that are familiar in classical physics, quantum mechanics enables the existence of electronic phases of matter that can have both exotic and useful properties. In the last century, the thorough understanding of the simplest quantum electronic phase - the electrical insulator - enabled the development of the solid state electronics technology that is ubiquitous in today's information age. In the present century, new "topological" electronic phases are being discovered that may enable future technologies by allowing the seemingly impossible to occur: indivisible objects, like an electron or a quantum bit of information, can be split into two, allowing mysterious features of quantum mechanics to be harnessed. Our understanding of topological phases, which was celebrated by the 2016 Nobel Prize in physics, builds on deep ideas in mathematics. We will try to convey that they are as beautiful as they are fundamental. 8 p.m., Thursday, May 4, 2017 McDonnell Hall, Auditorium A-02 Host: Paul Steinhardt, Princeton Center for Theoretical Science A free lecture open to the public. Princeton University Featured Event Save

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Yingfei Wang will present her FPO, "Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications" on Tuesday, 5/9/2017 at 1pm in CS 302.
by Nicki Gotsis 27 Apr '17

27 Apr '17

Yingfei Wang will present her FPO, "Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications" on Tuesday, 5/9/2017 at 1pm in CS 302. The members of her committee are: Adviser: Warren Powell (ORFE) Readers: Bernard Chazelle and Mengdi Wang (ORFE) Examiners: Szymon Rusinkiewicz, Han Liu (ORFE) and Warren Powell (ORFE) A copy of her thesis is available in Room 310. Everyone is invited to attend her talk. The talk abstract follow below: This thesis considers the problem of sequentially making decisions under uncertainty, exploring the ways where efficient information collection influences and improves decision-making strategies. Most previous optimal learning approaches are restricted to fully sequential settings with Gaussian noise models where exact analytic solutions can be easily obtained. In this thesis, we bridge the gap between statistics, machine learning and optimal learning by providing a comprehensive set of techniques that span from designing appropriate stochastic models to describe the uncertain environment, to proposing novel statistical models and inferences, to finite-time and asymptotic guarantees, with an emphasis on how ecient information collection can expand access, decrease costs and improve quality in health care. Specifically, we provide the first finite-time bound for the knowledge gradient policy. Since there are many situations where the outcomes are dichotomous, we consider the problem of sequentially making decisions that are rewarded by “successes” and “failures”. The binary outcome can be predicted through an unknown relationship that depends on partially controllable attributes of each instance. With the adaptation of an online Bayesian linear classifier, we design a knowledge gradient (KG) policy to guide the experiment. Motivated by personalized medicine where a treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals, we further extend our knowledge gradient policy to a Bayesian contextual bandits setting. Since the sparsity and the relatively small number of patients make leaning more dicult, we design an ensemble optimal learning method, in which multiple models are strategically generated and combined to minimize the incorrect selection of a particularly poorly performing statistical model. Driven by numerous needs among materials science society, we developed a KG policy for sequential experiments when experiments can be conducted in parallel and/or there are multiple tunable parameters which are decided at di↵erent stages in the process. Finally, we present a new Modular, Optimal Learning Testing Environment (MOLTE) as a public-domain test environment to facilitate the process of more comprehensive comparisons, on a broader set of test problems and a broader set of policies.

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Talk: Building a new decentralized internet with Blockstack- Today 1:30pm
by Nicole E. Wagenblast 26 Apr '17

26 Apr '17

Time : 1:30pm to 2:50pm Location : Friend 003 Title : Building a new decentralized internet with Blockstack Abstract : The internet was designed more than 30 years ago and is showing signs of age. Recent attacks on internet infrastructure, like DDoS attacks on the Domain Name System (DNS), and cases of fraudulent security certificates issued by certificate authorities (CAs) have exposed the ugly underbelly of the internet along with flaws in the internet's original design. Centralized services and hidden middlemen introduce points of failure and control. In this talk, we present the design and implementation of a new internet architecture that explicitly follows the trust-to-trust design principle and remotes any centralized point of trust from the middle of the network. More specifically, we present the design of (1) a decentralized naming and PKI system, (2) a decentralized storage system that gives comparable performance to traditional cloud storage services and (3) discuss how blockchains can be used to bootstrap trust in these new decentralized systems. Further, we present our lessons from operating one of the largest production deployments of blockchain-based systems, with over 70,000 domains registered, and discuss how our experiences helped evolve our design over time. Our new system, called Blockstack, is released as open-source and has an open-source community of 5000+ members. Bio : Muneeb Ali is the co-founder of Blockstack, a new decentralized internet where users own their data and apps run locally without remote servers. Muneeb is an alumnus of Y Combinator and has raised $5.1M from Union Square Ventures, Lux Capital, Naval Navikant, and others for his startup. Muneeb is also a final-year PhD candidate at Princeton University, where he has worked in the Systems and Networks group and at PlanetLab. He helped start a new course at Princeton on “How to be a CTO” and gives guest lectures on cloud computing at Princeton. Muneeb has been awarded a J. William Fulbright Fellowship and has served as the Program Co-Chair of ACM NSDR. He has built a wide range of production systems and published research papers with over 900 citations.

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Colloquium Speaker: Po-Ru Loh, Today, 12:30pm
by Nicole E. Wagenblast 25 Apr '17

25 Apr '17

Colloquium Speaker Po-Ru Loh, Harvard University Tuesday, April 25, 2017 - 12:30pm Computer Science 105 Host: Prof. Mona Singh Fast statistical methods for big data genetics As genomic data sizes have grown exponentially over the past decade, efficient statistical analysis has become a key challenge in quantitative genetics. I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and somatic structural variant detection, each taking place at the intersection of mathematics, computer science, and genetics. I am currently a Postdoctoral Research Associate in Statistical Genetics at the Harvard T.H. Chan School of Public Health working with Dr. Alkes Price. The broad aim of my research is to develop efficient computational algorithms to analyze very large genetic data sets. My current areas of focus include (1) fast algorithms for genotype phasing and imputation, (2) linear mixed model methods for heritability analysis, association testing, and risk prediction, and (3) computational phase-based detection of pre-cancerous mosaic chromosomal aberrations. Several of my research projects have resulted in software packages now in use by the genetics community. Prior to joining the Harvard Chan School in 2013, I received my Ph.D. in Applied Mathematics from the Massachusetts Institute of Technology and my B.S. in Mathematics from the California Institute of Technology.

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Colloquium Speaker: Po-Ru Loh, Tueseday, April 25th, 12:30pm- reminder
by Nicole E. Wagenblast 24 Apr '17

24 Apr '17

Colloquium Speaker Po-Ru Loh, Harvard University Tuesday, April 25, 2017 - 12:30pm Computer Science 105 Host: Prof. Mona Singh Fast statistical methods for big data genetics As genomic data sizes have grown exponentially over the past decade, efficient statistical analysis has become a key challenge in quantitative genetics. I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and somatic structural variant detection, each taking place at the intersection of mathematics, computer science, and genetics. I am currently a Postdoctoral Research Associate in Statistical Genetics at the Harvard T.H. Chan School of Public Health working with Dr. Alkes Price. The broad aim of my research is to develop efficient computational algorithms to analyze very large genetic data sets. My current areas of focus include (1) fast algorithms for genotype phasing and imputation, (2) linear mixed model methods for heritability analysis, association testing, and risk prediction, and (3) computational phase-based detection of pre-cancerous mosaic chromosomal aberrations. Several of my research projects have resulted in software packages now in use by the genetics community. Prior to joining the Harvard Chan School in 2013, I received my Ph.D. in Applied Mathematics from the Massachusetts Institute of Technology and my B.S. in Mathematics from the California Institute of Technology.

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Colloquium Speaker: Po-Ru Loh, Tueseday, April 25th, 12:30pm
by Nicole E. Wagenblast 20 Apr '17

20 Apr '17

Colloquium Speaker Po-Ru Loh, Harvard University Tuesday, April 25, 2017 - 12:30pm Computer Science 105 Host: Prof. Mona Singh Fast statistical methods for big data genetics As genomic data sizes have grown exponentially over the past decade, efficient statistical analysis has become a key challenge in quantitative genetics. I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and somatic structural variant detection, each taking place at the intersection of mathematics, computer science, and genetics. I am currently a Postdoctoral Research Associate in Statistical Genetics at the Harvard T.H. Chan School of Public Health working with Dr. Alkes Price. The broad aim of my research is to develop efficient computational algorithms to analyze very large genetic data sets. My current areas of focus include (1) fast algorithms for genotype phasing and imputation, (2) linear mixed model methods for heritability analysis, association testing, and risk prediction, and (3) computational phase-based detection of pre-cancerous mosaic chromosomal aberrations. Several of my research projects have resulted in software packages now in use by the genetics community. Prior to joining the Harvard Chan School in 2013, I received my Ph.D. in Applied Mathematics from the Massachusetts Institute of Technology and my B.S. in Mathematics from the California Institute of Technology.

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Mojgan Ghasemi will present her Pre FPO on Tuesday, April 25th, 3:30-5:30pm in J401, Engineering Quadrangle
by Nicki Gotsis 20 Apr '17

20 Apr '17

Mojgan Ghasemi will present her Pre FPO on Tuesday April 25th, 3:30-5:30pm in J401, Engineering Quadrangle. The members of her committee are Jennifer Rexford (Advisor, reader), Theophilus Benson (reader - Duke), Mung Chiang (reader), Nick Feamster (non-reader), Prateek Mittal (non-reader). Everyone is invited to attend her talk. The talk title and abstract follow below: Title: Data-Driven Management of CDN Performance Abstract: Content Distribution Networks (CDNs) carry most of the web content, with the goal of offering good performance to users while keeping the cost low. In this talk, we introduce measurement and analysis techniques to help CDNs balance these goals. First, we allocate resources across the distributed edge servers to jointly minimize network latency and cache miss rates, and evaluate our methods using server request logs from a commercial CDN. Second, we introduced fine-grained measurement of the end-to-end video delivery path to detect and diagnose performance problems for a commercial content provider.Finally, we dive deeper into diagnosing network problems by monitoring TCP connections directly in the network devices.

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MAE Talk: Maja Mataric, Socially Assistive Robotics: Creating Robots That Care, Friday, April 21- 3:30pm
by Nicole E. Wagenblast 18 Apr '17

18 Apr '17

Mechanical and Aerospace Engineering Talk Socially Assistive Robotics: Creating Robots That Care Maja Mataric, University of Southern California Friday, April 21, 2017 - 3:30pm Maeder Hall, ACEE Socially assistive robotics (SAR) is a new field of intelligent robotics that focuses on developing machines capable of assisting users through social rather than physical interaction. The robot’s physical embodiment is at the heart of SAR’s effectiveness, as it leverages the inherently human tendency to engage with lifelike (but not necessarily human-like or otherwise biomimetic) social behavior. People readily ascribe intention, personality, and emotion to robots; SAR leverages this engagement stemming from non-contact social interaction involving speech, gesture, movement demonstration and imitation, and encouragement, to develop robots capable of monitoring, motivating, and sustaining user activities and improving human learning, training, performance and health outcomes. Human-robot interaction (HRI) for SAR is a growing multifaceted research area at the intersection of engineering, health sciences, neuroscience, social, and cognitive sciences. This talk will describe our research into embodiment, modeling and steering social dynamics, and long-term user adaptation for SAR. The research will be grounded in projects involving analysis of multi-modal activity data, modeling personality and engagement, formalizing social use of space and non-verbal communication, and personalizing the interaction with the user over a period of months, among others. The presented methods and algorithms will be validated on implemented SAR systems evaluated by human subject cohorts from a variety of user populations, including stroke patients, children with autism spectrum disorder, and elderly with Alzheimers and other forms of dementia. Maja Mataric´ is the Chan Soon-Shiong Professor of Computer Science, Neuroscience, and Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (rasc.usc.edu) and her Interaction Lab (robotics.usc.edu/interaction). She received a PhD and MS from MIT and BS from the University of Kansas. She is Fellow of AAAS, IEEE, and AAAI, and recipient of the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring, Anita Borg Institute Women of Vision Award in Innovation, NSF Career, MIT TR35 Innovation, and IEEE RAS Early Career Awards, among others. A founder of the rapidly growing field of socially assistive robotics, her research is focused on enabling robots to help through non physical means: through motivation, companionship and coaching. Her work has been validated in therapy, rehabilitation, training, and education for children with autism, stroke survivors, Alzheimer's patients and other special needs populations. She is passionate about conveying the importance of engineering research and careers in STEM to K-12 students and teachers, women and other underrepresented groups in engineering, the media, and policy makers.

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Colloquium Speaker: Rashmi Vinayak, Today- 4:30pm
by Nicole E. Wagenblast 13 Apr '17

13 Apr '17

Colloquium Speaker Departments of Computer Science and Electrical Engineering Rashmi Vinayak, University of California, Berkeley Thursday, April 13, 2017- 4:30pm Computer Science 105 Host: Prof. Kyle Jamieson (CS) and Prof. Naveen Verma (EE) Smart redundancy for big-data systems: Theory and Practice Large-scale distributed storage and caching systems form the foundation of big-data systems. A key scalability challenge in distributed storage systems is achieving fault tolerance in a resource-efficient manner. Towards addressing this challenge, erasure codes provide a storage-efficient alternative to the traditional approach of data replication. However, classical erasure codes come with critical drawbacks: while optimal in utilizing storage space, they significantly increase the usage of other important cluster resources such as network and I/O. In the first part of the talk, I present new erasure codes and theoretical optimality guarantees. The proposed codes reduce the network and I/O usage by 35-70% for typical parameters while retaining the storage efficiency of classical codes. I then present an erasure-coded storage system that employs the proposed codes, and demonstrate significant benefits over the state-of-the-art in evaluations under production setting at Facebo ok. Our codes have been integrated into Apache Hadoop 3.0. The second part of the talk focuses on achieving high performance in distributed caching systems. These systems routinely face the challenges of skew in data popularity, background traffic imbalance, and server failures, which result in load imbalance across servers and degradation in read latencies. I present EC-Cache, a cluster cache that employs erasure coding to achieve a 3-5x improvement as compared to the state-of-the-art. Rashmi K. Vinayak received her PhD in the EECS department at UC Berkeley in 2016, where she is now a postdoctoral researcher at AMPLab/RISELab and BLISS working with Ion Stoica and Kannan Ramchandran. Her dissertation received the Eli Jury Award 2016 from the EECS department at UC Berkeley for outstanding achievement in the area of systems, communications, control, or signal processing. Rashmi is the recipient of the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012. She is also a recipient of the Facebook Fellowship 2012-13, the Microsoft Research PhD Fellowship 2013-15, and the Google Anita Borg Memorial Scholarship 2015-16. Her research interests lie in the theoretical and system challenges that arise in storage and analysis of big data.

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Friday: Quantitative Social Science Colloquium on Internet Censorship Measurement
by Nick Feamster 12 Apr '17

12 Apr '17

All, I am giving a talk in the Quantitative Social Science (QSS) seminar on our Internet censorship measurement work over the past 4+ years. The talk is on Friday at 12p in Corwin Hall: http://q-aps.princeton.edu/book/QSS-seminar Come to the talk to learn about how we have managed to get continuous measurements of Internet censorship from 170+ countries around the world. -Nick > Begin forwarded message: > > ————— > > Global Measurements of Internet Censorship > Nick Feamster, Princeton University > > Internet users in many countries around the world are subject to various forms of censorship and information control. Despite its widespread nature, however, measuring Internet censorship on a global scale has remained an elusive goal. Internet censorship is known to vary across time and within regions (and Internet Service Providers) within a country. To capture these complex dynamics, Internet censorship must be both continuous and distributed across a large number of vantage points. To date, gathering such information has required recruiting volunteers to perform measurements from within countries of interest; this approach does not permit collection of continuous measurements, and it also does not permit collection from a large number of measurement locations; it may also put the people performing the measurements at risk. Over the past four years, we have developed a collection of measurement techniques to surmount the limitations of these conventional approaches. In this talk, I will describe three such techniques: (1) Encore, a tool that performs cross-origin requests to measure Web filtering; (2) Augur, a tool that exploits side-channel information in the Internet Protocol (IP) to measure filtering using network-level access control lists; and (3) a tool to measure DNS filtering using queries through open DNS resolvers. These three tools allow us—for the first time ever—to characterize Internet censorship continuously, from hundreds of countries around the world, at different layers of the network protocol stack. each of these techniques involves both technical and ethical challenges. I will describe some of the challenges that we faced in designing and implementing these tools, how we tackled these challenges, our experiences with measurements to date, and our plans for the future. Long term, our goal is to collaborate with social scientists to bring data to bear on a wide variety of questions concerning Internet censorship and information control; I will conclude with an appeal to cross-disciplinary work in this area and some ideas for how computer scientists and social scientists might work together on some of these pressing questions going forward. > > This research is in collaboration with Sam Burnett, Roya Ensafi, Paul Pearce, Ben Jones, Frank Li, and Vern Paxson.

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