- talks - lists.cs.princeton.edu

Fwd: EE Seminar - November 24, 2014, 4:30 pm, E-Quad B205 - Prof. Lakshminarayanan Subramanian
by Jennifer Rexford 18 Nov '14

18 Nov '14

> From: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > Date: November 18, 2014 at 3:12:35 PM EST > To: ee-seminar(a)Princeton.EDU > Subject: EE Seminar - November 24, 2014, 4:30 pm, E-Quad B205 - Prof. Lakshminarayanan Subramanian > Reply-To: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > > > > Speaker: Prof. Lakshminarayanan Subramanian > New York University > Title: Societal Network Science: Where Societal Networks meets Data Science > Date: Monday, November 24, 2014 > Time: 4:30 p.m. > Room: E-Quad, Room B205 > Host: Prof. Prateek Mittal > > Abstract: This talk focuses on a new emerging area of research, which we term "Societal Network Science", where the goal is to understand important societal, social and human indicators based on the network characteristics of how humans, physical and digital entities interact. Societal networks aim to capture meaningful human interaction patterns and the potential causal relationships between these interactions. In this talk, we will outline our recent results on three related but seemingly disparate problems in societal network science: disease surveillance, road traffic networks and event networks. In the space of disease surveillance, our group has worked on a 3-year long project to build a fine-grained dengue surveillance system in the country of Pakistan, where we can predict the incidence level of dengue using citizen driven data at a sub-city granularity, 14 days ahead of time. In the space of road traffic networks, we will illustrate how one can predict sudden traffic jams in NY city with high confidence 3 minutes before they happen; surprisingly, 3 minutes is sufficient to potentially avoid the jam too! In the space of event networks, we will show how one can use NLP techniques to learn networks of real world events from news corpora and how such event networks can be used in the prediction of real-world social phenomena like drought, price variations and disease outbreaks. Our longer term vision is to build an automated analytics engine for societal network science that can learn and infer socio-economic phenomena from highly diverse and noisy data sources. > > Biography: Lakshminarayanan Subramanian is an Associate Professor in the Courant Institute of Mathematical Sciences at NYU. His research interests are in the areas of networked systems, big data analytics and computing for development. He is a member of the NYU Systems group and co-established the Center for Technology and Economic Development (CTED) at NYU. He is also affiliated with the Center for Data Science and the Global Institute of Public Health (GIPH) at NYU. He is the co-founder of Entrupy Inc, a startup on surface fingerprinting. He completed his PhD from UC Berkeley and undergraduate studies from IIT-Madras. > >

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Colloquium Speaker Emma Brunskill Wed Nov 19, 4:30pm
by Nicole E. Wagenblast 18 Nov '14

18 Nov '14

Colloquium Speaker Emma Brunskill , Carnegie Mellon University Wednesday, November 19, 2014 - 4:30pm Computer Science 105 Interactive ML for People: The Small Data Problem Consider an intelligent tutoring system or an autonomous decision support tool for a doctor. Though such systems may in aggregate have a huge amount of data, the data collected for a single individual is typically very small, and the policy space (of what to next teach a student or how to help treat a patient) is enormous. I will describe two machine learning efforts to tackle these small data challenges: learning across multiple tasks, and better use of previously collected task data, where tasks in both cases involve sequential stochastic decision processes (reinforcement learning and bandits). I will also present results of how one of these techniques allowed us to substantially increase engagement in an educational game to teach fractions. Emma Brunskill is an assistant professor in the computer science department at Carnegie Mellon University. She is also affiliated with the machine learning department at CMU. She works on reinforcement learning, focusing on applications that involve artificial agents interacting with people, such as intelligent tutoring systems. She is a Rhodes Scholar, Microsoft Faculty Fellow and NSF CAREER award recipient, and her work has received best paper nominations in Education Data Mining (2012, 2013) and CHI (2014).

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Distinguished Colloquium Speaker Dahlia Malkhi, Today, 4:30pm
by Nicole E. Wagenblast 18 Nov '14

18 Nov '14

Distinguished Colloquium Speaker Dahlia Malkhi, until recently from Microsoft Research, Silicon Valley Tuesday, November 18, 4:30pm Computer Science 105 CorfuDB: Transactional Data Services over a Shared Log Conventional wisdom has it that the only way to scale replicated services is by partitioning the data. What would you do if given an infrastructure that breaks the seeming tradeoff between consistency and scale? The talk will describe our experience with building CorfuDB, a distributed fabric that drives consistency and transactional guarantees at high-throughput. CorfuDB facilitates building distributed services in which in-memory data-structures are backed by a shared log. The core is built around the CORFU log, which clients can append-to and read-from over a network. Internally, CORFU is distributed over a cluster of machines with no single I/O bottleneck to either appends or reads. Atop CORFU is Tango, a fabric for programming transactional data services such as the Hyder DB and an Apache-ZooKeeper alternative. Dahlia Malkhi has been a principal researcher at Microsoft Research, Silicon Valley from 2004 until the lab shut down in Sep 2014. She works on applied and foundational research in reliability, consistency and security of distributed computing since the early nineties. Prior to joining Microsoft Research, Dr. Malkhi was an associate professor at the Hebrew University of Jerusalem (1999-2007), left for a brief sabbatical, but was bitten by the Silicon Valley bug and remained at Microsoft. She holds a PhD (1994), M.Sc and B.Sc in computer science from the Hebrew University of Jerusalem, making her the only CS faculty to return to the Hebrew U. for all four academic stages. Dr. Malkhi was elected an ACM fellow in 2011, received the IBM Faculty award in 2003 and 2004 and the German-Israeli Foundation (G.I.F.) Young Scientist award in 2002. She serves on the editorial boards of the IEEE Transactions of Dependable and Secure Computing and of the Distributed Computing Journal. She chaired LADIS 2012 , Locality 2007, PODC 2006, Locality 2005 and DISC 2002.

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Distinguished Colloquium Speaker Dahlia Malkhi, Tues Nov 18, 4:30pm
by Nicole E. Wagenblast 17 Nov '14

17 Nov '14

Distinguished Colloquium Speaker Dahlia Malkhi, until recently from Microsoft Research, Silicon Valley Tuesday, November 18, 4:30pm Computer Science 105 CorfuDB: Transactional Data Services over a Shared Log Conventional wisdom has it that the only way to scale replicated services is by partitioning the data. What would you do if given an infrastructure that breaks the seeming tradeoff between consistency and scale? The talk will describe our experience with building CorfuDB, a distributed fabric that drives consistency and transactional guarantees at high-throughput. CorfuDB facilitates building distributed services in which in-memory data-structures are backed by a shared log. The core is built around the CORFU log, which clients can append-to and read-from over a network. Internally, CORFU is distributed over a cluster of machines with no single I/O bottleneck to either appends or reads. Atop CORFU is Tango, a fabric for programming transactional data services such as the Hyder DB and an Apache-ZooKeeper alternative. Dahlia Malkhi has been a principal researcher at Microsoft Research, Silicon Valley from 2004 until the lab shut down in Sep 2014. She works on applied and foundational research in reliability, consistency and security of distributed computing since the early nineties. Prior to joining Microsoft Research, Dr. Malkhi was an associate professor at the Hebrew University of Jerusalem (1999-2007), left for a brief sabbatical, but was bitten by the Silicon Valley bug and remained at Microsoft. She holds a PhD (1994), M.Sc and B.Sc in computer science from the Hebrew University of Jerusalem, making her the only CS faculty to return to the Hebrew U. for all four academic stages. Dr. Malkhi was elected an ACM fellow in 2011, received the IBM Faculty award in 2003 and 2004 and the German-Israeli Foundation (G.I.F.) Young Scientist award in 2002. She serves on the editorial boards of the IEEE Transactions of Dependable and Secure Computing and of the Distributed Computing Journal. She chaired LADIS 2012 , Locality 2007, PODC 2006, Locality 2005 and DISC 2002.

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Colloquium Speaker Sebastian Burckhardt Thurs Nov 20, 4:30pm
by Nicole E. Wagenblast 14 Nov '14

14 Nov '14

Colloquium Speaker Sebastian Burckhardt , from Microsoft Research Thursday, November 20, 2014 - 4:30pm Computer Science Small Auditorium (Room 105) Robust Abstractions for Replicated Shared State In the age of cloud-connected mobile devices, users want responsive apps that read and write shared data everywhere, at all times, even if network connections are slow or unavailable. Replication and eventual consistency, while able to deliver this experience, require us to face the complexity of asynchronous update propagation and conflict resolution. Our research goal is to find abstractions that encapsulate this complexity, in order to simplify the programming of distributed applications that are responsive, reactive, and collaborative. In this talk, we first discuss the general principles of eventual consistency. Then, we introduce our programming model, consisting of cloud types (for declarative type-based conflict resolution) and the GLUT model (an operational consistency model based on a global log of update transactions). Finally, we report on our practical experiences with supporting cloud types and GLUT in the TouchDevelop programming language and mobile development environment. Sebastian Burckhardt was born and raised in Basel, Switzerland, where he studied Mathematics at the local University. During an exchange year at Brandeis University, he discovered his affinity to Computer Science and immigrated to the United States. After a few years of industry experience at IBM, he returned to academia and earned his PhD in Computer Science at the University of Pennsylvania. Since then, he has worked as a researcher at Microsoft Research in Redmond. His general research interest is the study of programming models for of concurrent, parallel, and distributed systems. More specific interests include consistency models, concurrency testing, self-adjusting computation, and the concurrent revisions programming model.

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ORFE Optimization Seminar: Moses Charikar, Thursday, Nov 20th at 4:30pm, Sherrerd Hall 101
by Nicole E. Wagenblast 13 Nov '14

13 Nov '14

************************************************************************************** === ORFE Optimization Seminar === DATE: Thursday, November 20, 2014 TIME: 4:30pm ***new time*** LOCATION: Sherrerd Hall room 101 ***new room*** SPEAKER: Moses Charikar, Computer Science Dept., Princeton University TITLE: Spectral Embedding of k-Cliques, Graph Partitioning and k-Means ABSTRACT: We introduce and study a new notion of graph partitioning, intimately connected to k-means clustering. Informally, our graph partitioning objective asks for the optimal spectral simplification of a graph as a disjoint union of k normalized cliques. It is a variant of graph decomposition into expanders (where expansion is not measured w.r.t. the induced graph). Optimizing this new objective is equivalent to clustering the effective resistance embedding of the original graph. Our semidefinite programming based approximation algorithm for the new objective is closely related to spectral clustering: it optimizes the k-means objective on a certain smoothed version of the resistive distance embedding. We also show that spectral clustering applied directly to the original graph gives guarantees for our new objective function. In order to illustrate the power of our new notion, we show that approximation algorithms for our new objective can be used in a black box fashion to approximately recover a partition of a graph into k pieces given a guarantee that a good partition exists with sufficiently large gap in internal and external conductance. Joint work with Pranjal Awasthi, Ravishankar Krishnaswamy, and Ali Kemal Sinop. **************************************************************************************

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Colloquium Speaker Emma Brunskill Wed Nov 19, 4:30pm
by Nicole E. Wagenblast 13 Nov '14

13 Nov '14

Colloquium Speaker Emma Brunskill , Carnegie Mellon University Wednesday, November 19, 2014 - 4:30pm Computer Science 105 Interactive ML for People: The Small Data Problem Consider an intelligent tutoring system or an autonomous decision support tool for a doctor. Though such systems may in aggregate have a huge amount of data, the data collected for a single individual is typically very small, and the policy space (of what to next teach a student or how to help treat a patient) is enormous. I will describe two machine learning efforts to tackle these small data challenges: learning across multiple tasks, and better use of previously collected task data, where tasks in both cases involve sequential stochastic decision processes (reinforcement learning and bandits). I will also present results of how one of these techniques allowed us to substantially increase engagement in an educational game to teach fractions. Emma Brunskill is an assistant professor in the computer science department at Carnegie Mellon University. She is also affiliated with the machine learning department at CMU. She works on reinforcement learning, focusing on applications that involve artificial agents interacting with people, such as intelligent tutoring systems. She is a Rhodes Scholar, Microsoft Faculty Fellow and NSF CAREER award recipient, and her work has received best paper nominations in Education Data Mining (2012, 2013) and CHI (2014).

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Colloquium Speaker Ryan Adams Today, 4:30pm
by Nicole E. Wagenblast 13 Nov '14

13 Nov '14

Better Science Through Better Bayesian Computation Ryan Adams, Harvard University Thursday, November 13, 4:30pm Computer Science 105 As we grapple with the hype of "big data" in computer science, it is important to remember that the data are not the central objects: we collect data to answer questions and inform decisions in science, engineering, policy, and beyond. In this talk, I will discuss my work in developing tools for large-scale data analysis, and the scientific collaborations in neuroscience, chemistry, and astronomy that motivate me and keep this work grounded. I will focus on two lines of research that I believe capture an important dichotomy in my work and in modern probabilistic modeling more generally: identifying the "best" hypothesis versus incorporating hypothesis uncertainty. In the first case, I will discuss my recent work in Bayesian optimization, which has become the state-of-the-art technique for automatically tuning machine learning algorithms, finding use across academia and industry. In the second case, I will discuss scalable Markov chain Monte Carlo and the new technique of Firefly Monte Carlo, which is the first provably correct MCMC algorithm that can take advantage of subsets of data. Ryan Adams is an Assistant Professor of Computer Science at Harvard University, in the School of Engineering and Applied Sciences. He leads the Harvard Intelligent Probabilistic Systems group, whose research focuses on machine learning and computational statistics, with applied collaborations across the sciences. Ryan received his undergraduate training in EECS at MIT and completed his Ph.D. in Physics at Cambridge University as a Gates Cambridge Scholar under David MacKay. He was a CIFAR Junior Research Fellow at the University of Toronto before joining the faculty at Harvard. His Ph.D. thesis received Honorable Mention for the Leonard J. Savage Award for Bayesian Theory and Methods from the International Society for Bayesian Analysis. Ryan has won paper awards at ICML, AISTATS, and UAI, and received the DARPA Young Faculty Award.

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Distinguished Colloquium Speaker Dahlia Malkhi, Tues Nov 18, 4:30pm
by Nicole E. Wagenblast 12 Nov '14

12 Nov '14

Distinguished Colloquium Speaker Dahlia Malkhi, until recently from Microsoft Research, Silicon Valley Tuesday, November 18, 4:30pm Computer Science 105 CorfuDB: Transactional Data Services over a Shared Log Conventional wisdom has it that the only way to scale replicated services is by partitioning the data. What would you do if given an infrastructure that breaks the seeming tradeoff between consistency and scale? The talk will describe our experience with building CorfuDB, a distributed fabric that drives consistency and transactional guarantees at high-throughput. CorfuDB facilitates building distributed services in which in-memory data-structures are backed by a shared log. The core is built around the CORFU log, which clients can append-to and read-from over a network. Internally, CORFU is distributed over a cluster of machines with no single I/O bottleneck to either appends or reads. Atop CORFU is Tango, a fabric for programming transactional data services such as the Hyder DB and an Apache-ZooKeeper alternative. Dahlia Malkhi has been a principal researcher at Microsoft Research, Silicon Valley from 2004 until the lab shut down in Sep 2014. She works on applied and foundational research in reliability, consistency and security of distributed computing since the early nineties. Prior to joining Microsoft Research, Dr. Malkhi was an associate professor at the Hebrew University of Jerusalem (1999-2007), left for a brief sabbatical, but was bitten by the Silicon Valley bug and remained at Microsoft. She holds a PhD (1994), M.Sc and B.Sc in computer science from the Hebrew University of Jerusalem, making her the only CS faculty to return to the Hebrew U. for all four academic stages. Dr. Malkhi was elected an ACM fellow in 2011, received the IBM Faculty award in 2003 and 2004 and the German-Israeli Foundation (G.I.F.) Young Scientist award in 2002. She serves on the editorial boards of the IEEE Transactions of Dependable and Secure Computing and of the Distributed Computing Journal. She chaired LADIS 2012 , Locality 2007, PODC 2006, Locality 2005 and DISC 2002.

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Colloquium Speaker Ryan Adams Thurs Nov 13, 4:30pm
by Nicole E. Wagenblast 12 Nov '14

12 Nov '14

Better Science Through Better Bayesian Computation Ryan Adams, Harvard University Thursday, November 13, 4:30pm Computer Science 105 As we grapple with the hype of "big data" in computer science, it is important to remember that the data are not the central objects: we collect data to answer questions and inform decisions in science, engineering, policy, and beyond. In this talk, I will discuss my work in developing tools for large-scale data analysis, and the scientific collaborations in neuroscience, chemistry, and astronomy that motivate me and keep this work grounded. I will focus on two lines of research that I believe capture an important dichotomy in my work and in modern probabilistic modeling more generally: identifying the "best" hypothesis versus incorporating hypothesis uncertainty. In the first case, I will discuss my recent work in Bayesian optimization, which has become the state-of-the-art technique for automatically tuning machine learning algorithms, finding use across academia and industry. In the second case, I will discuss scalable Markov chain Monte Carlo and the new technique of Firefly Monte Carlo, which is the first provably correct MCMC algorithm that can take advantage of subsets of data. Ryan Adams is an Assistant Professor of Computer Science at Harvard University, in the School of Engineering and Applied Sciences. He leads the Harvard Intelligent Probabilistic Systems group, whose research focuses on machine learning and computational statistics, with applied collaborations across the sciences. Ryan received his undergraduate training in EECS at MIT and completed his Ph.D. in Physics at Cambridge University as a Gates Cambridge Scholar under David MacKay. He was a CIFAR Junior Research Fellow at the University of Toronto before joining the faculty at Harvard. His Ph.D. thesis received Honorable Mention for the Leonard J. Savage Award for Bayesian Theory and Methods from the International Society for Bayesian Analysis. Ryan has won paper awards at ICML, AISTATS, and UAI, and received the DARPA Young Faculty Award.

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