CIS Seminars & Events

Fall 2014 Colloquium Series

Unless otherwise noted, our lectures are held weekly on Tuesday and/or Thursday from 3:00 p.m. to 4:15 p.m. in Wu and Chen Auditorium, Levine Hall.

September 2

Warren Center Speaker
Eva Tordos
Department of Computer Science, Cornell University
"Composable Mechanisms, Learning, and Price of Anarchy in Auctions"

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Abstract: Selfish behavior can often lead to suboptimal outcome for all participants, a phenomenon illustrated by classical examples in game theory, such as the prisoner dilemma .  Over the last decade we have developed good understanding how to quantify the impact of strategic user behavior on overall performance in some concrete games.  In this talk, we will consider online auctions from this perspective. A key property of this environments is that players typically participate in multiple  auctions, have valuations that are complex  functions of multiple outcomes, and are using learning strategies to deal with  an uncertain environment. In this talk we show how to provide robust guarantees for the performance of many simple auctions even in such complex environments.

Bio: Eva Tardos is a Jacob Gould Schurman Professor of Computer Science at Cornell University, was Computer Science department chair 2006-2010. She received her BA and PhD from Eotvos University in Budapest. She joined the faculty at Cornell in 1989. She has been elected to the National Academy of Engineering, the National Academy of Sciences, the American Academy of Arts and Sciences, is an external member of the Hungarian Academy of Sciences, and is the recipient of a number of fellowships and awards including the Packard Fellowship, the Goedel Prize, Dantzig Prize, Fulkerson Prize, and the IEEE Technical Achievement Award. She was editor editor-in-Chief of SIAM Journal of Computing 2004-2009, and is currently editor of several other journals including the Journal of the ACM and Combinatorica, served as problem committee member for many conferences, and was program committee chair for SODA’96, FOCS’05, and EC’13.

Tardos’s research interest is algorithms and algorithmic game theory, the subarea of theoretical computer science theory of designing systems and algorithms for selfish users. Her research focuses on algorithms and games on networks.  She is most known for her work on network-flow algorithms, approximation algorithms, and quantifying the efficiency of selfish routing.

September 9

CIS Distinguished Lecture Series
Jon Moore
"Not-So-Big Data After All: Managing Reference Data with Sirius"

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Abstract: Many interesting “reference datasets” now fit in a single commodity server’s RAM, with more on the way as main memory sizes continue to grow. The open source Sirius library allows developers access to this data in native datastructures while managing the distributed systems heavy lifting of replication and persistence.

In this talk, I’ll describe what “reference data” means so that you can recognize this common use case in your own applications, highlighting problems that Sirius is a good fit for (and those that it isn’t). I’ll describe the overall architecture of a Sirius-based application—-the high-level concept of how it works. Finally, I’ll use a case study from Comcast as an example where Sirius is powering applications serving tens of millions of customers.

This talk covers the highlights of a paper we published in the USENIX Annual Technical Conference this year.

Bio: Jon Moore describes himself as being equally comfortable giving architecture talks and personally writing production-ready code. A Technical Fellow at Comcast Corporation, he runs the Core Application Platforms group that focuses on building scalable, performant, robust software components for the company’s varied software product development groups. His current interests include distributed systems, hypermedia APIs, and fault tolerance. Jon received his Ph.D. in Computer and Information Science from the University of Pennsylvania and currently resides in West Philadelphia.

September 18

Warren Center Speaker
Cynthia Dwork
Microsoft Research Silicon Valley
"Fairness, Awareness, and Privacy"

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Abstract: "Why was my loan application denied?" “Why was I not shown this advertisement?"

This talk will address fairness in classification, where the goal is to prevent discrimination against protected population subgroups in  classification systems while simultaneously preserving utility for the  party carrying out the classification, for example, the advertiser,  bank, or admissions committee. We argue that a classification is fair only when individuals who are similar with respect to the classification task at hand are treated similarly, and this in turn requires understanding of sub-cultures of the population, as well more (not less!) information about the individuals to be classified. Our approach provides a (theoretical) method by which an on-line advertising network can prevent discrimination against protected groups, even when the advertisers are unknown and untrusted.

Having argued that fair classification requires more information, we observe a surprising connection to differential privacy, an approach to privacy-preserving analysis of internet-scale data sets, and show that ideas from that field can lead to solutions to the fair classification problem.

Joint work with Moritz Hardt, Toniann Pitassi, Omer Reingold, and  Richard Zemel ("Fairness Through Awareness"), and Deirdre Mulligan ("It's Not Privacy and It's Not Fair").

Bio: Cynthia Dwork is a Distinguished Scientist at Microsoft Research and is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. She is a member of the National Academy of Sciences and the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences.

September 29

Warren Center Speaker
Susan Athey
Graduate School of Business, Stanford University
"Talk Title TBA"

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Abstract: text forthcoming.

Bio: text forthcoming.

October 14

CIS Distinguished Lecture Series
Laura Haas
IBM Fellow and Director, Technology and Operations IBM Research Accelerated Discovery Lab
"Accelerating Data Discovery for Better Health"

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Abstract: The volumes of healthcare data are sky-rocketing, and new sources and types of patient, biology, medical and contextual information are proliferating; we can now get more data on patients and disease than ever before. But that data is only valuable for the insight that can be gained from it – insights that let us better address both medical and business challenges, for example, improving treatments, understanding the basic science of disease, or reducing hospital re-admissions. There is a real opportunity to harness this data and dramatically change the practice of medicine, but to do so, we must do more than capture information.  We must correlate and align information across sources, extract meaning from it, and leverage that meaning to create value.  This talk will describe some of the challenges of capturing, integrating, and analyzing healthcare information and some of the progress that has been made in terms of runtimes and tools to support these tasks, as well as some ongoing research in this space. We will highlight some successful applications of these technologies, and close with a proposal to advance the state of the art in these technologies and in their application.

Bio: Laura Haas is an IBM Fellow and Director for Technology and Operations of IBM Research’s Accelerated Discovery Lab, which is creating a plug-and-play environment to facilitate deriving insight from data. The environment will meet dual goals: (1) to enable research in and improvements to the tools and systems that facilitate discovery, and (2) to enable the business person or domain expert who uses the environment to focus on their investigations, alleviating the systems and data challenges to speed discovery. Dr. Haas was the director of computer science at IBM Almaden Research Center from 2005-2011, and had worldwide responsibility for IBM Research’s exploratory science program from 2009 through 2013. Previously, she was responsible for Information Integration Solutions (IIS) architecture in IBM's Software Group after leading the IIS development team through its first two years. She joined the development team in 2001 as manager of DB2 UDB Query Compiler development. Before that, Dr. Haas was a research staff member and manager at the Almaden lab for nearly twenty years. In IBM Research, she worked on and managed a number of exploratory projects in distributed database systems.

Dr. Haas is best known for her work on the Starburst query processor (from which DB2 UDB was developed); on Garlic, a system which allowed federation of heterogeneous data sources; and on Clio, the first semi-automatic tool for heterogeneous schema mapping. Garlic technology, married with DB2 UDB query processing, is the basis for the IBM InfoSphere Federation Server, while Clio capabilities are a core differentiator in IBM’s InfoSphere Data Architect.

Dr. Haas is an active member of the database community. She served as Vice President of the VLDB Endowment Board of Trustees from 2004-2009 and was vice chair of ACM SIGMOD from 1989-1997. Dr. Haas has received several IBM awards for Outstanding Technical Achievement and Outstanding Innovation, and an IBM Corporate Award for her work on federated database technology. In 2010 she was recognized with the Anita Borg Institute Technical Leadership Award. She is a member of the National Academy of Engineering and the IBM Academy of Technology, an ACM Fellow, and Vice Chair of the board of the Computing Research Association. Dr. Haas received her PhD from the University of Texas at Austin, and her bachelor degree from Harvard University.

October 21

CIS Distinguished Lecture Series
Emin Gun Sirer
Computer Science Department, Cornell University
"Bitcoin: How to Get Rich or Die Tryin'"

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Abstract: Cryptocurrencies have recently emerged as a new and popular medium of exchange. In this talk, I will describe the technological underpinnings behind these new currencies ,and discuss their strengths and weaknesses from a distributed systems perspective. I will specifically focus on a novel strategy I co-discovered, with Ittay Eyal, that enables a miner to make more money than his fair share. I will also touch upon exchange failures that plague the cryptocurrency space, with an eye towards identifying future research directions.

Bio: Gun's research spans operating systems, networking and distributed systems. His current projects involve a novel secure operating system and system infrastructure for high-performance cloud computing applications. He likes building things, especially systems that have some principled reason for why they should work. 

October 28

CIS Distinguished Lecture Series
Time Kelly
Department of Computer Science, University of York
"Mechanising Informal Arguments - Some Challenges and Possibilities"

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Abstract: In many safety-critical industries developers and operators are required to construct and present well reasoned arguments that their systems achieve acceptable levels of safety. These arguments (together with supporting evidence) are typically referred to as a “safety case” or an “assurance case". Safety arguments historically have been communicated through narrative text, leading often to problems of comprehension and communication. Over the last twenty years there has been increasing interest in using structured argumentation notations such as GSN (Goal Structuring Notation) to communicate the structure of the argument. Whilst such arguments are structured, they remain informal. There is increasing interest in exploring how these informal arguments may be modeled in formal logic, potentially opening up benefits of forms of analysis not possible with informally recorded argument. This talk discusses a number of the considerations that need to be made in balancing the role of informal and formal logic in modeling assurance case arguments. The talk will also present some of our recent work on model-based assurance case that seeks to bring the benefits of model-driven engineering, such as automation,transformation and validation, to what is currently a lengthy and informal process.In this work approach, the assurance case itself is treated as a structured model, with the ultimate aim that all entities in the assurance argument become linked explicitly to the models that represent them.

November 6

CIS Distinguished Lecture Series
Mingyao Li
Department of Biostatistics and Epidemiology, University of Pennsylvania
"Statistical and Computational Modeling of RNA Sequencing Data"

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Abstract: RNA sequencing (RNA-seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. It has rapidly replaced microarrays as the major platform for transcriptomics studies. Analysis of RNA-seq data, however, is challenging because various biases present in RNA-Seq data can complicate the analysis, and if not appropriately corrected, will affect gene expression estimation and downstream modeling. In this talk, I will present several statistical/computational issues related to the analysis of RNA-seq data. I will first present PennSeq, a method that we recently developed for isoform-specific gene expression estimation. I will then discuss methods for detecting differential gene expression and differential alternative splicing. I will show simulation results as well as examples from real RNA-seq studies.

November 11

Grace Hopper Distinguished Lecture Series
Jennifer Rexford
Department of Computer Science, Princeton University
"Putting the 'Inter' in 'Internet'"
Learn more about this Grace Hopper Lecture

November 18

CIS Distinguished Lecture Series
Philip Resnik

Linguistics Department, University of Maryland, College Park
"Modeling Agendas and Framing in Political Debates and Other Conversations"

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Abstract: Computational social science has been emerging over the last several years as a hotbed of interesting work, taking advantage of, to quote Lazer et al. (Science, v.323), "digital traces that can be compiled into comprehensive pictures of both individual and group behavior, with the potential to transform our understanding of our lives, organizations, and societies." Within that larger setting, I'm interested in how language is used to influence people, with an emphasis on computational modeling of agendas (who is most effectively directing attention, and toward what topics?), framing or "spin" (what underlying perspective does this language seek to encourage?), and sentiment (how does someone feel, as evidenced in the language they use?) These questions are particularly salient in political discourse. In this talk, I'll present recent work looking at political debates and other conversations using Bayesian models to capture relevant aspects of the conversational dynamics, as well as new methods for collecting people's reactions to speeches, debates, and other public conversations on a large scale.This talk includes work done in collaboration with Jordan Boyd-Graber, Viet-An Nguyen, Deborah Cai, Amber Boydstun, Rebecca Glazier, Matthew Pietryka, Tim Jurka, and Kris Miler.

Bio: Philip Resnik is Professor of Linguistics at the University of Maryland, holding a joint appointment at UMD's Institute for Advanced Computer Studies. He received his Ph.D. in Computer and Information Science at the University of Pennsylvania (1993), and has worked in industry R&D at Bolt Beranek and Newman, IBM T.J. Watson Research Center, and Sun Microsystems Laboratories. His research emphasizes combining linguistic knowledge and statistical methods in computational linguistics, with a focus on multilingual applications and computational social science. He co-edited The Balancing Act: Combining Symbolic and Statistical Approaches to Language (MIT Press, 1996, with Judith Klavans), and has served on the editorial boards of Computational Linguistics, Cognition, Computers and the Humanities, and Linguistics in Language Technology. As extracurricular activities, he was a technical founder of CodeRyte Inc., a provider of language technology solutions in healthcare (acquired in 2012 by 3M), he has served as lead scientist for Converseon, a leading social media consultancy, and he is currently commercializing React Labs, a mobile platform for real-time polling and audience engagement.

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December 2

Panos Ipeirotis
Stern School of Business, New York University
"Adventures In Crowdsourcing"

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Abstract: Crowdsourcing is becoming increasingly popular in many fields. In thistalk, I will describe a set of systems that we built over the last few years, which combine human and machine intelligence, to create systems that are better than using humans or computers alone. I will cover a diverse set of topics surrounding the creation of such systems, including worker quality control, fair payment schemes, vulnerability detection for machine learning systems, and how to use online advertising systems for targeting knowledgeable users. Time permitting, I will conclude with an illustration of how Mechanical Turk workers and mice are not that different after all.

Bio: Panos Ipeirotis is an Associate Professor and George A. Kellner Faculty Fellow at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University. He received his Ph.D. degree in Computer Science from Columbia University in 2004. His recent research interests focus on crowdsourcing. He has received six "Best Paper" awards (IEEE ICDE 2005, ACM SIGMOD 2006, WWW 2011, ICIS 2012, HCOMP 2014, Management Science 2011-2013), three "Best Paper Runner Up" awards (JCDL 2002, ACM KDD 2008, INFORMS 2014 Data Mining contest), and is also a recipient of a CAREER award from the National Science Foundation and of several other grants.

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December 11

Andrew DeOrio
University of Michigan
"A Lesson on Data Abstraction"

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Abstract: Data abstraction helps computer scientists model complex phenomena and makes programs easier to maintain and modify.  This interactive lesson will focus on computer science concepts that apply to many different programming languages, although examples will be in C++. The material is based on my lectures from a 200-level programming and introductory data structures class.  I will conclude with a brief overview of my research, which aims to ensure the correctness of digital hardware designs.

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