Explore our events, and discover upcoming workshops and symposia you might be interested in attending. Be sure to add our Google Calendar to stay up-to-date on IDEAL events.

March 25, 2020
April 13, 2020
  • Postponed: IDEAL Workshop: Computational vs Statistical Tradeoffs in Network Inference

    April 13, 2020 | 9:00 am - 1:00 pm
    Mudd 3514

    Researchers in diverse communities like economics, social science, and computer science have been studying network models for a long time as a way of understanding the impact of interconnections between various entities on their actions. The recent trend in availability of fine grained data makes it possible to use these network models to infer network structure based on the observations of actions of these entities. However, the inherent high-dimensionality of the hypothesis class makes such inference challenging without substantial unrealistic assumptions on the network structure. These challenges lead to several new and exciting statistical and algorithmic questions at the interface of network science, econometrics and machine learning. The workshop will cover some of the advances in the last decade on understanding trade-offs between statistical and computational efficiency for many algorithmic learning problems on networks. The speakers are Andrea Montanari (Stanford), Liza Levina (Michigan), Edoardo Airoldi (Temple). Ankur Moitra (MIT).

    More details:

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May 5, 2020
  • Kickoff Workshop: Inference and Data Science on Networks

    May 5, 2020 | 11:00 am - 3:00 pm
    Register for Zoom; or livestream from Panopto

    Over the past decade or so, many diverse communities have become increasingly interested in networks as a way of understanding the role of interconnections between various entities. For example, economists, social scientists and policy researchers have studied how the social and economic ties between agents drive important economic phenomena such as income inequality, human capital accumulation, the dynamics of the business cycle and research productivity. Likewise, there has been interest from computer scientists and engineers in understanding the network structures that emerge in the Internet, the World Wide Web and online social networks. However, the empirical literature has been limited by the fact that networks are inherently high-dimensional objects, which makes it intractable to directly assess the importance of network structure without a substantial amount of unrealistic functional form assumptions. These present several new and exciting statistical and algorithmic challenges at the interface of network science, econometrics and machine learning.

    In this workshop several of the coorganizers of the special quarter will speak about their vision for the theme. Talks will be by Eric Auerbach, Randall Berry, Ozan Candogan, and Aravindan Vijayaraghavan.

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  • Open Problems: Inference and Data Science on Networks

    May 5, 2020 | 4:00 pm - 5:00 pm

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May 13, 2020
  • Dean's Lecture: Jon Kleinberg on "Fairness and Bias in Algorithmic Decision-Making"

    May 13, 2020 | 12:00 pm - 1:00 pm

    Recent discussion in the public sphere about classification by algorithms has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent work on the interactions between these conditions. We also explore how the complexity of a classification rule interacts with its fairness properties, showing how natural ways of approximating a classifier via a simpler rule can act in conflict with fairness goals.

    The talk will be based on joint work with Jens Ludwig, Sendhil Mullainathan, Manish Raghavan, and Cass Sunstein.

    Bio sketch: Jon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on the interaction of algorithms and networks, and the roles they play in large-scale social and information systems. He is a member of the National Academy of Sciences and the National Academy of Engineering, and the recipient of MacArthur, Packard, Simons, Sloan, and Vannevar Bush research fellowships, as well awards including the Harvey Prize, the Nevanlinna Prize, and the ACM Prize in Computing.

    Please go here to register for this talk.

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May 14, 2020
  • Workshop: Estimation of Network Processes and Information Diffusion.

    May 14, 2020 | 11:00 am - 5:00 pm

    Many important dynamic processes are determined by an underlying network structure. Examples include the spread of epidemics, the dynamics of public opinions, the diffusion of information about social programs, and biological processes such as neural spike trains. Data about these processes is becoming increasingly available which has lead to a number of different research communities to tackle related questions independently. What is the source of a rumor? Will a given disease spread widely? Who is the key player? This workshop will cover some new tools being developed to address such questions.  

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May 27, 2020
  • IDEAL Speaks: Dongning Guo on “The Nakamoto Consensus and Its Probabilistic Security Guarantee”

    May 27, 2020 | 12:00 pm - 1:00 pm
    Northwestern CS Colloquium Livestream

    The Nakamoto consensus is at the core of many peer-to-peer blockchain systems, including the well-known bitcoin network.  In this lecture, we describe a continuous-time probabilistic model for mining and blockchains.  We impose no restrictions on adversarial miners except for an upper bound on their aggregate mining rate.  The only assumption about the mining network is that block propagation delays are bounded.  We develop the first published rigorous proof of the Nakamoto consensus’ security guarantee in continuous time.  In particular, but for a small probability exponential in k, a transaction that is k-deep in some credible blockchain remains permanent in all future credible blockchains.


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June 13, 2020
  • IDEAL Quarter Concludes (Spring 2020: Inference and Data Science on Networks)

    June 13, 2020
    2233 Tech Dr, Evanston, IL 60208, USA

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June 29, 2020
  • Workshop: Computational vs Statistical Tradeoffs in Network Inference

    June 29, 2020 | 11:00 am - 5:00 pm

    Network models have been used as a tool to understand the role of interconnections between entities, by diverse communities such as sociology, biology, meteorology, economics, and computer science, to name a few. Moreover emerging technological developments allow collecting data on increasingly larger networks. This leads to both computational and statistical challenges when inferring or learning the structure of such networks. This workshop will cover some of the advances in the last decade on understanding trade-offs between statistical and computational efficiency for many inference problems on large networks.  

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August 14, 2020
  • IDEAL / Theory Seminar: Algorithms for Submodular Maximization / Alina Ene, Boston University

    August 14, 2020 | 11:00 am - 12:00 pm

    Speaker: Alina Ene, Boston University. 
    Title: Algorithms forSubmodular Maximization
    Time: August 14th, 2020 at11AM CT
    Abstract: Submodularmaximization problems arise in many domains in Computer Science, Mathematics,and Economics. Due to their unusual mix of generality and tractability, theyhave played a central role in discrete optimization and they have witnessedwide adoption in applications in data mining, machine learning, and computervision.

    In this talk, we explore two fundamental algorithmic challenges in submodularmaximization: understanding the approximability of central problems anddesigning very efficient and parallel algorithms. We discuss how submodularfunctions and their continuous counterparts provide an interface betweendiscrete and continuous optimization, and we present algorithms that build onboth the combinatorial structure and powerful primitives from continuousoptimization.

    We illustrate the synergy between continuous and discrete ideas using tworesearch vignettes. In the first vignette, we obtain better approximationalgorithms for constrained submodular maximization that build on differentialequations and continuous optimization, and overcome a long-standing barriertowards settling the approximability of this class of problems. In the secondvignette, we obtain algorithms whose parallel running times are exponentiallyfaster than previous algorithms.    

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