Spring 2020

Inference and Data Science on Networks

March 30 – June 6, 2020

Synopsis

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 on-line 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.

Organizers

Eric Auerbach, Randall Berry, Aravindan Vijayaraghavan, Samir Khuller, Varun Gupta.

Events

  • May 5: Kickoff Workshop: Inference and Data Science on Networks
  • May 13: Dean’s Lecture: Jon Kleinberg, Fairness and Bias in Algorithmic Decision Making
    Public debates about classification by algorithms has created tension around what it means to be fair to different groups. Cornell University’s Jon Kleinberg will consider key fairness conditions at the heart of these debates, and discuss recent work on the interactions between these conditions. He will 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. [Registration Required]
  • May 14: Workshop: Estimation of Network Processes and Information Diffusion.
    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.
  • June 29: Workshop: Computational vs Statistical Tradeoffs in Network Inference.
    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.

See the institute event listing for a full listing of upcoming events

Graduate Courses

The following graduate courses are coordinated to facilitate attendance by participants of the special quarter.

These course will be offered remotely. Students interested in enrolling in these courses should can apply here.

Special Quarter Visitors

Please see the participation page for information about participating in this special quarter.

Calendar

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