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  • Mar25

    Recovering data from noise is a fundamental problem that arises in many applications such as compressed sensing, channel communication, pattern matching, and internet advertising. The problem is known to be extremely difficult to solve with no additional information about the datasets. However, efficient recovery may be possible if the datasets have some specific pattern or structure associated with them. In this talk, I will present my work on the algorithmic aspects of recovering certain structured datasets from noise.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Venkata Gandikota
    TRIPODS - Institute for Theoretical Foundations of Data Science | University of Massachusetts Amherst
  • Mar25

    Postponed due to UI COVID-19 response - 

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    (All day)
    Online
    UI Dept of Computer Science | UI Center for Advancement
  • Mar23

    Which locations and staff should we monitor in order to detect pathogen outbreaks in hospitals? How do we predict the peak intensity of the influenza incidence in an interpretable fashion? How do we infer the states of all nodes in a critical infrastructure network where failures have occurred? Leveraging domain-based information should make it possible to answer these questions. However, several new challenges arise such as (a) presence of more complex dynamics (b) data sparsity and (c) mismatch between data and the process.

    4:00pm to 5:00pm
    Zoom - See emails for details
    Bijaya Adhikari
    Virginia Tech
  • Mar11

    Given the increasing volume of sensitive data stored on systems that are connected to the internet, it is likely that cyber threats such as insider and ransomware will continue to be lucrative ammunitions for cybercriminals and cause billions of dollars of damage. While machine learning, more specifically, anomaly detection techniques can help us quickly detect and resolve such unexpected intrusions, critical applications such as health monitoring devices cannot directly leverage third-party anomaly detection services due to the sensitive nature of the data.

    4:00pm to 5:00pm
    218 MLH
    Shagufta Mehnaz
    Purdue University
  • Mar09

    Security and user privacy for complex networks and cyber-physical systems are often considered as afterthoughts. This leads to inadequate security evaluation early on the development cycle that fails to identify missing security and privacy guarantees in protocol designs. To make matters worse, unsafe practices and operational oversights stemming from unvetted simplification of complex protocol interactions further contribute to the deviation of deployments from designs.

    4:00pm to 5:00pm
    218 MLH
    Syed Rafiul Hussain
    Purdue University
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