D'Orazio, Vito

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/7028

Vito D'Orazio is an Assistant Professor of Political Science. His research interests "are at the intersection of conflict studies and computational methods." This includes:

  • The Causes of Militarized Conflict
  • Peacekeeping and Other Forms of Military Cooperation
  • Conflict Forecasting
  • Machine Learning Algorithms
  • Methods for the Content Analysis of News Reports
  • Social Measurement
  • Data Privacy
  • Software Development
Dr. D'Orazio is also a co-author of TwoRavens, a statistical analysis tool for data exploration, analysis, and meta-analysis

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Recent Submissions

Now showing 1 - 2 of 2
  • Item
    Towards Human-Guided Machine Learning
    (Association for Computing Machinery, 2019-03) Gil, Y.; Honaker, J.; Gupta, S.; Ma, Y.; D'Orazio, Vito; Garijo, D.; Gadewar, S.; Yang, Q.; Jahanshad, N.; 0000-0003-4249-0768 (D'Orazio, V); D'Orazio, Vito
    Automated Machine Learning (AutoML) systems are emerging that automatically search for possible solutions from a large space of possible kinds of models. Although fully automated machine learning is appropriate for many applications, users often have knowledge that supplements and constraints the available data and solutions. This paper proposes human-guided machine learning (HGML) as a hybrid approach where a user interacts with an AutoML system and tasks it to explore different problem settings that reflect the user's knowledge about the data available. We present: 1) a task analysis of HGML that shows the tasks that a user would want to carry out, 2) a characterization of two scientific publications, one in neuroscience and one in political science, in terms of how the authors would search for solutions using an AutoML system, 3) requirements for HGML based on those characterizations, and 4) an assessment of existing AutoML systems in terms of those requirements. © 2019 Copyright is held by the owner/author(s).
  • Item
    TwoRavens for Event Data
    (Institute of Electrical and Electronics Engineers Inc.) D'Orazio, Vito; Deng, Marcus; Shoemate, Michael; 0000-0003-4249-0768 (D'Orazio, V); D'Orazio, Vito; Deng, Marcus; Shoemate, Michael
    Event data contains information for descriptive, predictive and inferential statistical analysis of political and social actions. The varied formats and large size of event datasets present considerable difficulties for time-efficient analysis, cross-dataset replication and comparison, and discoverability. We address these difficulties with an event data platform, TwoRavens for Event Data, that provides an interface to construct generalized, declarative and reusable descriptions of data subsets and aggregations. The platform may be used to quickly construct and visualize datasets of research interest. Processed data may be curated and saved, and is easily discoverable. ©2018 IEEE.

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