Miro Mannino is a Research Assistant for the Prof. Azza Abouzied. His research work focuses on designing intuitive data querying tools.
Miro's major contributions currently are:
Qetch: a tool that allows users to freely sketch patterns on a scale-less canvas to query time series data without specifying query length or amplitude. Qetch won the Best Paper Award during the SIGCHI'18 conference.
sPaQL Tools: a Stochastic Package Query Interface for Scalable Constrained Optimization. This project won Best Demo Award during the VLDB’20 conference.
EpiPolicy: a population-based epidemic simulator and policy aid that allows users to customize its compartmental model to capture different epidemic scenarios for epidemics like COVID-19.
Synner: a tool that helps users generate real-looking synthetic data by visually and declaratively specifying the properties of the dataset such as each field’s statistical distribution, its domain, and its relationship to other fields.
Texture: a framework for data extraction over print documents that allows end-users to construct data extraction rules over an inferred document structure.
In his spare time Miro is actively involved on small personal projects, and participated as part-time teaching assistant for the Database System course.