Yuval Moskovitch (University of Michigan)
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Data-driven methods are increasingly being used in domains such as fraud and risk detection, where data-driven algorithmic decision making may affect human life.
The growing impact of data and data-driven systems on society makes it important that people be able to trust analytical results obtained from data-driven computations.
This can be done in two complementary ways: by providing result explanations so that the user understands the computation and the basis for the observed results; and by profiling and monitoring the data used in the computation, to make the results more reliable in the first place.
In the first part of the talk, I will present the use of provenance -- information regarding the data origin and computational process -- for providing explanations of computational results. In the second part of the talk, I will present a method for data profiling using labels, as an example of a data-focused technique to facilitate an analyst building a reliable decision-making pipeline.
Yuval is a postdoctoral researcher at the University of Michigan, hosted by Prof. H. V. Jagadish. Her research is centered around data management, advanced database applications, provenance, and process analysis. In her current research, she focuses on data management for fairness and responsible data science. Yuval obtained her Ph.D. in Computer Science from Tel Aviv University, under the supervision of Prof. Daniel Deutch. She completed a BSc in Software Engineering and MSc in Computer Science at Ben Gurion University. Yuval is the recipient of several awards including the Shulamit Aloni Scholarship for Advancing Women in Science of the Israeli Ministry of Science and Technology and the Data Science Fellowship for outstanding postdocs of the planning and budgeting committee of the council for higher education (VATAT).