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Michael Behrisch is a Postdoctoral Fellow at the
Harvard School of Engineering and Applied Sciences


Research Focus: In my research I focus on novel visual interactive techniques, algorithmic approaches and integrated visual analytics systems to support users in navigating and exploring large amounts of relational data. One central research objective is, amongst others, to automatically assess the interestingness of visualizations and show only potentially important views from a large exploration space to reduce the users’ cognitive overload.

With my studies we want contribute to the matrix visualization research by enlarging the scope to data sets that have on top of its large and dense characteristics, also multivariate and/or dynamic aspects. In case of multivariate data one matrix can be constructed for every data type. In the case of dynamic datasets one matrix can be retrieved for every time instance. In both cases, large amounts of matrices lead to both processing- and visualization challenges.

How can we support the exploration process for relational data with the help of matrix-based representations? How can enhance the expressiveness and effectiveness of matrix visualizations? Which interaction concepts help the user in exploring relational data in matrix visualizations?

How can we describe and quantify the interestingness of matrices wrt. its contained patterns? How can we measure the occurrence of specific visual features (i.e., patterns) contained in matrices? How can we derive interestingness scores depending on pattern descriptions for matrix-based representations?

How can we help the user in navigating and exploring large matrix spaces? How can we compare matrices, e.g., to allow for ’more-like-this’ queries? How can we support the user in defining queries for matrix patterns? How can we train computer systems to reflect an analyst’s notion of interestingness?