Abstract: Modeling multi-way data is important for applications involving multi-indexed observables, e.g., hyperpsectral data that is indexed over spatial, frequency, and temporal dimensions. The sparse matrix normal model is a multivariate Gaussian representation that expresses the covariance matrix as a Kronecker product of sparse lower dimensional covariances. This model is equivalent to assuming the conditional dependencies of the covariates can be represented as a direct-product graph with few edges. We will present an alternative framework based on Cartesian product graph representation and Kronecker sums that leads to ultra-sparse models for multi-way data.

Bio: Alfred Hero is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan.

At the University of Michigan his primary appointment is in the Department of Electrical Engineering and Computer Science (EECS) and he has secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), and the Michigan Institute for Data Science (MIDAS).

Alfred Hero is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a member of Tau Beta Pi, the American Statistical Association (ASA) , the Society for Industrial and Applied Mathematics (SIAM), and the US National Commission (Commission C) of the International Union of Radio Science (URSI). Alfred Hero was awarded the University of Michigan Distinguished Faculty Achievement Award (2011), the Stephen S. Attwood Excellence in Engineering Award (2017), and the H. Scott Fogler Award for Professional Leadership and Service (2018). He has been plenary and keynote speaker at several workshops and conferences. He has received several best paper awards including: an IEEE Signal Processing Society Best Paper Award (1998), a Best Original Paper Award from the Journal of Flow Cytometry (2008), a Best Magazine Paper Award from the IEEE Signal Processing Society (2010), a SPIE Best Student Paper Award (2011), an IEEE ICASSP Best Student Paper Award (2011), an AISTATS Notable Paper Award (2013), and an IEEE ICIP Best Paper Award (2013). He received an IEEE Signal Processing Society Meritorious Service Award (1998), an IEEE Third Millenium Medal (2000), an IEEE Signal Processing Society Distinguished Lecturership (2002), and an IEEE Signal Processing Society Technical Achievement Award (2014). He received the 2015 Society Award, which is the highest career award bestowed by the IEEE Signal Processing Society, and the 2020 Fourier Award, which is the IEEE Technical Field Award for Signal Processing.

His recent research interests are in high dimensional spatio-temporal data, multi-modal data integration, statistical signal processing, and machine learning. Of particular interest are applications to social networks, network security and forensics, computer vision, and personalized health.