Short Biosketch

Alfred O. Hero III received the B.S. (summa cum laude) from Boston University (1980) and the Ph.D from Princeton University (1984), both in Electrical Engineering. Since 1984 he has been with the University of Michigan, Ann Arbor, where he is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering. His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. Alfred Hero serves as Chair of the Committee on Applied and Theoretical Statistics (CATS) of the US National Academies of Science. He is a Section Editor of the SIAM Journal on Mathematics of Data Science and a Senior Editor of the IEEE Journal on Selected Topics in Signal Processing . He is on the editorial board of the Harvard Data Science Review
(HDSR) . He serves as moderator for the Electrical Engineering and Systems Science category of the arXiv .

Alfred Hero is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the Society for Industrial and Applied Mathematics (SIAM). Alfred Hero was awarded the University of Michigan Distinguished Faculty Achievement Award (2011) and the Stephen S. Attwood Excellence in Engineering Award (2017). 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 from the IEEE Signal Processing Society and he received the 2020 Fourier Award from the IEEE.

Alfred Hero was founding Co-Director of the University’s Michigan Institute for Data Science (MIDAS) (2015-2018). From 2008 to 2013 he held the Digiteo Chaire d’Excellence, sponsored by Digiteo Research Park in Paris, located at the Ecole Superieure d’Electricite, Gif-sur-Yvette, France. He has held other visiting positions at LIDS Massachusetts Institute of Technology (2006), Boston University (2006), I3S University of Nice, Sophia-Antipolis, France (2001), Ecole Normale Superieure de Lyon (1999), Ecole Nationale Superieure des Telecommunications, Paris (1999), Lucent Bell Laboratories (1999), Scientific Research Labs of the Ford Motor Company, Dearborn, Michigan (1993), Ecole Nationale Superieure des Techniques Avancees (ENSTA), Ecole Superieure d’Electricite, Paris (1990), and M.I.T. Lincoln Laboratory (1987 – 1989).

Alfred Hero was President of the IEEE Signal Processing Society (2006-2007). He was a member of the IEEE TAB Society Review Committee (2009), the IEEE Awards Committee (2010-2011), and served on the Board of Directors of the IEEE (2009-2011) as Director of Division IX (Signals and Applications). He served on the IEEE TAB Nominations and Appointments Committee (2012-2014). Alfred Hero is currently a member of the Big Data Special Interest Group (SIG) of the IEEE Signal Processing Society. Since 2011 to 2020 he was a member of the Committee on Applied and Theoretical Statistics (CATS) of the US National Academies of Science, Engineering and Medicine, and Chaired CATS from 2018 to 2020. He was co-General Chair of the IEEE International Symposium on Information Theory (ISIT).

Alfred Hero’s 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.