Alfred Hero is the R. Jamison and Betty Williams Professor of Engineering and co-director of the Michigan Institute for Data Science (MIDAS) with appointments in Departments of Electrical Engineering and Computer Science, Biomedical Engineering and Statistics. He has held visiting positions at MIT, Boston University, Lucent Bell Laboratories (Murray Hill), Ford Motor Company in addition to the University of Nice, the École Normale Supérieure de Lyon, and Telecom-ParisTech in France. He received the B.S. (summa cum laude) from Boston University (1980) and the Ph.D. from Princeton University (1984), both in Electrical Engineering. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and has received numerous distinctions and awards for his research and service to the profession. These include 8 Best Paper Awards, the University of Michigan Distinguished Faculty Achievement Award (2011), the IEEE Signal Processing Society Meritorious Service Award (1998), the IEEE Third Millenium Medal (2000), and the IEEE Signal Processing Society Technical Achievement Award (2014). Dr. Hero was President of the IEEE Signal Processing Society (2006-2008) and was on the Board of Directors of the IEEE (2009-2011) where he served as Director of Division IX (Signals and Applications). He served on the IEEE TAB Nominations and Appointments Committee (2012-2014). Dr. Hero is also a member of the Big Data Special Interest Group (SIG) of the IEEE Signal Processing Society. Since 2011, he has been a member of the Committee on Applied and Theoretical Statistics (CATS) of the US National Academies of Science.
Over his 30 year academic career at the University of Michigan, Dr. Hero has supervised over 50 PhD students and 25 post-doctoral students in electrical engineering and computer science, biomedical engineering, mathematics, statistics, physics, and bioinformatics. His recent research interests are in the analysis of high dimensional spatio-temporal data, statistical signal processing, and machine learning. Of particular interest are applications to networks, including social networks, multi-modal sensing and tracking, database indexing and retrieval, imaging, biomedical signal processing, and biomolecular signal processing.