Just as we can better understand a complex, historical event through multiple points of view, the relationships between signals at nodes of a network can be used to create a clearer picture of a complicated network.
Alfred Hero will explain how we can infer these intricate, hidden properties of a network in his upcoming Distinguished University Professor lecture, “Locating the Nodes: From Sensor Arrays to Genomic Networks.”
This talk will show how the node localization problem arises in applications ranging from geo-locating nodes in the Internet of Things to locating nodes in high dimensional networks such as gene interactions.
The cross-disciplinary lecture will take place at 4 p.m. Tuesday in Rackham Amphitheatre. The lecture and following reception are free and open to the public.
A Distinguished University Professorship is the highest professorial honor bestowed on U-M faculty. Hero was named the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science in 2016.
Hero named his professorship after John Holland, the first individual to receive a Ph.D. in computer science at U-M and a leader in genetic algorithms.
“Holland was a deep thinker who did not adhere to scientific dogma and who circulated freely across disciplinary boundaries,” Hero says.
In addition to being the R. Jamison and Betty Williams Professor of Engineering and holding appointments in the Department of Biomedical Engineering and the Department of Statistics, Hero also is co-director of the Michigan Institute for Data Science.
MIDAS was created in 2015 and aims to advance the multidisciplinary field of data science with greater collaboration, resources and funding. It is a key component of U-M’s Data Science Initiative, which includes Consulting for Statistics, Computing and Analytics Research, and Advanced Research Computing — Technology Services.
As well as data science, Hero’s recent research explores bioinformatics and personalized health, statistical signal processing and imaging, correlation mining, statistical machine learning and pattern recognition, wearable wireless sensors, sensor networks, and sensor management.
