Daniel Sussman

Dan Sussman is an Assistant Professor in the Department of Mathematics and Statistics and a Faculty Affiliate of the Center for Information and Systems Engineering at Boston University.

His research interests include statistics for network data; node embeddings; graph matching; causal inference under interference; and brain networks, molecular networks, social networks, knowledge graphs. A major focus is working with neuroscientists employing network techniques to understand how the networked structure of the brain relates to its function, developing new methods to assess fundamental conjectures in neuroscience, and helping the neuroscientists perform statistical analysis on super high resolution images of neurons to understand their basic properties. Dr. Sussman Sussman has begun to study the impact of computational constraints on statistical risk and efficiency. With the view that there are fundamental trade-offs between fast computation and low statistical risk, his research seeks to develop a framework to describe and quantify these trade-offs, allowing practitioners to make informed decisions about the trade-off and for method developers to push the frontiers of this trade-off.

Dr. Sussman received his Ph.D. in Applied Math and Statistics from Johns Hopkins University in 2014. He received his
B.A. in Mathematics from Cornell University in 2008.