Daniel Sussman

Dr. Daniel Sussman is an Assistant Professor at Boston University, affiliated with the Probability and Statistics research group. He also serves as the Associate Director of the Program in Statistics. His work focuses on statistical methodologies for network analysis, including graph matching, latent variable models, and causal inference under network interference. Dr. Sussman develops computational tools for analyzing complex networks, such as the iGraphMatch R package for graph matching analysis. His research bridges theoretical foundations with practical applications in neuroscience, epidemiology, and computational social science.

Key research interests include network dynamics, stochastic processes on graphs, and statistical estimation in noisy network data. He explores geometric and algebraic properties of random graphs, as well as scalable algorithms for large network datasets. His contributions span theoretical results (e.g., asymptotic consistency of embeddings) to applied methods (e.g., unbiased estimation in exposure models).

Dr. Sussman’s publications highlight advancements in graph matching techniques, including matched filter approaches and multi-scale heat diffusion methods. His work emphasizes robustness to noise and scalability for real-world networks. While no explicit awards are listed, his contributions reflect significant impact in statistical network analysis.

His professional role as Associate Director involves overseeing statistical training programs and curricula. Though no advising details are provided, his research group likely supports graduate students focused on network science and computational statistics.