Stochastic models defined on networks introduce novel algorithmic challenges. This challenges arise from diverse application fields, such as molecular biology, computer networks and social networks. In this talk I will survey some recent progress in this area. In particular, I will discuss the problem of reconstructing the network from observations at some nodes and optimization problems defined on such networks.
Based on joint works with C. Daskalakis and S. Roch, with S. Roch and with C. Daskalakis, D. Karp, S. Riesenfeld and E. Verbin
Speaker Bio:
Elchanan Mossel is from Jerusalem where he earned his PhD in Mathematics at the Hebrew University under Prof. Yuval Peres. He was a post-doc with the Theory Group at Microsoft Research and a Miller fellow in Statistics and Computer Science at Berkeley. His research Interests include Applied Probability, Discrete Fourier analysis, Markov Chains, Markov Random Fields, Learning, Social Choice and Phylogeny.