Applied Statistics Workshop (Gov 3009)

The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. There is a free lunch provided. Manuel Gomez Rodriguez Title: COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Abstract: Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. In this talk, we introduce a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives. ———— Bio: Manuel Gomez Rodriguez is a tenure-track faculty at Max Planck Institute for Software Systems. Manuel develops machine learning and large-scale data mining methods for the analysis, modeling and control of large real-world networks and processes that take place over them. He is particularly interested in problems arising in the Web and social media and has received several recognitions for his research, including an Outstanding Paper Award at NIPS'13 and a Best Research Paper Honorable Mention at KDD'10. Manuel holds a PhD in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Carlos III University in Madrid (Spain). You can find more about him at http://www.mpi-sws.org/~manuelgr/.