Join us for at the Seminar “Machine Learning for Dynamic Social Network Analysis” with Manuel Gómez Rodríguez of Max Planck Institute for Software Systems:
- Title: Machine Learning for Dynamic Social Network Analysis.
- Seminar Dates: 2017, May 12, 15 and 16; 11:00 – 13:30.
- Location: 4.2.E02 Room; Torres Quevedo Building; Leganés Campus; Universidad Carlos III de Madrid.
In recent years, there has been an increasing effort on developing realistic representations and models as well as learning, inference and control algorithms to understand, predict, and control dynamic processes over social and information networks. This has been in part due to the increasing availability and granularity of large-scale social activity data, which allows for data-driven approaches with unprecedented accuracy.
In this seminar, you will first learn how to utilize the theory of temporal point processes to create realistic representations and models for a wide variety of dynamic processes in social and information networks. Then, you will get introduced to several inference and control problems of practical importance in the context of dynamic processes over networks, and learn about state-of-the-art machine learning algorithms to solve these problems.
Manuel Gómez Rodríguez is a tenure-track research group leader at Max Planck Institute for Software Systems. Manuel develops machine learning and large-scale data mining methods for the analysis and modeling 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/.