Machine Learning for Dynamic Social Network Analysis
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 enhance the functioning of large online social and information systems. 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 course, 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 systems. Then, you will get introduced to several inference and control problems of practical importance and learn about state-of-the-art machine learning algorithms to solve these problems.
Social networks, information networks, machine learning, temporal point processes.
Python, scipy, cvxpy.
Basic probability, linear algebra.
Dr. Manuel Gomez Rodriguez
Tenure-track faculty at the Max Planck Institute for Software Systems, Germany
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 social and information online systems. He is particularly interested in the creation, acquisition and/or dissemination of reliable knowledge and information, which is ubiquitous 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 and WWW’17. Manuel holds a BS in Electrical Engineering from Carlos III University in Madrid (Spain), a MS and PhD in Electrical Engineering from Stanford University, and has received postdoctoral training at the Max Planck Institute for Intelligent Systems. You can find more about him at http://learning.mpi-sws.org
Fields of interests: Machine learning, social network analysis, temporal point processes, fair machine learning.