My name is Behzad Tabibian, I am a PhD student at Max Planck Institute, Emperical inference department with Prof. Dr. Bernhard Schölkopf and Dr. Manuel Gomez Rodriguez. I studied Computer Science at Edinburgh University and graduated in June of 2012 and completed my masters at university of Pittsburgh. My main research interests are Machine Learning, Statistics and Inference. I primarily apply Machine Learning on (Social) Networks problems.
Previously I was interested in robotics and software engineering. In my free time I enjoy talking about books, particularly history, political philosophy and philosophy of science.
Recent News
Enhancing human learning via spaced repetition optimization @ PNAS
Our paper, "Enhancing human learning via spaced repetition optimization", has been accepted at Proceedings of National Academy of Sciences of the United States of America.
Design and Analysis of the NIPS 2016 Review Process Accepted at JMLR
In this paper, we analyze several aspects of the data collected during the review process, including an experiment investigating the efficacy of collecting ordinal rankings from reviewers (vs. usual scores aka cardinal rankings). Our goal is to check the soundness of the review process we implemented and, in going so, provide insights that may be useful in the design of the review process of subsequent conferences. We introduce a number of metrics that could be used for monitoring improvements when new ideas are introduced.
Research Internship @ FB
I will be spending the summar at Facebook in Menlo Park with Misinformation team addressing problems related to spread of Misinformation in social networks.
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation @ WSDM 2018
Our paper, Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation, has been accepted (for a long talk) at 2018 ACM Web Search and Data Mining conference, Los Angles CA, USA. Project webpage and preprint of the paper are now available.
Recent Posts
Hacking into Evolutionary Dynamics
This notebook contains some implementations of algorithms and ideas discussed in "Evolutionary Dynamics" by M. Novak. For better undrestanding of the code and equtions please consult first five chapters of the book.
Tutorial on Latent Dirichlet Allocation
I gave a tutorial on Latent Dirichlet Allocation as part of PhD tutorials at our department. Here you can find the contents I covered in this talk.