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.

Therefore the seeker after the truth is not one who studies the writings of the ancients and, following his natural disposition, puts his trust on them, but rather the one who suspects his faith in them and questions what he gathers from them, the one who submits to argument and demonstration, and not to the sayings of a human being whose nature is fraught with all kinds of imperfection and deficiency.
Thus the duty of man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads, and, applying his mind to the core and margins of its content, attack it from every side.
He should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency.
Ibn al Haytham 965-1040

Recent News

2017 Machine Learning Summer School

I am part of organization team for MLSS2017. The MLSS is a renowned venue for graduate students, researchers, and professionals. It offers an opportunity to learn about fundamental and advanced aspects of machine learning, data analysis and inference, from intellectual leaders of the field.

Applied ML Days @ EPFL

I will be attending ML Days conference at EPFL in Lausanne, Switzerland. I will also present a poster on our latest project on Information Reliability and Source Trustworthiness.

Distilling Information Reliability and Source Trustworthiness from Digital Traces

Our paper on Information Reliability and Source Trustworthiness has been accepted at WWW 2017, preprint available on Arxiv. Code and data will be published soon.