Christian Schaffner
I studied mathematics at ETH Zurich (Switzerland) in 2003 and obtained a PhD degree in computer science from Aarhus University (Denmark) in 2007. After being a postdoctoral scholar at CWI Amsterdam and faculty member at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, I now am full professor in Theoretical Computer Science and group leader of the Theory of Computer Science (TCS) group at the Informatics Institute at University of Amsterdam. I’m an active member of the TCS community in Amsterdam, and senior researcher at QuSoft, the Dutch research center for quantum software.
I carry out research in quantum cryptography, both on non-quantum cryptography that remains secure against quantum attackers (also known as post-quantum cryptography) and on the design of protocols that solve cryptographic problems involving quantum data and quantum communication.
Since 2014, I am teaching a yearly master course on Shannon Information Theory at University of Amsterdam.
Sessions
In his 1948 scientific article entitled "A mathematical theory of communication", Claude E. Shannon introduced the word “bit”. The article laid down the foundations for the field of information theory which in turn opened up the way to digital information processing.
In this overview talk, I will present in an accessible way three nuggets from Shannon information theory:
1. Shannon entropy, a mathematical quantification of uncertainty of a probability distribution.
2. Information Compression: Shannon entropy provides a fundamental lower bound on how much information from a source can be compressed so that it can later be recovered.
3. Error correction: when digital information is transmitted over a noisy channel, the methods of error-correction provide ways to protect this information from noise. Yet again, Shannon entropy provides the fundamental quantity of how much information can be transmitted over a noisy channel.
While the content of this talk is of mathematical nature, I will try my best to make it accessible to anybody with (very) basic knowledge of probabilities and programming.