Jakub Tětek

Jakub Tětek is a researcher in the field of theoretical computer science, with a focus on algorithms for big data. He completed his Master's degree at the University of Copenhagen in Denmark, where he was supported by the Bakala foundation. After finishing his Master's, he decided to continue his studies and pursue a Ph.D. at the same university. In his Ph.D., Jakub is working on developing more efficient algorithms for processing large amounts of data. He uses tools from probability theory to analyze and improve the performance of these algorithms. Through his research, he aims to make it possible to handle ever-increasing amounts of data in a more efficient and effective way. Recently, Jakub has been working on the topic of differential privacy. Differential privacy is a mathematical framework for analyzing and protecting the privacy of individuals during data analysis. It allows us to make use of data while ensuring that the private information of any specific individual is not revealed. Jakub is particularly interested in developing more efficient algorithms for big data analysis under the constraints of differential privacy. He aims to strike a balance between getting meaningful insights from large datasets while not violating the privacy of individuals. He is working on new techniques that make it possible to get very efficient algorithms for analyzing large datasets without violating people's privacy.