Matej Čief

Research areas: machine learning, reinforcement learning, deep learning, natural language processing, recommender systems, user modelling

Position: PhD Student

Matej is a PhD student focused on recommender systems (off-policy learning and evaluation). In his research he designs estimators and data-gathering policies that can evaluate recommendation algorithms without the need of deploying the whole system online. Supervised by Michal Kompan (KInIT) and Branislav Kveton (Amazon’s lab in Berkeley).

He holds a Master’s degree in Intelligent Software Systems from the Slovak University of Technology. During his studies, he has worked on multiple research projects, including User Modeling and his master’s thesis: Use of Semi-supervised Learning for Fake News Detection. Matej has gained experience in applied machine learning research through his work as a data scientist, where he mainly focused on solving problems in the fields of natural language processing and time series forecasting.