Balázs Hidasi

I’m a research scientist working on the field of machine learning. I have 15+ years of experience in designing algorithms.

My research topics include utilizing deep learning for session-based (sequential) recommendations, evaluation of recommender systems, context-aware tensor factorization on implicit feedback data, and more recently counterfactual learning. I was one of the pioneers of deep learning technology for recommender systems and contributed to evangelizing it in the research community.

For the majority of my career, I have been working on algorithms for recommender systems in the industry. Therefore I have vast experience in a wide variety of recommender systems related topics, and my algorithms are designed with efficient resource utilization, scalability and fast training/inference in mind. Moreover, I have been frequently involved in other aspects of the business - beside research and coding - from strategy to product design to leading innovation, allowing me to see the big picture. I have been leading small machine learning or data science focused teams since 2015.

I received my summa cum laude PhD / MSc / BSc from the Budapest University of Technology.