PhD Tome Eftimov
no.: 50854

Statistical data analysis, natural language processing, machine learning, metaheuristics, information theory

Tome Eftimov is a senior researcher at the Computer Systems Department at the Jožef Stefan Institute. He is a visiting assistant professor at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje. He was a postdoctoral research fellow at Stanford University, USA, where he investigated biomedical relations outcomes by using AI methods. In addition, he was a research associate at the University of California, San Francisco, investigating AI methods for information extraction from electronic health records. He obtained his PhD in Information and Communication Technologies (2018). His research interests include statistical data analysis, metaheuristics, natural language processing, representation learning, meta-learning, and machine learning. He has presented his work as 81 conference articles, 50 journal articles, and one Springer book published 2022. He was selected in Stanford University's top 2% of influential scientists worldwide in all disciplines for AI contributions for 2022. The work related to Deep Statistical Comparison was presented as a tutorial (i.e. IJCCI 2018, IEEE SSCI 2019, GECCO 2020, 2021, 2022, 2024, PPSN 2020, 2022, IEEE CEC 2021, 2022, 2023) or as an invited lecture to several international conferences and universities. He is an organizer of several workshops related to AI at high-ranked international conferences. He is an Editor in Evolutionary Computation Journal and Associate Editor in Expert Systems with Applications He is involved in both national and European projects. Currently, he is coordinating bilateral projects with Sorbonne University, France (algorithm selection and configuration), Leibniz University Hannover, Germany (fair benchmarking for dynamic algorithm configuration), and the University of Banja Luka, Bosnia and Herzegovina (theoretical and machine learning approaches for graph data). He has previously coordinated national projects on representation learning for stochastic optimization algorithms (2022-2024) and robust statistical analysis for single-objective optimization (2019-2021), as well as an EFSA-funded project on natural language processing for food science (2021-2022).