Keynote Lectures

Available soon.

 

Brief Bio
João Gama is a Full Professor at the Faculty of Economy, University of Porto. He is a researcher and vice-director of LIAAD, a group belonging to INESC TEC. He got the PhD degree from the University of Porto, in 2000. He is a Senior member of IEEE.He has worked on several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery, Learning from Massive, and Structured Data, etc. He served as Co-Program chair of ECML 2005, DS 2009, ADMA 2009, IDA 2011, and ECML/PKDD 2015. He served as track chair on Data Streams with ACM SAC from 2007 till 2016. He organized a series of Workshops on Knowledge Discovery from Data Streams with ECML/PKDD, and Knowledge Discovery from Sensor Data with ACM SIGKDD. He is the author of several books on Data Mining (in Portuguese) and authored a monograph on Knowledge Discovery from Data Streams. He authored more than 250 peer-reviewed papers in areas related to machine learning, data mining, and data streams. He is a member of the editorial board of international journals ML, DMKD, TKDE, IDA, NGC, and KAIS. He (co-)supervised more than 12 PhD students and 50 MSc students.

Trustworthy Benchmarking for Black-Box Single-Objective Optimization

 

Brief Bio
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).

Search Trajectories Illuminated

 

Brief Bio
Gabriela Ochoa is a Professor of Computing Science at the University of Stirling in Scotland, UK. Her research lies in the foundations and applications of evolutionary algorithms and metaheuristics, with a recent emphasis on fitness landscape analysis and visualisation. She holds a PhD from the University of Sussex, UK, and has worked at the University Simon Bolivar, Venezuela, and the University of Nottingham, UK. Her Google Scholar h-index is 44. She has published over 180 refereed articles and obtained 6 best-paper awards and 10 other nominations (GECCO, EvoStar and PPSN), many of them related to her work on Local Optima Networks (LONs) and Search Trajectory Networks (STNs). She collaborates cross-disciplines to apply evolutionary computation in healthcare and conservation. She has been active in organisation and editorial roles in venues such as the Genetic and Evolutionary Computation Conference (GECCO), Parallel Problem Solving from Nature (PPSN), the Evolutionary Computation Journal (ECJ) and the ACM Transactions on Evolutionary Learning and Optimisation (TELO). She is a member of the executive board for The ACM interest group in evolutionary computation, SIGEVO, and the editor of the SIGEVOlution newsletter. In 2020, she was recognised by the leading European event on bio-inspired algorithms, EvoStar, for her outstanding contributions to the field.