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| aira:start [2026/01/15 16:10] – [2026-01-15] mzk | aira:start [2026/01/30 11:04] (current) – [Schedule Autumn 2025] mtm | ||
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| ===== Schedule Autumn 2025 ===== | ===== Schedule Autumn 2025 ===== | ||
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| + | * **[PHD TRACK] 2026.01.29**: | ||
| + | * Meeting link: | ||
| + | * Recording: | ||
| + | * Presentation slides: | ||
| * **[PHD TRACK] 2026.01.22**: | * **[PHD TRACK] 2026.01.22**: | ||
| * Meeting link: | * Meeting link: | ||
| - | * Recording: | + | * Recording: |
| - | * Presentation slides: | + | * Presentation slides: |
| * **[RESEARCH TRACK] 2026.01.15**: | * **[RESEARCH TRACK] 2026.01.15**: | ||
| * Meeting link: | * Meeting link: | ||
| - | * Recording: | + | * Recording: |
| * Presentation slides: | * Presentation slides: | ||
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| + | ==== 2026-01-29 ==== | ||
| + | <WRAP column 15%> | ||
| + | {{ : | ||
| + | </ | ||
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| + | <WRAP column 75%> | ||
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| + | **Speaker**: | ||
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| + | **Title**: Calm-Data Generator: A Flexible Framework for Synthetic Dataset Creation Under Concept Drift. | ||
| + | |||
| + | **Abstract**: | ||
| + | Calm-Data Generator is designed to produce realistic synthetic datasets exhibiting different types of concept drift, enabling controlled evaluation of machine-learning models in dynamic environments. The framework implements multiple drift mechanisms, supports tabular data with customizable complexity, and provides a flexible API for defining data-generation functions, experimental scenarios, and temporal transitions. Its primary goal is to facilitate reproducible experimentation in concept drift research, allowing researchers to benchmark methods, analyze performance degradation, | ||
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| + | **Biogram**: | ||
| + | Antonio Guillén-Teruel (a.guillenteruel@um.es) is a PhD Student graduated in Mathematics from the University of Murcia in 2020. In 2021 he got a Masters degree in Big Data from the same university and started his Ph.D studies in Informatics. The following year he got a Masters degree in Advanced Mathematics at the University of Murcia. His research focuses on imbalanced problems in Machine Learning (ML), including both in regression and classification problems, as well as the study of concept drift in medical domains for imbalanced datasets. | ||
| + | </ | ||
| + | <WRAP clear></ | ||
| ==== 2026-01-22 ==== | ==== 2026-01-22 ==== | ||