publications: update

Author: anjos

.gitignore

@@ -3,7 +3,7 @@
 .DS_Store
 .pixi/
 *.synctex.gz
-main.pdf
+cv.pdf
 
 # latex files
 *.swp

publications.bib

@@ -2,7 +2,7 @@
 %%  2026  %%
 %%%%%%%%%%%%
 
-@article{acmhealth-2025,
+@article{acmhealth-2026,
   author = {Queiroz Neto, Dilermando and Carlos, Anderson and Anjos,
   Andr{\'{e}} and Berton, Lilian},
   keywords = {Fairness, Foundation Models, World Health},
@@ -40,6 +40,53 @@ @article{acmhealth-2025
 %%  2025  %%
 %%%%%%%%%%%%
 
+@ARTICLE{melba-2025,
+  author = {{\"{O}}zbulak, G{\"{o}}khan and Jimenez-del-Toro, Oscar
+  and Fatoretto, Ma{\'{\i}}ra and Berton, Lilian and Anjos, Andr{\'{e}}},
+  keywords = {machine learning, Medical Image Analysis,
+    multi-objective optimization, Multidimensional Fairness Evaluation,
+  Utility-Fairness Trade-off},
+  month = dec,
+  title = {A Multi-Objective Evaluation Framework for Analyzing
+  Utility-Fairness Trade-Offs in Machine Learning Systems},
+  journal = {The Journal of Machine Learning for Biomedical Imaging},
+  volume = {3},
+  year = {2025},
+  pages = {938-957},
+  doi = {10.59275/j.melba.2025-ab9a},
+  abstract = {The evaluation of fairness models in Machine Learning
+    involves complex challenges, such as defining appropriate metrics,
+    balancing trade-offs between utility and fairness, and there are
+    still gaps in this stage. This work presents a novel
+    multi-objective evaluation framework that enables the analysis of
+    utility-fairness trade-offs in Machine Learning systems. The
+    framework was developed using criteria from Multi-Objective
+    Optimization that collect comprehensive information regarding this
+    complex evaluation task. The assessment of multiple Machine
+    Learning systems is summarized, both quantitatively and
+    qualitatively, in a straightforward manner through a radar chart
+    and a measurement table encompassing various aspects such as
+    convergence, system capacity, and diversity. The framework’s
+    compact representation of performance facilitates the comparative
+    analysis of different Machine Learning strategies for
+    decision-makers, in real-world applications, with single or
+    multiple fairness requirements. In particular, this study focuses
+    on the medical imaging domain, where fairness considerations are
+    crucial due to the potential impact of biased diagnostic systems on
+    patient outcomes. The proposed framework enables a systematic
+    evaluation of multiple fairness constraints, helping to identify
+    and mitigate disparities among demographic groups while maintaining
+    diagnostic performance. The framework is model-agnostic and
+    flexible to be adapted to any kind of Machine Learning systems,
+    that is, black- or white-box, any kind and quantity of evaluation
+    metrics, including multidimensional fairness criteria. The
+    functionality and effectiveness of the proposed framework are shown
+    with different simulations, and an empirical study conducted on
+    three real-world medical imaging datasets with various Machine
+    Learning systems. Our evaluation framework is publicly available at
+  https://pypi.org/project/fairical.},
+}
+
 @article{cbm-2025,
   author = {Amiot, Victor and Jimenez-del-Toro, Oscar and
     Guex-Croisier, Yan and Ott, Muriel and Bogaciu, Teodora-Elena and