A5033315494- Business Process Modeling and Analysis
- Geographic Information Systems Studies
- Data Visualization and Analytics
- Numerical Methods and Algorithms
- Image Retrieval and Classification Techniques
- Constraint Satisfaction and Optimization
- Parallel Computing and Optimization Techniques
- Corporate Insolvency and Governance
- Psychometric Methodologies and Testing
- Private Equity and Venture Capital
- Insurance and Financial Risk Management
- Ethics and Social Impacts of AI
California University of Pennsylvania
2025
New York University
2024
Fundação Getulio Vargas
2023
The emergence of distinct machine learning explanation methods has leveraged a number new issues to be investigated. disagreement problem is one such issue, as there may scenarios where the output different disagree with each other. Although understanding how often, when, and agree or important increase confidence in explanations, few works have been dedicated investigating problem. In this work, we proposed Visagreement, visualization tool designed assist practitioners Visagreement builds...
High-dimensional data, characterized by many features, can be difficult to visualize effectively. Dimensionality reduction techniques, such as PCA, UMAP, and t-SNE, address this challenge projecting the data into a lower-dimensional space while preserving important relationships. TopoMap is another technique that excels at underlying structure of leading interpretable visualizations. In particular, maps high-dimensional visual space, guaranteeing 0-dimensional persistence diagram Rips...
With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountability model predictions. As result, large number local explainability models have been developed popularized. However, machine learning explanations are still hard to evaluate compare due high dimensionality, heterogeneous representations, varying scales, stochastic nature some these methods. Topological Data Analysis...
With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountability model predictions. As result, large number local explainability models have been developed popularized. However, machine learning explanations are still hard to evaluate compare due high dimensionality, heterogeneous representations, varying scales, stochastic nature some these methods. Topological Data Analysis...
High-dimensional data, characterized by many features, can be difficult to visualize effectively. Dimensionality reduction techniques, such as PCA, UMAP, and t-SNE, address this challenge projecting the data into a lower-dimensional space while preserving important relationships. TopoMap is another technique that excels at underlying structure of leading interpretable visualizations. In particular, maps high-dimensional visual space, guaranteeing 0-dimensional persistence diagram Rips...
The widespread use of machine learning in credit scoring has brought significant advancements risk assessment and decision-making. However, it also raised concerns about potential biases, discrimination, lack transparency these automated systems. This tutorial paper performed a non-systematic literature review to guide best practices for developing responsible models scoring, focusing on fairness, reject inference, explainability. We discuss definitions, metrics, techniques mitigating biases...