Marcos M. Raimundo

Professor of Machine Learning and Optimization

Institute of Computing - University of Campinas

Marcos M. Raimundo is an Assistant Professor at University of Campinas (UNICAMP), in the Institute of Computing (IC). He is also a faculty member of the REasoning for COmplex Data laboratory (Recod.ai). His research interests include Machine Learning, Multi-objective Optimization, Ethical AI, Mathematical Programming.

Teaching

SEE ALL →

Projects

Long-Term Impacts of Machine Learning-Supported Decisions

2026 – 2029
Responsible AI
Long-Term Fairness
Sequential Decision-Making
Multi-Objective Optimization
Offline Reinforcement Learning
Contextual Bandits
Performative Prediction

A two-stage sequential decision-making framework combining offline reinforcement learning, contextual bandits, and multi-objective optimization to eliminate harmful feedback loops, mitigate selective labels (reject inference), and ensure long-term fairness and policy flexibility.

Multi-objective Optimization and Ensemble Learning for Machine Learning Models

2014 – 2025
Multi-objective Optimization
Continuous Optimization
Mathematical Programming
Ensemble Learning
Algorithmic Fairness
Machine Learning

Research on continuous and many-objective optimization techniques (NISE/MONISE) applied to regularized machine learning, model selection, ensemble generation, and algorithmic fairness.

AI for Finance - HIAAC

2022 – 2026
Credit Scoring
Fraud Detection
Edge Computing
Explainable AI
Fairness
Financial Inclusion
Machine Learning
Interpretability

The goal of this research line is to develop robust and transparent AI models for credit analysis and similar problems in the financial sector, such as fraud detection.

Applied Deep Learning for Specialized Domains

2018 – ongoing
Deep Learning
Computer Vision
Applied Machine Learning
Collaborative Research

Collaborations applying deep learning methods to specific, real-world domain problems — from ecological image classification to medical imaging and code generation.

No matching items

SEE ALL →

Latest Publications

SEE ALL PUBLICATIONS →

Meet the team

A growing group of students and collaborators working on machine learning, optimization, and fairness.

Meet the team →