Research Group

The OPTImization with Multiple Objectives (OptiMO) group works on the design, analysis and application of algorithms for multi-objective optimization and reinforcement learning. We are based at the Department of Computer Science, IIMAS-UNAM. The group currently supervises 13 students across BSc, MSc, and PhD levels, and is funded by PAPIIT and Google.

Write to carlos.hernandez at iimas.unam.mx if you are interested in joining. Open thesis topics are listed here.

Current Students

PhD students

• Rodrigo F. Velázquez Cruz — Robust neural architecture search for multi-objective problems. MSc graduate of the group, December 2024.
• José Olivas Díaz — Multi-agent multi-objective reinforcement learning via evolutionary algorithms. MSc graduate of the group, August 2025.
• Iván Alcalá Paz — Kolmogorov-Arnold networks for differential equations. Co-advised with Leonid Serkin.
• Daniel Alonso Bastos — Policy landscape modelling and navigation in reinforcement learning via graph neural network surrogate models.
• Ricardo D. Fernández Noguez — Preference-based multi-objective reinforcement learning. Co-advised with Gibran Fuentes.

MSc students

• Teresa Becerril Torres — Particle swarm optimization for multi-objective reinforcement learning.
• Pablo Uriel Benítez Ramírez — Multi-objective reinforcement learning based on Pareto Tracer for multimodal traffic management. Co-advised with Oliver Schütze.
• Fernando R. Valenzuela G. de León — AI assistant for dance choreography creation. Co-advised with Wendy Aguilar.
• Luz Itzel Valdeolivar Hernández — Hyperparameter tuning for the superiorization method via exploratory landscape analysis. Co-advised with Edgar Garduño.
• José A. Alonso González — Multi-objective optimization for water distribution network operation. Co-advised with Cristina Verde.

BSc students

• Alexandra Jiménez — Archivers for MORL: policy and objective spaces.
• Emma Jiménez — SPO+ for stochastic inventory management.
• Néstor Hernández — MORL benchmark with adjustable front geometries.

Graduated Students

Postdoctoral researchers

• Gerardo Altamirano Gómez — Evolutionary design of deep invariance learning architectures for object recognition. 2024–2025.

MSc

• María Carmen Aguirre Delgado — When does the weighted sum method work well in multi-task learning? October 2025. Co-advised with Gibran Fuentes.
• José Olivas Díaz — Evaluation of multi-objective multi-agent reinforcement learning algorithms via weighted sum. August 2025. Now PhD student in the group.
• Sofía Borrel Miller — R2-based multi-objective reinforcement learning. June 2025.
• Alberto M. Millán Prado — Uncertainty quantification for multi-objective reinforcement learning. February 2025.
• Rodrigo F. Velázquez Cruz — Cooperative multi-indicator ant colony optimization for multi-objective vehicle routing under uncertainty. December 2024. Now PhD student in the group.
• Víctor M. Sánchez Sánchez — Effect of temporal heterogeneity on selection pressure of evolutionary algorithms. November 2024. Co-advised with Carlos Gershenson.
• Juan A. López Rivera — Parameter optimization in weighted entropic associative memory systems. February 2024. Co-advised with Luis Pineda.

BSc

• Fernando Avitua Varela — Objective reduction in multi-objective problems using quality indicators. November 2024.