Alejandro Morales Hernández

Alejandro Morales Hernández

He/Him

Postdoctoral researcher

Talks and presentations

A Tutorial on Smart Process Optimization with Bayesian Optimization: Leveraging AI for Efficient Decision-Making

October 26, 2025

Tutorial, The Engineering School, University of Bologna, Bologna, Italy

In this tutorial, we introduce how Bayesian optimization (BO) can be applied to process-optimization tasks for researchers and practitioners, using an adhesive bonding process as example. We explain the theoretical foundations of BO and provide practical guidance (yes, with code) for implementing it in real-world workflows.

Leveraging SUMO for Traffic Twins: Experiences in Urban Traffic Processing

May 13, 2025

Poster, SUMO User conference - German Aerospace Center (DLR), Berlin-Adlershof, Germany

The TORRES project develops an AI-driven framework that integrates diverse mobility data sources with SUMO simulations to improve real-time traffic monitoring, prediction, and decision-making in urban environments. Applied in Brussels and Namur, the framework focuses on three key contributions: traffic calibration, data interpolation, and simulation-assisted prediction for efficient mobility management.

Integration of simulation and machine learning to predict the traffic in the event of disruptions

June 27, 2024

Poster, Rencontres Francophones Transport Mobilite (RFTM2024) - Université Libre de Bruxelles (ULB), Brussels, Belgium

Decision-making in intelligent transportation systems is complex due to urban traffic’s unpredictable dynamics, particularly when assessing the impact of road works on congestion. To address this, a method combining simulation using SUMO and machine learning with GNN is proposed for traffic prediction in the event of road disruptions.

Multi-objective optimization of adhesive bonding process in constrained and noisy settings

May 03, 2023

Talk, International Conference on Optimization and Learning (OLA2023), University of Málaga, Málaga, Spain

This research applies machine learning models within a Bayesian optimization framework to efficiently identify Pareto-optimal parameters for adhesive bonding processes, balancing strength, cost, and quality constraints. By emulating objective and constraint functions with limited experimental data, the approach reduces the need for costly and time-consuming lab experiments.

Encore Abstract: Online learning of windmill time series using Long Short-term Cognitive Networks

November 07, 2022

Talk, Joint International Scientific Conferences on AI and Machine Learning (BNAIC/BeNeLearn2022), Lamot Conference and Heritage Centre, Mechelen, Belgium

This work introduces Long Short-term Cognitive Networks (LSTCNs), a novel gated neural network designed for efficient online learning in windmill farm forecasting. Case study results show that LSTCNs achieve lower forecasting errors and faster training compared to traditional recurrent models.

Multi-objective hyperparameter optimization with performance uncertainty

July 18, 2022

Talk, International Conference on Optimization and Learning (OLA2022), Syracusa Faculty of Architecture, Syracusa, Sicilia, Italy

This paper proposes a multi-objective hyperparameter optimization method that combines Tree-structured Parzen Estimators with Gaussian Process Regression to account for uncertainty in performance evaluations. Experiments on analytical test functions and ML problems demonstrate improved optimization results compared to using TPE or GPR alone.

Multi-objective simulation optimization of the adhesive bonding process of materials

December 13, 2021

Talk, Winter Simulation Conference (WSC2021), JW Marriott Phoenix Desert Ridge Resort & Spa, Phoenix, Arizona, US (virtual)

This research applies Bayesian optimization with Gaussian Process Regression and Logistic Regression to identify optimal adhesive bonding process parameters efficiently. The approach reduces experimental effort while guiding designs toward Pareto-optimal solutions for lightweight automotive applications.