Javier Jiménez — Data Scientist
for real projects & play

"In recent years, the production of LNG and LPG ships has increased, driven by the increasing demand for natural and petroleum gases. To meet this demand, ship designs must be optimized at all stages, requiring designers to have advanced, efficient and accurate design tools. This study presents new statistical and regression analyses of data extracted from the Hyundai Heavy Industries Shipbuilding Group catalogue which includes information on LNG and LPG ships built between 1979 and 2023. The database includes 145 LNG carriers and 322 LPG carriers, representing approximately 20% of the global gas carrier fleet. Simple and multiple regression analyses were used to estimate dependent variables and their correlation to other ship parameters. In addition, Machine Learning algorithms were trained and compared against these traditional methods. This study provides updated tools to support the preliminary design of LNG and LPG ships, enhancing the understanding and accuracy of early-stage design decisions."

"We present HADAD (Hexagonal A-Star with Differential Algorithm Designed for weather routing), a novel optimization algorithm for weather routing. HADAD conducts a global exploration using an A⋆ search on a hexagonal grid with higher-order neighbors, enhancing directional flexibility and overcoming limitations of traditional graph searches that constrain vessel movements. It then refines the solution using a discrete Newton–Jacobi variational method, ensuring convergence to a locally optimal, smooth route in continuous space. To evaluate the effectiveness of HADAD, we developed a benchmark comprising 1,560 instances over a full year, varying in origin–destination pairs, vessel speeds and oceanographic conditions. Our results show that HADAD outperforms pure A⋆ graph search methods by an extra 4% savings with respect to the shortest-distance route, thanks to more flexible smoother trajectories obtained by gradient descent. In our seasonal study we observe that the savings distribution shows large seasonal variations (double savings on average in winter with respect to summer) and contains a significant number of outliers. Savings reach 27% in these cases of extreme weather events. Validation of the algorithm performed with synthetic vector fields has been conducted. In this setting, the algorithm has been adapted to handle fuel consumption optimization for Just-in-Time arrival. By integrating global search and local optimization, HADAD effectively balances computational efficiency with route optimality, offering a practical and adaptable solution for real-world weather routing applications."

"The number of babies born prematurely is rising every year... In this work we present NEOCAM, an open source platform conceived to be deployed in NICUs for the real-time non-invasive monitorization of preterm in incubators or beds using edge-computing devices... We show how information of preterm limb's activity, face emotion and breath rate can be measured continuously in real time by processing video with three AI-based models running in parallel and dedicated algorithms designed to be flexible and robust."