• Developed algorithms and tools for network science, dimensionality reduction, node embedding, word embedding, network alignment, bioinformatics, graphlets, knowledge graphs, NLP, and explainable AI to analyze biological networks and support personalized medicine.• Designed and implemented the full-stack architecture of GraphFusion, an open-source, modular, and scalable application built using Docker, Python, C++, JavaScript, and MySQL. This comprehensive platform facilitates network visualization, analysis, and the seamless integration of multi-network data. GraphFusion leverages a combination of multi-modal embeddings and various matrix factorization methodologies, focusing on NMF and NMTF with orthogonality and non-negativity constraints.• Created vina_docking, an open-source Python module designed for simulating and analyzing protein-ligand interactions. This module leverages AutoDock Vina and protein visualization libraries to facilitate accurate and insightful analysis of molecular interactions.• Conducted research on matrix factorization techniques for large sparse matrices and parallel solutions for efficient computation on Marenostrum, a leading European supercomputer.• Actively reviewed papers for the EasyChair conference management system.• Implemented network force-directed visualization techniques, incorporating spring embeddings and visualization libraries like PyVis. This approach enabled the comprehensive visualization and analysis of biological networks.• Utilized Large Language Models (LLMs) to vectorize and summarize text extracted from biological and network science papers relevant to our research domain.• Contributed to the creation of the startup Graphlet Technologies, which aimed to leverage cutting-edge eXplainable AI (XAI) methods for multi-omic data fusion.

• Innovated software tools for drug discovery and molecular data analysis using Python, C++, graph matching, and machine learning techniques to infer molecular similarity and reactivity prediction.• Applied Quantitative Structure-Activity Relationship (QSAR) models, genetic algorithms, and NLP-inspiredmethodologies, including Node2Vec and DeepWalk. These advanced techniques were instrumental in refining molecular similarity measures and optimizing virtual screening processes.• Contributed to the open-source RDKit project by developing a C++ module for molecular reduction using the ErG approach based on pharmacophore-type node descriptions.• Instructed Mathematics, Statistics, and Programming for engineering degree programs.• Collaborated with the Intelligent Robotics and Computer Vision Group (IRCV) to analyze MRI images and classify breast tumors.

• Engaged in a project titled “Development of solutions for software distribution in new hardware automotive architectures: Micro-controllers vs Micro-processors.” This initiative aimed to explore and implement Linux kernel solutions customized for car modular computers using Yocto, C++, and embedded Linux systems. The goal was to facilitate the seamless integration of car computers with a broader range of electronic sensors and actuators.

• Led the project to build an Android application focused on call management and secure voicemail, effectively blocking and redirecting telemarketing calls. Leveraged technologies such as Firebase cloud database, GCP, SIP Protocol, PHP web services, and REST. • Collaborated on an international phone recharging website using Python, Heroku, PostgreSQL, Posa (recharging API), and AuthorizeNet (payment API).

• Designed an Android application to automate vehicle checking processes at Toyota factories with the help of RFID readers. This project participated in the 2013 RFID Journal Awards for best implementations.• Developed and designed a web application using .NET Framework, C#, and SQL to work alongside RFID data collected from live production at a Nestle factory.• Worked on PCB design, circuit assembly, and C# programming for the first CleverCap prototype. CleverCap was the only medication adherence solution that gave doctors access to patients’ detailed medication adherence. The project received the Frost & Sullivan New Product Innovation Award for Medical Compliance 2013.