Alumni

Alumni

Alumni

The DAE Alumni Network is founded on the PhD graduates of the UC3M Aerospace Engineering Department. It aims to connect former colleagues and unite different generations of PhD students who have shared common experiences, values, and goals during their doctoral studies. This community serves as a platform for members to reconnect, share updates on their post-graduation endeavors, celebrate achievements, and exchange insights on ongoing projects and relevant opportunities of interest to the network and its affiliates.

Through the DAE Alumni Network, former PhD students can foster long-term, collaborative relationships with each other and maintain strong ties with the department

Activities
2024
Alumni Network Seminar
  • 18th October, Friday, 13h

    For this event in the Aerospace Alumni Seminar Series, we have the pleasure of hosting Dr. Fermín Mallor, Director of Growth and Product Manager in PredictiveIQ.

    The event will take place in the Salón de Grados on Friday October 18th at 13:00 pm and will be streamed (Online).

    Fermín Mallor Franco is an alumni of the Aerospace Engineering BSc program at UC3M. He began his research career during his final year at UC3M, studying turbulent heat transfer under Professors Andrea Ianiro and Stefano Discetti. After graduation, Fermín moved to Stockholm to pursue a Master’s and PhD in Engineering Mechanics, specializing in Fluid Mechanics, at KTH Royal Institute of Technology.

    At KTH, he worked on wind tunnel testing, large-scale simulations of turbulent flows around wings, and developed advanced data-driven models and frameworks. He also participated in projects involving large language models (LLMs) and sustainability.

    Recently, Fermín transitioned to industry by joining PredictiveIQ as Director of Growth and Product Manager. In this role, he is responsible for expanding the company’s presence in the European market and overseeing the development of physics-informed AI solutions for industrial applications.

    “PredictiveIQ: Bridging Physics and AI for Industrial Innovation”

    Abstract: 

    PredictiveIQ bridges academia and industry by integrating advanced machine learning techniques into industrial applications. In this seminar, we present our development of generalized physics-informed AI models through our Accelerated Engineering capabilities. By leveraging operator-based networks and embedding intrinsic physical properties into our models, we overcome the limitations of traditional AI approaches that require large amounts of data.

    Our methodology enables the creation of surrogate models trained with minimal simulations, empowering engineering teams to optimize products and prototypes efficiently and accelerate time-to-market. Additionally, these lightweight models are integrated into Digital Twins within our Optimized Product Performance services, facilitating predictive maintenance and the optimization of entire systems tailored to specific conditions.

    Our mission is to make advanced machine learning practical and accessible for industrial applications, helping businesses innovate and remain competitive by harnessing the latest academic research. Attendees will gain insights into the use of physics-informed AI models, the benefits of operator-based networks, and their impact on solving complex industrial challenges.

The seminar begin at 13:00 pm and will take place in the Salón de Grados, Leganés.
No previous registration is required.

     

    Members