Alejandro Guerrero-López

Alejandro Guerrero-López

Postdoctoral researcher in Bayesian Machine Learning.

Alejandro Guerrero-López

Who am I

Currently, I am a postdoctoral researcher at the Bioengineering and Optoelectronics Group in Universidad Politécnica de Madrid (UPM).

Research. My research primarily focuses on advancing the field of Machine Learning by understanding and developing novel probabilistic models that address real-world biomedical problems. To overcome the common challenges encountered in the biomedical field, such as missing data and heterogeneous modalities, I am dedicated to learn, develop and apply heterogeneous generative models that offer more accurate and reliable predictions. I have a deep interest in the fields of eXplainable AI (XAI) and ethical AI. With the growing influence of AI technologies in our society, it is crucial to ensure transparency, trustworthiness, and ethical alignment.

Previously. I obtained my Ph.D. with honors (cum laude) from UC3M (Universidad Carlos III de Madrid) in the field of Machine Learning for Personalised Medicine, specifically the clinical microbiology field. I am grateful for the invaluable guidance and joint supervision provided by Dr. Pablo M. Olmos and Dr. Vanessa Gómez-Verdejo throughout my doctoral journey. During my thesis, I was affiliated with the Gregorio Marañón Health Research Institute (IISGM), where I had the privilege of working under the guidance of Dr. Belén Rodríguez-Sánchez. The experience gained during my time at IISGM has significantly shaped my research interests and provided me with valuable insights into the challenges faced by the biomedical community.

🌟 Big news 🌟

🌐 Introducing BacteriaID: A Leap Forward in Bacterial Identification 🌐

We’re thrilled to unveil our latest innovation: BacteriaID, a pioneering web application poised to transform the landscape of bacterial classification. Developed from our recent research, BacteriaID employs state-of-the-art MALDI-TOF MS technology to automate the identification of toxigenic European ribotypes of Clostridioides difficile.

BacteriaID Landing Page

🔒 Currently in closed beta, BacteriaID represents our most ambitious milestone yet, embodying the synergy of cutting-edge research and practical application. This tool helps advancing microbiology through technology, providing an accessible, accurate, and indispensable resource for researchers and healthcare professionals worldwide.

👥 Crafted through a collaborative effort with renowned institutions, BacteriaID is not just a tool but a movement towards a future where bacterial infections are detected with unprecedented precision and speed.

Stay tuned for exclusive updates, insights, and the official launch date. Join us in this groundbreaking journey at BacteriaID.

🌟 Latest Buzz 🌟

  • March 2024: 📢 Exciting developments this month! Our research on AF detection via PPG signals, incorporating explainability features, has been officially accepted by EAAI for publication! Read it here!

  • March 2024: 📢 We’re thrilled to unveil our latest contribution to Parkinson’s disease research: the MARTA model. This innovative approach sheds new light on how Parkinson’s influences phonemic groups in speech. Learn more about our findings!

  • March 2024: 📢 Exciting news! Along with the dataset, a descriptive preprint of the dataset is also public! Perfect for understand how to use the dataset, it comes with some code to launch your first experiments! Check it out now.

  • March 2024: 📢 Exciting news! We’ve released a groundbreaking public dataset on Parkinsonian speech. Perfect for researchers! Use it wisely and don’t forget to cite! Explore now.

  • January 2024 🚀: Dive into our latest breakthrough! Discover our new preprint on revolutionizing Clostridioide difficile ribotyping with cutting-edge probabilistic Machine Learning. Check it out!

👀 Keep your eyes peeled for more thrilling updates and milestones in my journey!

Short Bio