#  Made in the UPM 

 



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####  calendar\_today Date and Time 

 **May 12, 2026** 

 05:30PM EDT 

####  pin\_drop Location 

 **RCCHU Conference Room**  

 [26 Trowbridge St.  
Cambridge, MA 02138  
United States



 ](<https://www.google.com/maps?q=US MA Cambridge 02138 26 Trowbridge St.>) 



 

 [ Zoom Link arrow\_circle\_right ](https://zoom.us/j/95604709358?pwd=H6Z2bjDYU0c8ogUhPVmJQjqSRqUPgF.1) 

 



 

**Rethinking Therapeutic Opportunities with AI and Network Medicine**

Can we rethink how we discover treatments for complex diseases? This talk explores how artificial intelligence and network medicine can be combined to uncover new therapeutic opportunities beyond traditional approaches. By modeling diseases within large-scale biomedical networks, this framework enables the identification of new indications and the design of combinatorial strategies across diverse types of interventions. Drawing on research developed during her stay in Boston, Lucía Prieto-Santamaría will present how integrating network-based insights with representation learning allows the generation of mechanistically grounded and clinically relevant hypotheses. This work points towards a new paradigm for therapeutic discovery, where AI helps navigate the complexity of human disease to identify more effective interventions.

Lucía Prieto Santamaría. Lucía is an Assistant Professor at the Escuela Técnica Superior de Ingenieros Informáticos (ETSIInf) of the Universidad Politécnica de Madrid (UPM) and a researcher at the Medical Data Analytics Laboratory (MEDAL) of the Center for Biomedical Technology (CTB). She holds a Ph.D. in Software, Systems, and Computing (2023), a Master's degree in Computational Biology (2019), and a Bachelor's degree in Biotechnology (2018). Her research lies at the intersection of biomedical and computational sciences, with a strong focus on network medicine and drug repurposing. Her expertise spans medical knowledge representation, biomedical informatics, and health-related social media analysis, with artificial intelligence playing a central role in her work. She is currently a visiting scholar at the Center for Complex Network Research (CCNR) of the Northeastern University, supported by a competitive José Castillejo–Fulbright fellowship, marking her second research stay in the laboratory of Prof. Barabási.

   ![poster](/sites/g/files/omnuum986/files/styles/hwp_1_1__720x720_scale/public/2026-05/Made%20in%20UPM%202026%20Luci%CC%81a%20Prieto%20Santamari%CC%81a_0.png?itok=8IMBa5on) 

 

**Machine Learning and Agent-Based Simulation for Smarter Cities: Optimizing Bike Sharing Networks**

How can cities use their own data to build better infrastructure? At the City Science group within the MIT Media Lab, I have been developing a framework that combines machine learning and agent-based simulation to optimize the location and size of bike sharing stations. Designed to be translatable to any city in the world, the system learns from existing usage patterns to identify which urban parameters drive high ridership and low rebalancing demand, helping cities everywhere expand or redesign their networks more effectively and efficiently.

Xabier Bastida Zubiaurre. Industrial Engineering student at Tecnun (University of Navarra).

   ![poster](/sites/g/files/omnuum986/files/styles/hwp_1_1__720x720_scale/public/2026-05/Made%20in%20UPM%202026%20Xabier%20Bastida.png?itok=hbceVx_o) 

 



 

 



 

 

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