L. Julián Lechuga López
PhD candidate in Computer Science and Engineering, Tandon School of Engineering, New York University
Graduate Research Assistant, Clinical AI Laboratory, New York University Abu Dhabi
My research is focused on improving the reliability of multimodal learning and vision-language foundation models using uncertainty quantification for AI-assisted clinical applications, proudly advised by Farah Shamout and Tim G. J. Rudner. As a CS Global PhD Fellow, I am part of the Clinical AI Laboratory at New York University Abu Dhabi.
I received a double master’s degree in Mathematics and Informatics Data Science MIDS from Université de Paris Cité and an undergraduate degree in mechatronics engineering from the Instituto Tecnológico y de Estudios Superiores de Monterrey ITESM.
For specific details, please see my CV.
Born and raised in Mexico, I have been privileged to live in France, Germany, Latvia, Japan, Belgium, the United Arab Emirates and the United States. All these places have given me amazing experiences, reinforcing my desire to learn about the world, science, culture and different languages. If you stumbled upon my profile, feel free to reach out to me if you want to chat or discuss about my background or anything you are curious about. I am no expert and consider myself as a always chasing knowledge, but I have experience that may be useful for anyone navigating moving to another country, applying to gradschool, research, or life in general!
“Stand on the shoulders of giants”
News
Jul ‘25 Presented our work on Uncertainty-Aware Multimodal AI for Respiratory Shock Detection at IEEE EMBC 2025.
Jun ‘25 Presented Uncertainty-Aware Foundation Models for Trustworthy Chest X-ray Report Generation at the CHIL Doctoral Symposium and Uncertainty Quantification for Machine Learning in Healthcare: A Survey at CHIL 2025 at UC Berkeley.
May ‘25 Presented Uncertainty-Aware Multimodal AI for Trustworthy Clinical Decision Support at SAIL 2025 in beautiful Puerto Rico!
Oct ‘24 I was a finalist at the NYUAD GradSlam with the 3-minute pitch: Improving the Future of Clinical Diagnostics.
Dec ‘23 My first official poster presentation! Informative Priors Improve the Reliability of Multimodal Clinical Data Classification at ML4H in New Orleans.
Jul ‘23 Gave a tutorial session on Open Source in Healthcare: Industry & Academia at the Bumblekite ML Summer School in Healthcare & Biosciences, ETH Zürich.
May ‘23 Presented Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives at the Trustworthy ML for Healthcare Workshop at ICLR in Kigali, Rwanda.