I am Daniel Rivas
Medical Doctor and PhD in Computational Medicine with a decade of experience designing and developing AI‑driven solutions for biomedical challenges. Expertise spans the entire pipeline from multimodal health data (clinical text, imaging, omics) to the development and deployment of robust algorithms for disease prediction and digital therapeutics.
About
I architect AI systems that transform healthcare data into clinical breakthroughs. As both an MD and PhD in artificial intelligence, I bridge the worlds of medicine and technology to create solutions that are both scientifically rigorous and industrially viable.
Passionate about:
- Precision Medicine: Developing AI frameworks that enable truly personalized patient care
- Scalable Health Tech: Building robust systems that translate research into real-world impact
- Medical Innovation: Advancing novel approaches at the intersection of biology and computation
With over a decade of experience across academia and industry, I now dedicate my efforts to entrepreneurial ventures that push the boundaries of what is possible in AI-driven medicine.
Let's connect if you:
- Lead a health tech company seeking AI/ML expertise
- Are a clinician looking to build data-driven solutions
- Seek a scientific co-founder for a precision medicine venture
- Want to discuss deploying AI in healthcare systems
I am actively collaborating with forward-thinking organizations and individuals who are serious about deploying AI to improve patient outcomes. Reach out to explore how we can partner to make precision medicine a clinical reality.
Languages: English, Spanish, French, Italian.
Experience
- Developing machine learning model for multi‑omics data integration and drug discovery
- Extract and analyze large amounts of sales data
- Develop automation & visualization tools
- Interact with cloud computing systems for data analysis, e.g., AWS & Databricks
- Designed, curated and performed data analysis on different clinical datasets using state‑of‑the‑art software and analysis techniques
- Developed and instructed a clinical course for data and statistical analysis aimed for clinicians
- Participated and coordinated multidisciplinary collaborative projects in basic and clinical medicine
- Provided domain‑specific and technical expertise building machine learning architectures, and performed data analyses
- Supervised numerous students on projects such as: identification of candidate genes and functional motifs in cancer research, prediction of anomalous events from ECG/EEG recordings, deployment of semiautomatic echocardiographic diagnosis, identification of gene expression patterns in vaccine in vivo models
Projects
Technical Skills
Languages
Databases
Other
Education
Uppsala University
Degree: Doctor in Philosophy – Computational Medicine & Artificial Intelligence
Thesis: The revolutionary partnership of computation and biology.
Relevant:
- Secured the Matariki Global Citizens Fellowship
- Graduated from the National (Sweden) School of Medical Bioinformatics
- Attended the Swedish Bioinformatics Advisory Program by SciLifeLab
Uppsala University
Degree: Master of Sciences – Infection Biology
Thesis: Looking into the rabbit hole: a study of evolutionary associations among retroviruses and hosts using rabbit as a model.
Relevant:
- Received the Scholarship for Graduate Studies Abroad from CONACYT
- Graduated with Honors
University of Turku
Degree: Master of Sciences – Biomedical Imaging
Thesis: Through the looking‑glass: microscopy techniques for studying mitochondria.
Relevant:
- Awarded the Personal Research Grant by the Turku University Foundation
- Received a Tukisetelinsaajat (Internship Voucher)
- Won the 2015 Multimedia Contest with Beyond the Limits
University of Guadalajara
Degree: Physician, Surgeon & Obstetrician
Relevant:
- Graduated by Scientific Production and Academic Excellence
- Awarded a Scholarship of Economic Support for Outstanding Students in Research
- Completed Clinical Practice Internship
- Served as Laboratory Instructor
Selected Publications
- Correa‑Correa V, Núñez‑Enríquez JC, Mezei G, et al. Extremely low‑frequency magnetic fields (ELF‑MF) and radiofrequency: Risk of childhood CNS tumors in a city with elevated ELF‑MF exposure. Environ Res. 2025;286(Pt 2):122858. doi:10.1016/j.envres.2025.122858
- Rivas‑Carrillo SD, Akkuratov EE, Valdez Ruvalcaba H, Vargas‑Sanchez A, Komorowski J, San‑Juan D, Grabherr MG. MindReader: Unsupervised Classification of Electroencephalographic Data. Sensors 2023; 23, 2971. doi:10.3390/s23062971
- Rivas‑Carrillo SD, Pettersson ME, Rubin C‑J, Jern P. Whole‑genome comparison of endogenous retrovirus segregation across wild and domestic host species populations. Proceedings of the National Academy of Sciences 2018; 115 (43) 11012. doi:10.1073/pnas.1810058115
- Azpeitia‑Hernandez Y, Rivas‑Carrillo D. Endocarditis Infecciosa. Revista Alpha Ciencias de la Salud 2014; 1(1):29‑39.
- Rivas‑Carrillo SD, Kanamune J, Iwanaga Y, Uemoto S, Daneri‑Navarro A, Rivas‑Carrillo JD. Endothelial Cells Promote Pancreatic Stem Cell Activation During Islet Regeneration in Mice. Transplantation Proceedings 2011, 43, 3209‑3211.