Francisco Carrillo-Perez
PhD in machine learning applied to bioinformatics.
Granada (Spain)
carrilloperezfrancisco at gmail dot com
pacocppacocp9
franciscocp
About me
Hello there!
My name is Francisco (Paco) Carrillo-Perez, and I hold a Ph.D. in machine learning applied to bioinformatics from the University of Granada.
I am working now as a freelance postdoctoral researcher in the Gevaert's lab at Stanford University. I am mainly interested in multi-modal self-supervised learning and generative models for cancer research.
In my spare time I like to climb, take long
hikes, and read
a good
book!
Recent News
| 04/03/2023 | New published publication in Nature Medicine: A deep-learning algorithm to classify skin lesions from mpox virus infection. |
| 02/12/2022 | New published publication in Trends in Molecular Medicine: Imaging genomics: data fusion in uncovering disease heritability. |
| 10/04/2022 | New published publication in the Journal of Personalized Medicine: Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis. |
| 29/11/2021 | New published publication in the Journal of Esthetic and Restorative Dentistry: Applications of artificial intelligence in dentistry: A comprehensive review. |
| 22/09/2021 | New published publication in the BMC Bioinformatics journal: Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion. |
| 19/04/2021 | New published publication in the Computers in Biology and Medicine journal: KnowSeq R-Bioc Package: The Automatic Smart Gene Expression Tool For Retrieving Relevant Biological Knowledge. |
| 12/04/2021 | Honored to become a Fulbright fellow! I will do a twelve-month research collaboration at Stanford University under the supervision of Professor Olivier Geavert starting October 2021. |
| 22/03/2021 | New published publication in the Journal of Dentistry: INFLUENCE OF BACKGROUND COLOR ON COLOR PERCEPTION IN DENTISTRY. |
| 19/02/2021 | New published publication in the Neural Computing and Applications journal: Deep learning to classify ultra-high-energy cosmic rays by means of PMT signals. |