I work on Agentic AI. Specifically, I focus on how to achieve more with LLMs by providing them with access to tools that can act on behalf of the users.
Experience
I currently work as a Developer Advocate at Arcade AI
Previously, I worked as:
- Postdoctoral Associate at the Weill Institute for Cell and Molecular Biology at Cornell University as part of the Yu lab.
- Postdoctoral Research Assistant at Fundação Getúlio Vargas
During my undergraduate years I worked in web development until 2015, when I joined the PaccanaroLab group as a PhD Student under the supervision of Prof. Alberto Paccanaro.
Academic stuff
I got a PhD in Computer Science at Royal Holloway, University of London specialising in Computational Biology. Before that, I graduated from Universidad Católica "Nuestra Señora de la Asunción" with an Engineering degree in Computer Science, specialising in many-objective optimisation using the Particle Swarm meta-heuristic.
Publications
See all my publications in Google Scholar.
Selected Publications
-
Horacio Caniza, Juan J. Cáceres, Mateo Torres, Alberto Paccanaro (2024).
LanDis: the disease landscape explorer.
European Journal of Human Genetics .
doi: 10.1038/s41431-023-01511-9 -
Sacramento C. Q., Fintelman-Rodrigues N, Dias S. S. G., Temerozo J. R., Da Silva A. P. D., da Silva C. S., Blanco C., Ferreira A. C., Mattos M., Soares V. C., Pereira-Dutra F., Miranda M. D., Barreto-Vieira D. F., da Silva M. A. N., Santos S. S., Torres M., Chaves O. A., Rajoli R. K. R., Paccanaro A., Owen A., Bou-Habib D. C., Bozza P. T., Souza T. M. L. (2022).
Unlike Chloroquine, Mefloquine Inhibits SARS-CoV-2 Infection in Physiologically Relevant Cells.
Viruses .
doi: 10.3390/v14020374 -
Torres, M., Yang, H., Romero, A.E., and Paccanaro, A. (2021).
Protein function prediction for newly sequenced organisms.
Nature Machine Intelligence .
doi: 10.1038/s42256-021-00419-7 -
Santos, S., Torres, M., Galeano, D., Sánchez, M., Cernuzzi, L., and Paccanaro, A. (2021).
Machine learning and network medicine approaches for drug repositioning for COVID-19.
Cell Patterns .
doi: 10.1016/j.patter.2021.100396 -
Zhou, N., Jiang, Y., Bergquist, T.R. et al. (2019).
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.
Genome Biology , 20, 244 (2019).
doi: 10.1186/s13059-019-1835-8
Links
If you're here for the Pattern Recognition and Machine Learning study group, you can find useful material here.