PhD. on Machine Learning at the University of Adelaide. My main research interests are in the fields of machine learning, optimization, simulation and data visualization. I enjoy building interactive systems to allow domain experts to explore complex machine learning and optimization systems.

• R. Hermoza, G. Maicas, J. C. Nascimento, and G. Carneiro. “Censor-Aware Semi-supervised Learning for Survival Time Prediction from Medical Images” Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Cham, 2022, pp. 213–222. DOI: 10.1007/978-3-031-16449-1_21
• R. Hermoza, G. Maicas, J. C. Nascimento, and G. Carneiro. “Post-Hoc Overall Survival Time Prediction From Brain MRI” 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): 1476-1480 DOI: 10.1109/ISBI48211.2021.9433877
• R. Hermoza, G. Maicas, J. C. Nascimento, and G. Carneiro. “Region Proposals for Saliency Map Refinement for Weakly-Supervised Disease Localisation and Classification.” Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Cham, 2020, pp. 539–549. DOI: 10.1007/978-3-030-59725-2_52
• M. Hasani-Shoreh, R. H. Aragonés, and F. Neumann. “Neural networks in evolutionary dynamic constrained optimization: computational cost and benefits.” Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020). DOI: 10.3233/FAIA200103
• M. Hasani-Shoreh, R. H. Aragonés, and F. Neumann. “Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation.” 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, 2020, pp. 289-296. DOI: 10.1109/SSCI47803.2020.9308154
• Renato Hermoza and Ivan Sipiran. “3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network.” Proceedings of Computer Graphics International 2018 (CGI 2018). ACM, New York, NY, USA, 5-11. DOI: 10.1145/3208159.3208173
• E. Garcia, R. Hermoza, C. B. Castanon, L. Cano, M. Castillo and C. Castanñeda. “Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning,” 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, 2017, pp. 200-204. DOI: 10.1109/CBMS.2017.94

• Knowledge and active research of the current state of the art on deep learning.
• Advance skill on Python building data pipelines, machine learning models, web servers and open software development.
• Experience using Rust for simulation, CLI applications, backend servers, web scrapping, WebAssembly, Python bindings and high-performance use cases like data pipelines.
• Experience building data visualization and interactive dashboards using Python and Typescript (Svelte, React, D3, Threejs).
• Main programming languages: Python, Rust, Julia and JS/Typescript.
• Additional skills: message queues (Kafka, RabbitMQ, ZeroMQ), spatial data (PostGIS).

• Spanish (native)
• English (fluent)