About me

Hi! this is Renato Hermoza. I have PhD on Machine Learning from the University of Adelaide, my work focused on applying machine learning to medical images, survival prediction and weakly supervised localization. My main research interests are in the fields of machine learning, optimization, simulation and data visualization. I enjoy building interactive systems for domain experts to explore complex machine learning and optimization systems.

Would you like to talk about Python, Rust adoption, machine learning? You can schedule 30 min talk here or send me a mail to rhermoza145@gmail.com.

You can find my CV here.

News

  • July 2022: Started my role as Chief Scientist in Kashin.
  • June 2022: Phd thesis submitted :)
  • November 2018: Started Phd at the University of Adelaide.
  • July 2018: Finished MS in computer science at the Pontificia Universidad Catolica del Perú.

Publications

  • 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

Presentations

  • Oral Presentation at the 23rd International Conference On Medical Image Computing & Computer Assisted Intervention (MICCAI 2020) - Perú, Pontifical Catholic University of Perú.
  • Workshop: "AI and ML fundamentals for clinicians" on the 16th International Symposium on Medical Information Processing and Analysis (SIPAIM 2020) - Perú, Pontifical Catholic University of Perú.
  • Deep Learning Mini-Course. Presented at Workshop on Artificial Intelligence and Machine Learning Applications 2017 - Perú, Pontifical Catholic University of Perú.
  • Poster presentation at the 30th ΙΕΕΕ International Symposium on Computer-Based Medical Systems (CBMS 2017) - Greece, Aristotle University of Thessaloniki.