Hi there! I’m José Ignacio Orlando, but everyone call me Nacho :)
I’m an Associate Researcher at CONICET, working as part of Yatiris lab at Pladema Institute in Tandil, Argentina. Furthermore, I’m Director of AI Labs at Arionkoder, as part of a CONICET STAN. You can learn more about what we do in Arionkoder in this link. Last but not least, I’m a part-time professor at Facultad de Ciencias Exactas in UNICEN, Tandil, Argentina, where I teach Fundamentals of Data Science and a Workshop of Applied Mathematics.
My research interests include machine learning and computer vision techniques for medical imaging applications, mostly centered in ophthalmology. But I’m also interested in other applications of machine learning, such as natural language processing. I’m always open to new collaborations and projects, so feel free to reach out to me if you think we could work together!
Apart from PLADEMA (where I did my PhD from 2013 to 2017), my previous affiliations include the Center for Learning and Visual Computing in Paris, France (2013, 6 months working as an intern funded by INRIA), ESAT-PSI in KU Leuven, Belgium (2016, 1 month working as a visiting scholar) and the Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA) from the Medical University of Vienna, Austria (2018-2019, almost 2 years working as a postdoctoral researcher).
PhD in Computational and Industrial Mathematics, 2017
UNICEN
Software Engineer, 2013
UNICEN
This paper presents a computational tool for guiding intracranial aneurysm treatment by aligning preoperative simulation data with real-time X-Ray imaging, enabling more accurate flow diverter deployment. The method was validated on patient data, showing its potential to improve decision-making during procedures by visualizing the device’s current and planned positions.
The paper evaluates domain generalization strategies for optic disc and cup segmentation in fundus images, highlighting issues with existing methods when applied to uncropped images, and proposes a semi-supervised learning approach based on the Noisy Student framework to improve performance across diverse datasets.
The paper presents a novel method for learning normal asymmetry patterns in brain structures. It accurately characterizes normal asymmetries and detects pathological alterations without relying on diseased cases for training. The approach shows promise in improving the identification of neurodegenerative conditions..
Although I try to keep this list updated, you better check my Google Scholar profile for a full list
These are all the courses in which I worked / I’m working on, either as a Teaching Assistant or as a Professor in charge.
Professor in:
Professor in:
Teaching Assistant in:
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Students and Researchers I’ve supervised
Advisor: José Ignacio Orlando.
Topic: Web scraping of images with anomaly detection based filtering.
Advisor: José Ignacio Orlando.
Topic: Implantable collamer lens sizing with machine learning models
Advisor: José Ignacio Orlando. Co-advisor: Ignacio Larrabide
Topic: Deep learning tools for assisting screening of retinal diseases
Advisor: Ignacio Larrabide. Co-advisor: José Ignacio Orlando.
Topic: Machine learning methods for biomedical signal analysis
Advisor: Ignacio Larrabide. Co-advisor: José Ignacio Orlando.
Topic: Characterization of normal asymmetries in homologous brain structures
Advisor: José Ignacio Orlando. Programa de Fortalecimiento a la Ciencia y la Tecnología en las Universidades Nacionales. SECAT, UNICEN.
Topic: Deep learning algorithms for computer assisted diagnostic of diabetic retinopathy from fundus pictures.
Advisor: Ignacio Larrabide. Co-advisor: José Ignacio Orlando. Consejo Interuniversitario Nacional (CIN).
Topic: DeepBrain: Machine Learning applied to study brain morphological alterations in MRI scans.
Advisors: José Ignacio Orlando, Carlos A. Bulant. Facultad de Ciencias Exactas, UNICEN.
Topic: Artery/Vein classification from color fundus photographs for blood flow simulations.
Advisors: José Ignacio Orlando, Ignacio Larrabide. Facultad de Ciencias Exactas, UNICEN.
Topic: Stereoscopic camera simulation using neural networks.
Advisor: Ignacio Larrabide. Co-advisor: José Ignacio Orlando.
Topic: Unpaired generative adversarial models for realistic abdominal ultrasound simulation.
Advisors: José Ignacio Orlando, Mariana del Fresno. Facultad de Ciencias Exactas, UNICEN.
Topic: Retinal blood vessel segmentation in ultra-wise field of view angiographies. Finished with grade 10/10.
Advisors: José Ignacio Orlando, Mariana del Fresno. Facultad de Ciencias Exactas, UNICEN.
Topic: Feature engineering for retinal blood vessel segmentation in fundus images. Finished with grade 10/10.
Advisors: José Ignacio Orlando, Mariana del Fresno. Facultad de Ciencias Exactas, UNICEN.
Topic: Integrating fuzzy C-means and deformable models for 3D medical image segmentation. Finished with grade 10/10.
General coordination and supervision of all the AI initiatives of the company.
Responsibilities include:
Research project: retinAR: computing and discovering retinal disease biomarkers from fundus images using deep learning
Responsibilities include:
Research project: Deep learning for retinal OCT image analysis
Responsibilities include:
Research project: Red lesion detection in fundus photographs
Responsibilities include:
Research project: Retinal image analysis with machine learning. Supervisor: Prof. Matthew B. Blaschko (INRIA)
Responsibilities include:
Research project: Machine learning for ophthalmic screening and diagnostics from fundus images
Supervisors: Prof. Matthew B. Blaschko (KU Leuven) & Prof. Mariana del Fresno (UNICEN)