Computer scientist / Buenos Aires
AI, computer graphics
& medical imaging.
CONICET & Universidad Torcuato Di Tella
I am a Computer Scientist at the National Scientific and Technical Research Council (CONICET) and an Assistant Professor at the Universidad Torcuato Di Tella in Buenos Aires. I completed my Ph.D. in Computer Graphics at the GRAPHDECO team, INRIA Sophia Antipolis, under the supervision of Adrien Bousseau and George Drettakis. After graduating, I was a postdoctoral researcher in Bernd Bickel's group at IST Austria. I also hold an Engineering degree from UNICEN University (2012). My research bridges AI, computer graphics, and medical imaging, focusing on generative models for 2D/3D content manipulation.
I participated in the LAC Connections Symposium 2026, organized by the National Academies of Sciences, Engineering, and Medicine.
I received the Premio a la Excelencia en Investigación (Research Excellence Award) from Universidad Torcuato Di Tella.
I chaired the RIVA Cervical Cytology Challenge at IEEE ISBI 2026 in London, UK.
Our paper has been published in Scientific Data (Nature) 2025. More info here.
I co-chaired the LatinX in AI Workshop at NeurIPS 2025, San Diego, CA.
I was invited to the CZI Open Science 2025 Meeting in San Diego, CA, where I participated as a speaker in the panel AI and Open Science.
Our paper VesselGPT: Autoregressive Modeling of Vascular Geometry has been accepted to MICCAI 2025. More info here.
Our paper Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling has been accepted to Medical Image Analysis (Elsevier) 2025. More info here.
Our paper Enhancing and Advancements in Deep Learning for Melanoma Detection: A Comprehensive Review has been published in Computers in Biology & Medicine 2025. More info here.
Our paper Improving Realism in Abdominal Ultrasound Simulation Combining a Segmentation-Guided Loss and Polar Coordinates Training has been accepted to Medical Physics 2025. More info here.
I was selected to attend Khipu 2025, the premier Latin-American Machine Learning and AI conference. More info here.
I participated in the workshop Exploring Opportunities for Large Language Models in Public Discourse held at UC Berkeley. More info here.
Our project VisDecode: AI-Driven Interpretation and Enhancement of Scientific Plots has been awarded a $69,960 grant from the Alfred P. Sloan Foundation's Trust in AI initiative as part of the Sloan–CZI "Pathways to AI-Enabled Research" program. More info here.
I participated in the CZI Open Science 2024 Meeting held in Boston. More info here.
I participated in the workshop Open Science Dynamic Convergence Workshop held in Washington, DC. More info here.
Our paper DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling has been accepted to CVPR 2024. More info here.
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We introduce RIVA, a public image dataset of conventional Pap smear cytology annotated independently by multiple experts. Designed to support the development and benchmarking of machine-learning models for cervical cancer screening, it captures inter-observer variability across a diverse set of cases [...]
Inspired by large-language-model advances, we encode vascular trees with a VQ-VAE and generate them autoregressively using a GPT-2 backbone. The model captures complex branching and geometry, enabling realistic synthesis of anatomical trees for simulation and planning […]
Anatomical trees play a pivotal role in clinical diagnosis and treatment planning, yet their intricate topology makes them hard to model. We introduce a recursive variational neural network that learns a compact manifold capturing branch connectivity and geometry, enabling realistic vascular synthesis […]
This systematic review surveys recent deep-learning approaches to melanoma detection, highlighting trends, replication issues, and generalization gaps. We analyze datasets, evaluation protocols, and state-of-the-art architectures to outline future directions for reliable skin-cancer screening […]
We present a framework that fuses physics-based ultrasound simulation with GAN-based image translation, guided by segmentation loss and polar-coordinate training. The method reduces hallucinations and enhances anatomical fidelity, yielding realistic abdominal US images for medical training […]
In recent years, there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However, UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients, generally resulting in [...]
We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. Existing model-based methods provide some degree of control and variation in the structures produced [...]
Although normal homologous brain structures are approximately symmetrical by definition, they also have shape differences due to e.g. natural ageing. On the other hand, neurodegenerative conditions induce their own changes in this asymmetry, making them more pronounced [...]
This study aims to provide a reliable method for the generation of realistic bone microstructure, serving for the training of neural networks and the development of new diagnostic parameters of bone architecture and mineralization. In a first step, we trained a volumetric generative model in a progressive manner to create patches of realistic bone [...]
Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer's Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex [...]
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time [...]
Finding point-wise correspondences between images is a long-standing problem in computer vision. Corresponding sketch images is particularly challenging due to the varying nature of human style, projection distortions and viewport changes. In this paper we present a feature descriptor targeting line drawings [...]
Research in vertebral bone micro-structure generally requires costly procedures to obtain physical scans of real bone with a pathology under study, since no methods are available yet to generate realistic bone structures in-silico. Here we propose to apply recent advances in generative adversarial networks (GANs) to develop such a method. We adapted style-transfer techniques [...]
In this paper we propose to apply generative adversarial neural networks trained with a cycle-consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from Computed Tomography (CT) scans. A ray-casting US simulation approach is used to generate intermediate synthetic images [...]
We propose FlexMaps, a novel framework for fabricating smooth shapes out of flat, flexible panels with tailored mechanical properties. We start by mapping the 3D surface onto a 2D domain as in traditional UV mapping to design a set of deformable flat panels called FlexMaps. For these panels [...]
Molding is a popular mass production method, in which the initial expenses for the mold are offset by the low per-unit production cost. However, the physical fabrication constraints of the molding technique commonly restrict the shape of moldable objects. For a complex shape, a decomposition of the object into moldable parts is a common strategy [...]
Wire wrapping is a traditional form of handmade jewelry that involves bending metal wire to create intricate shapes. The technique appeals to novices and casual crafters because of its low cost, accessibility and unique aesthetic. We present a computational design tool that addresses the two main challenges of creating 2D wire wrapped [...]
Designers frequently draw curvature lines to convey bending of smooth surfaces in concept sketches. We present a method to extrapolate curvature lines in a rough concept sketch, recovering the intended 3D curvature field and surface normal at each pixel of the sketch. This 3D information allows us to enrich the sketch with 3D-looking shading [...]
We present an interactive drawing tool that provides automated guidance over model photographs to help people practice traditional drawing-by-observation techniques. The drawing literature describes a number of techniques to support this task and help people gain consciousness of the shapes in a scene and their relationships. We compile these techniques and derive a set of construction lines that we automatically extract from a model [...]
Drawing is the earliest form of visual depiction. The goal of this thesis is to facilitate and accelerate drawing for amateurs as well as for expert designers and illustrators, employing computer graphics, image processing and interaction techniques. As this is a broad spectrum to tackle, we identify three specific problems related to drawing and propose computer tools to help users overcome the main challenges on each domain [...]
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