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Research interests

 

I work in computational neuroscience and machine learning, bringing in tools from complex systems, information theory and statistics, to try to understand how the brain acquires, stores, and processes information. To do so, I employ both analytic methods and numerical simulations/optimization.

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One of my main lines of work focuses on understanding how the brain encodes and deals with uncertainty, training biologically plausible recurrent neural networks to perform sampling-based probabilistic inference. I apply these tools to the study of perception in neurotypical subjects and in autism spectrum disorders.

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I also work in the field of algorithmic fairness, where the goal is to audit biases in ML systems in terms of protected attributes, understand the origin of these biases and how to mitigate them. We focus on ML systems for medical images and biomedical signals.

Sampling-based representation of uncertainty in the context of Bayesian inference. 

Dr. Rodrigo Echeveste

Adjunct Researcher (CONICET)
Adjunct Professor (FICH, UNL)
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sinc(i)
CONICET/UNL
Santa Fe, Argentina
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Email:

recheveste(at)sinc.unl.edu.ar

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Address:

Ciudad Universitaria UNL,

Ruta Nacional Nº 168, km 472.4,
FICH, 4to Piso (3000) Santa Fe – Argentina

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Curriculum Vitae

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PUBLICATIONS
PUBLICATIONS
Journals

  • Mansilla, L., Claucich, E., Echeveste, R., Milone, D.H., & Ferrante, E. Demographically-Informed Prediction Discrepancy Index: Early Warnings of Demographic Biases for Unlabeled Populations. TMLR (2024). [link]

  • Ricci Lara, M.A., Echeveste, R., & Ferrante, E. Addressing Fairness in Artificial Intelligence for Medical Imaging. Nature Communications, https://doi.org/10.1038/s41467-022-32186-3 (2022)[link]

  • Echeveste, R., Ferrante, E., Milone, H.D, & Samengo, I. Bridging physiological and perceptual views of
    autism by means of sampling-based Bayesian inference. Network Neuroscience, https://doi.org/10.1162/netn_a_00219 (2022) [link]

  • Echeveste, R., Aitchison, L., Hennequin, G. et al. Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nature Neuroscience (2020). https://doi.org/10.1038/s41593-020-0671-1 [link]

  • Tourigny, D.S., Karim, M.K.A, Echeveste, R., Kotter, M.R.N., & O'Neill, J.S. Energetic substrate availability regulates synchronous activity in an excitatory neural network. PLOS ONE (2019), 14(8): e0220937. doi: 10.1371/journal.pone.0220937.​ [link​]

  • Fonseca, M., Vattuone, N., Clavero, F., Echeveste, R., & Samengo, I. The subjective metric of remembered colors: An information-theoretical analysis of the geometry of human chromatic memory. PLOS ONE (2019), 14(1), e0207992. doi:10.1371/journal.pone.0207992 [link]

  • Echeveste, R., & Lengyel, M. The redemption of noise: inference with neural populations. (invited commentary on Ma et al. Nature Neuroscience 9:1432-1438, 2006). Trends in Neurosciences  (2018), 41(11), 767-770. doi:10.1016/j.tins.2018.09.003 [link]

  • Trapp, P., Echeveste, R., & Gros, C. E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks. Scientific Reports (2018), 8(1), 8939. doi:10.1038/s41598-018-27099-5 [link]

  • Echeveste, R., Eckmann, S., & Gros, C. Drifting states and synchronization induced chaos in autonomous networks of excitable neurons. Frontiers in Computational Neuroscience (2016), 10:98. doi: 10.3389/fncom.2016.00098 [link]

  • Echeveste, R., Eckmann, S., & Gros, C. The Fisher Information as a Neural Guiding Principle for Independent Component Analysis. Entropy (2015), 17(6), 3838-3856; doi:10.3390/e17063838. [link]

  • Echeveste, R., & Gros, C. Two-trace model for spike-timing dependent synaptic plasticity. Neural Computation (2015), 27 (3), 672-698. doi:10.1162/NECO_a_00707 [link]

  • Echeveste, R., & Gros, C. Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules. Frontiers in Robotics and AI (2014), 1:1. doi: 10.3389/frobt.2014.00001 [link]

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Proceedings

  • Ricci Lara M.A., Mosquera C., Ferrante E., & Echeveste R. Towards unraveling calibration biases in medical image analysis. The MICCAI 2023 Workshop on Fairness of AI in Medical Imaging (2023) [link]

  • Gaggion, N., Echeveste, R., Mansilla, L., Milone, D.H., & Ferrante, E. Unsupervised bias discovery in medical image segmentation. The MICCAI 2023 Workshop on Fairness of AI in Medical Imaging (2023) [link]

  • Mansilla, L., Echeveste, R., Milone, H.D., & Ferrante, E. Domain Generalization via Gradient Surgery. Proceedings of the IEEE/CVF International Conference on Computer Vision. (2021) [link]

  • Echeveste, R., & Gros, C. An objective function for self-limiting neural plasticity rules. ESANN 2015 Proceedings (2015), ISBN 978-287587014-8. [link]

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Preprints

  • Ricci Lara M.A., Mosquera C., Ferrante E., & Echeveste R. Towards unraveling calibration biases in medical image analysis (arXiv, 2023) [link]

  • Echeveste, R., Hennequin, G., & Lengyel, M. Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model (arXiv, 2017) [link]

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EDUCATION
RESEARCH EXPERIENCE
2019-present

Researcher

sinc(i), CONICET/UNL, Santa Fe, Argentina

Research foci: Artificial Neural Networks, Development of Machine Learning tools for Computational Neuroscience, Bayesian Inference, Fairness in AI, ASD

2016-2019

Research Associate

CBL, University of Cambridge, UK

Supervised by Prof. Dr. Máté Lengyel and cosupervised by  Dr. Guillaume Hennequin.

Research focus: Training biologically plausible Recurrent Neural Networks to perform Probabilistic Inference.

2012-2016

Research Assistant

ITP, Goethe University, Frankfurt, Germany

Supervised by Prof. Dr. Claudius Gros.

Research focus: Development and application of Synaptic Plasticity Rules, both from generating principles (top-down) and from its biophysical components (bottom-up). 

EDUCATION
CONTACT
2012-2016

Phd

PHD IN PHYSICS

Goethe University, Frankfurt, Germany

Supervisor: Prof. Dr. Claudius Gros

During my thesis I worked within the fields of complex systems and computational neuroscience, developing synaptic plasticity rules, both from generating principles (top-down) and from its biophysical components (bottom-up). 

2010-2011

Master's Degree

MSC IN PHYSICAL SCIENCES

Balseiro Institute, Bariloche, Argentina

Msc. Advisor: Dr. Inés Samengo

During my masters I worked in cognitive science and computational neuroscience, studying categorization in children with autism. I carried out experiments in schools and centers across Argentina. 

2005-2010

Bachelor's Degree

BSC IN PHYSICS

National University of Rosario,

and Balseiro Institute, Bariloche, Argentina

Bsc. Advisor: Dr. Inés Samengo

As a part of my thesis I developed a computer software, in the form of a videogame, to produce a quantitative assessment of the categorization capacity of children with autism.

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