PTZJEPA: Predictive Active Vision for Autonomous Visual Data Collection
Published:
A closed-loop active perception framework that combines self-supervised predictive world models with reinforcement-learning control of pan-tilt-zoom (PTZ) cameras.
Posts by Collection
portfolio
Super Computing 2022
Toward Self-Supervised Avian Diversity Monitoring
projects
PTZJEPA: Predictive Active Vision for Autonomous Visual Data Collection
Published:
A closed-loop active perception framework that combines self-supervised predictive world models with reinforcement-learning control of pan-tilt-zoom (PTZ) cameras.
publications
Phonetic acquisition in cortical dynamics, a computational approach
Published in Plos one, 2019
This paper is about brain inspired models for phonetic acquisition in cortical dynamics.
Recommended citation: Dario Dematties, et al. (2019). "Phonetic acquisition in cortical dynamics, a computational approach." PLoS ONE. 14(6). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217966
Towards High-End Scalability on Biologically-Inspired Computational Models
Published in Advances in Parallel Computing, 2020
This paper is about scalability on biologically-inspired computational models.
Recommended citation: Dario Dematties, et al. (2020). "Towards High-End Scalability on Biologically-Inspired Computational Models." Advances in Parallel Computing. 36. https://ebooks.iospress.nl/doi/10.3233/APC200077
Neural Network Approach for Dielectric Characterization of Tissues in Microwave Frequencies using Coplanar Waveguide Transmission
Published in 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020
This paper is about a neural network approach for dielectric characterization of tissues in microwave frequencies.
Recommended citation: Viktor Mattsson, et al. (2020). "Neural Network Approach for Dielectric Characterization of Tissues in Microwave Frequencies using Coplanar Waveguide Transmission." 2020 14th European Conference on Antennas and Propagation (EuCAP). https://ieeexplore.ieee.org/abstract/document/9135949
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
Published in Frontiers in Neural Circuits, 2020
This paper is about brain inspired models for the emergence of grammatical categories in cortical dynamics.
Recommended citation: Dario Dematties, et al. (2020). "A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics." Frontiers in Neural Circuits. 14(12). https://www.frontiersin.org/articles/10.3389/fncir.2020.00012/full
Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network
Published in ACS nano, 2021
This paper is about deep learning for nanopore sensing signals.
Recommended citation: Dario Dematties, et al. (2021). "Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network." ACS Nano. 15(9). https://pubs.acs.org/doi/full/10.1021/acsnano.1c03842
A guide to signal processing algorithms for nanopore sensors
Published in ACS sensors, 2021
This paper is a review that starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information.
Recommended citation: Chenyu Wen, et al. (2021). "A guide to signal processing algorithms for nanopore sensors." ACS sensors. 6(10). https://pubs.acs.org/doi/pdf/10.1021/acssensors.1c01618
Neural correlates of semantic integration in Spanish verb-noun compounds: an ERP study
Published in Language, Cognition and Neuroscience, 2022
This paper is about Neural correlates of semantic integration in Spanish verb-noun compounds.
Recommended citation: Mercedes Güemes, et al. (2022). "Neural correlates of semantic integration in Spanish verb-noun compounds: an ERP study." Language, Cognition and Neuroscience. 37(3). https://www.tandfonline.com/doi/full/10.1080/23273798.2021.1993283
A generalized transformer-based pulse detection algorithm
Published in ACS sensors, 2022
This paper is about a generalized transformer-based pulse detection algorithm.
Recommended citation: Dario Dematties, et al. (2022). "A generalized transformer-based pulse detection algorithm." ACS sensors. 7(9). https://pubs.acs.org/doi/full/10.1021/acssensors.2c01218
Towards an Active Foveated Approach to Computer Vision
Published in Computación y Sistemas, 2022
This paper aims to advance new CV approaches more in line with those found in biological agents to bring novel solutions to the main problems faced by current CV applications.
Recommended citation: Dario Dematties, et al. (2022). "Towards an Active Foveated Approach to Computer Vision." Computación y Sistemas. 26(4). https://www.scielo.org.mx/scielo.php?pid=S1405-55462022000401635&script=sci_arttext
A Self-supervised Approach for Cloud Image Analysis
Published in 103rd AMS Annual Meeting, 2023
This paper is about self-supervised learning for cloud image analysis.
Recommended citation: Bhupendra A Raut, et al. (2023). "A Self-supervised Approach for Cloud Image Analysis." 103rd AMS Annual Meeting. https://ams.confex.com/ams/103ANNUAL/mediafile/Manuscript/Paper413343/AMS2023_Abstract-413343.pdf
Optimizing cloud motion estimation on the edge with phase correlation and optical flow
Published in Atmospheric Measurement Techniques, 2023
This paper aims at optimizing cloud motion estimation.
Recommended citation: Bhupendra A Raut, et al. (2023). "Optimizing cloud motion estimation on the edge with phase correlation and optical flow." Atmospheric Measurement Techniques. 16(5). https://amt.copernicus.org/articles/16/1195/2023/
Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis
Published in Artificial Intelligence for the Earth Systems, 2023
This paper applies self-supervised learning for full-sky image analysis.
Recommended citation: Dario Dematties, et al. (2023). "Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis." Artificial Intelligence for the Earth Systems. 2(2). https://journals.ametsoc.org/view/journals/aies/2/2/AIES-D-22-0063.1.xml
ARMing the Edge: Designing Edge Computing-capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing
Published in Artificial Intelligence for the Earth Systems, 2023
This paper aims for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed.
Recommended citation: Robert C. Jackson, et al. (2023). "ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing." Artificial Intelligence for the Earth Systems. https://journals.ametsoc.org/view/journals/aies/2/4/AIES-D-22-0062.1.xml
Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices
Published in IEEE WF-IoT, 2023
This paper demonstrates that the model weights shared in FL can expose revealing information about the local data distributions of IoT devices.
Recommended citation: Samir Rajani, et al. (2023). "Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices." IEEE WF-IoT. https://arxiv.org/abs/2308.14658
Doppler spectra from ARM Doppler Lidar during ARMing the Edge
Published in Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Archive; Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center, 2023
This paper aims for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed.
Recommended citation: Bhupendra Raut, et al. (2023). "Doppler spectra from ARM Doppler Lidar during ARMing the Edge." Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Archive; Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center. https://www.osti.gov/biblio/2204486
Containerization on a self-supervised active foveated approach to computer vision
Published in Revista Colombiana de Computación, 2024
This paper discusses containerization for a self-supervised active foveated computer vision approach.
Recommended citation: Dario Dematties, Silvio Rizzi, and George Thiruvathukal. (2024). "Containerization on a self-supervised active foveated approach to computer vision." Revista Colombiana de Computación. 25, 29-38.
Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring
Published in Ecological Informatics, 2024
This paper applies self-supervised learning for full-sky image analysis.
Recommended citation: Dario Dematties, et al. (2024). "Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring." Ecological Informatics. 83. https://www.sciencedirect.com/science/article/pii/S1574954124003650
Perspective on self-supervised transformers for scalable data analysis, retrievals, and discovery in geosciences
Published in Artificial Intelligence for the Earth Systems, 2026
This perspective discusses self-supervised transformers for scalable geoscience data analysis, retrievals, and discovery.
Recommended citation: Bhupendra A. Raut, Siddhant Gupta, Robert C. Jackson, Dié Wang, Adam Theisen, Jingjing Tian, Seongha Park, Joseph R. O’Brien, Maxwell A. Grover, Troy Arcomano, Nicki L. Hickmon, Scott M. Collis, Nicola J. Ferrier, and Dario J. Dematties. (2026). "Perspective on self-supervised transformers for scalable data analysis, retrievals, and discovery in geosciences." Artificial Intelligence for the Earth Systems. e250069.
A semi-supervised learning method for the identification of bad exposures in large imaging surveys
Published in Open Journal of Astrophysics, 2026
This paper presents a semi-supervised learning method for identifying bad exposures in large astronomical imaging surveys.
Recommended citation: Yufeng Luo, Adam D. Myers, Alex Drlica-Wagner, Dario Dematties, Salma Borchani, Frank Valdes, Arjun Dey, David Schlegel, Rongpu Zhou, and DESI Legacy Imaging Surveys Team. (2026). "A semi-supervised learning method for the identification of bad exposures in large imaging surveys." Open Journal of Astrophysics. 9.
talks
Self‑Supervised Learning for Automated Species Detection from Passively Recorded Soundscapes in Avian Diversity Monitoring
Published:
Research Posters Display. Self‑supervised Learning through Vision Transformers applied to Edge Computing Label Scarce environments. Self‑supervised Learning for Avian diversity monitoring.
Self‑Supervised Learning at the Edge
Published:
Self‑supervised Learning through Vision Transformers applied to Edge Computing Label Scarce environments. Self‑supervised Learning for Full‑sky cloud image characterization and Avian diversity monitoring.
Towards Self-Supervised Learning @ the Edge
Published:
In the realm of deploying machine learning (ML) algorithms on edge devices, a plethora of challenges surfaces, ranging from compute resource limitations to the nuanced intricacies associated with acquiring dynamic data distributions. The majority of extant ML algorithms implemented on edge cyberinfrastructures undergo training with datasets that inadequately capture the true essence of the data streams collected at the edge. This presentation delves into an investigation of the viability of novel self-supervised learning algorithms to effectively characterize insufficiently curated, imbalanced, and unlabeled datasets in the context of edge computing. The primary focus is directed towards their potential applicability in addressing the unique challenges posed by edge environments. Two specific applications will be presented in this talk: Atmospheric Conditions Characterizations and Ecosystem Activity Monitoring. Both instances exemplify the autonomous characterization capabilities facilitated by the proposed self-supervised learning algorithms, obviating the need for human intervention in the process. Through these case studies, insights into the transformative potential of self-supervised learning in mitigating the challenges associated with deploying ML algorithms on edge devices will be elucidated.
teaching
Mathematical Analysis II
Undergraduate course, Universidad Tecnológica Nacional (UTN), 2013
University First Assistant.
Signals and Systems Analysis
Undergraduate course, Universidad Tecnológica Nacional (UTN), 2013
University First Assistant.
Mathematical Analysis II
Undergraduate course, Juán Agustín Maza University, 2013
University teacher: Assistant in charge of assignments.
Linguistics and Experimental Neurobiology of Language
Graduate course, INCIHUSA CONICET Mendoza Technological Scientific Center, 2015
Covered topics: Memory Prediction Framework, Hierarchical Temporal Memory, Modeling children’s early grammatical knowledge with Bayesian Processes.
Linguistics and Experimental Neurobiology of Language
Graduate course, INCIHUSA CONICET Mendoza Technological Scientific Center, 2016
Covered topics: Hierarchical Temporal Memory, Cortical Learning Algorithm, Sparse Distributed Representations, Semantic Folding Theory.