Diego Patiño

diego.patino@uta.edu
Department of Computer Science and Engineering
University of Texas - Arlington
Arlington, TX

I am an assistant professor in the Computer Science and Engineering department at the University of Texas - Arlington (UTA). Before my current appointment at UTA, I worked as a Postdoctoral Fellow in the Department of Electrical and Computer Engineering at Drexel University, working with Professor David K. Han. Before joining Drexel, I was a Post-Doctoral Researcher in the GRASP Laboratory at the University of Pennsylvania, working under the supervision of Professor Kostas Daniilidis. My research interests revolve around machine learning and geometric approaches to computer vision with applications in robotics. My research focuses on 3D vision, symmetry detection, 3D Reconstruction, graph neural networks, robot perception, and reinforcement learning.

I received my B.S., M.S., and Ph.D. degrees in Computer Engineering from the National University of Colombia, in 2010, 2012, and 2020 respectively. I was a visiting researcher at the University of Wisconsin-Madison and later at the University of Pennsylvania.

I am hiring PhD students. If you think you are a fit, please email “diego.patino at uta.edu” with the exact subject: prospective phd student. In the email, please include a CV and a document with a few paragraphs describing your skills and expertise in 3D computer vision and machine learning. Additional documents, such as transcripts, GRE, english proficiency, etc., are also helpful. If you follow these instructions, I will eventually review the documents and get back to you. Otherwise, I will miss the email.

news

Apr 2024	In Sept. 2024, I will start a position as an assistant professor in the Computer Science and Engineering Deparment at the University of Texas - Arlington.
Oct 2023	As of Nov. 2023, I will be a Postdoctoral Fellow in the Department of Electrical and Computer Engineering at Drexel University, working with professor David K. Han.
Aug 2023	I will be presenting my work “Learning to navigate in turbulent winds with Graph Neural Networks” at IROS 2023. If you are there, please come by.
May 2023	Our RA-L paper on Learning to navigate in turbulent winds with Graph Neural Networks is out. Check it out here.
Mar 2022	Our paper, “Level Set Mesher: Single-image to 3D reconstruction by following the level sets of the signed distance function” was accepted at ICPR’22!
Nov 2021	Good news! Our short paper, “Self-supervised implicit shape reconstruction and pose estimation for predicting the future” was accepted at the ICRA’22 Workshop on Implicit Neural Geometry!

selected publications

Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach

Diego Patiño, Siddharth Mayya, Juan Calderon, and 2 more authors

IEEE Robotics and Automation Letters 2023

Bib HTML PDF Code

@article{Patino2023_turbulence,
  author = {Patiño, Diego and Mayya, Siddharth and Calderon, Juan and Daniilidis, Kostas and Saldaña, David},
  journal = {IEEE Robotics and Automation Letters},
  title = {Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach},
  year = {2023},
  volume = {},
  number = {},
  pages = {1-8},
  doi = {10.1109/LRA.2023.3280806},
}

Level Set Mesher: Single-image to 3D reconstruction by following the level sets of the signed distance function

Diego Patiño, Carlos Esteves, and Kostas Daniilidis

In 26th International Conference on Pattern Recognition - ICPR 2022

Bib HTML PDF

@inproceedings{Patino2022_levelset_mesher,
  author = {Patiño, Diego and Esteves, Carlos and Daniilidis, Kostas},
  title = {Level Set Mesher: Single-image to 3D reconstruction by following the level sets of the signed distance function},
  pages = {161--174},
  year = {2022},
  doi = {},
  booktitle = {26th International Conference on Pattern Recognition -  ICPR},
  %project_page = {},
}

Self-supervised implicit shape reconstruction and pose estimation for predicting the future

Diego Patiño, Karl Schmeckpeper, Hita Gupta, and 2 more authors

In ICRA 2022 - Workshop on Motion Planning with Implicit Neural Representations of Geometry 2022

Bib HTML PDF

@inproceedings{Patino2022_implicit_pose_shape,
  author = {Patiño, Diego and Schmeckpeper, Karl and Gupta, Hita and Georgakis, Georgios and Daniilidis, Kostas},
  title = {Self-supervised implicit shape reconstruction and pose estimation for predicting the future},
  year = {2022},
  maintitle = {IEEE International Conference on Robotics and Automation},
  booktitle = {ICRA 2022 - Workshop on Motion Planning with Implicit Neural Representations of Geometry},
  url = {https://neural-implicit-workshop.stanford.edu/#accepted-papers},
}

Cosine-Pruned Medial Axis: A New Method for Isometric Equivariant and Noise-Free Medial Axis Extraction

Diego Patiño, and John W. Branch

IEEE Access 2021

Bib HTML Project page PDF Code

@article{Patino2021_cpma,
  author = {Patiño, Diego and Branch, John W.},
  journal = {IEEE Access},
  title = {Cosine-Pruned Medial Axis: A New Method for Isometric Equivariant and Noise-Free Medial Axis Extraction},
  year = {2021},
  volume = {9},
  number = {},
  pages = {65466-65481},
  doi = {10.1109/ACCESS.2021.3072},
  project_page = {cpma},
}

Melanoma detection on dermoscopic images using superpixels segmentation and shape-based features

Diego Patiño, Alberto M. Ceballos-Arroyo, Jairo A. Rodriguez-Rodriguez, and 2 more authors

In 15th International Symposium on Medical Information Processing and Analysis 2020

Bib PDF Code

@inproceedings{Patino2020_melanoma_classification,
  author = {Patiño, Diego and Ceballos-Arroyo, Alberto M. and Rodriguez-Rodriguez, Jairo A. and Sanchez-Torres, German and Branch-Bedoya, John W.},
  title = {{Melanoma detection on dermoscopic images using  superpixels segmentation and shape-based features}},
  volume = {11330},
  booktitle = {15th International Symposium on Medical Information Processing and Analysis},
  editor = {Romero, Eduardo and Lepore, Natasha and Brieva, Jorge},
  organization = {International Society for Optics and Photonics},
  publisher = {SPIE},
  pages = {1133018},
  keywords = {Superpixel, Melanoma, Asymmetry, skin lesion classification, SVM, Linear regression, Neural Network, color texture},
  year = {2020},
  doi = {10.1117/12.2545300},
  url = {https://doi.org/10.1117/12.2545300},
}

Automatic Skin Lesion Segmentation on Dermoscopic Images by the Means of Superpixel Merging

Diego Patiño, Jonathan Avendaño, and John W. Branch

In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 2018

Bib HTML PDF Code

@inproceedings{Patino2018_skin_superpixels,
  author = {Patiño, Diego and Avendaño, Jonathan and Branch, John W.},
  editor = {Frangi, Alejandro F. and Schnabel, Julia A. and Davatzikos, Christos and Alberola-López, Carlos and Fichtinger, Gabor},
  title = {Automatic Skin Lesion Segmentation on Dermoscopic Images by the Means of Superpixel Merging},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018},
  year = {2018},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {728--736},
  isbn = {978-3-030-00937-3},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-00937-3_83},
}

Computational Detection of Salient Information to Identify High Stress and Ambiguity Regions in Digital Photoelasticity Images

Juan C. Briñez León, Diego Patiño, Alejandro Restrepo M, and 1 more author

In Imaging and Applied Optics 2017 (3D, AIO, COSI, IS, MATH, pcAOP) 2017

Bib HTML PDF

@inproceedings{BrinezdeLeon2017_photoelasticity,
  author = {de León, Juan C. Briñez and Patiño, Diego and M, Alejandro Restrepo and Branch, John W.},
  booktitle = {Imaging and Applied Optics 2017 (3D, AIO, COSI, IS, MATH, pcAOP)},
  journal = {Imaging and Applied Optics 2017 (3D, AIO, COSI, IS, MATH, pcAOP)},
  keywords = {Image analysis; Birefringence; Algorithms ; Circular polarization; Digital imaging; Feature extraction; Light intensity; Light wavelength; Strain},
  pages = {IM4E.2},
  publisher = {Optica Publishing Group},
  title = {Computational Detection of Salient Information to Identify High Stress and Ambiguity Regions in Digital Photoelasticity Images},
  year = {2017},
  doi = {10.1364/ISA.2017.IM4E.2},
}

A clone-free, single molecule map of the domestic cow (Bos taurus) genome

Shiguo Zhou, Steve Goldstein, Michael Place, and 9 more authors

BMC Genomics 2015

Bib HTML PDF

@article{Zhou2015_genome,
  author = {Zhou, Shiguo and Goldstein, Steve and Place, Michael and Bechner, Michael and Patino, Diego and Potamousis, Konstantinos and Ravindran, Prabu and Pape, Louise and Rincon, Gonzalo and Hernandez-Ortiz, Juan and Medrano, Juan F. and Schwartz, David C.},
  da = {2015/08/28},
  date-added = {2022-10-12 18:07:15 +0000},
  date-modified = {2022-10-12 18:07:15 +0000},
  doi = {10.1186/s12864-015-1823-7},
  id = {Zhou2015},
  isbn = {1471-2164},
  journal = {BMC Genomics},
  number = {1},
  pages = {644},
  title = {A clone-free, single molecule map of the domestic cow (Bos taurus) genome},
  ty = {JOUR},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-00937-3_83},
  volume = {16},
  year = {2015},
  bdsk-url-1 = {https://doi.org/10.1186/s12864-015-1823-7},
  bdsk-url-2 = {http://dx.doi.org/10.1186/s12864-015-1823-7},
}