Diego Patiño

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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

  1. 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
  2. Level Set Mesher: Single-image to 3D reconstruction by following the level sets of the signed distance function
    Diego PatiñoCarlos Esteves, and Kostas Daniilidis
    In 26th International Conference on Pattern Recognition - ICPR 2022
  3. Self-supervised implicit shape reconstruction and pose estimation for predicting the future
    Diego PatiñoKarl Schmeckpeper, Hita Gupta, and 2 more authors
    In ICRA 2022 - Workshop on Motion Planning with Implicit Neural Representations of Geometry 2022
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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